International Journal of Power Electronics and Drive Systems (IJPEDS)
The International Journal of Power Electronics and Drive Systems (IJPEDS), p-ISSN: 2088-8694, e-ISSN: 2722-256X, is the official publication of the Institute of Advanced Engineering and Science (IAES). The scope of the journal includes all issues in the field of power electronics, electric drives, and energy systems. Included are techniques for advanced power semiconductor devices; control in power electronics; low and high power converters (inverters, converters, controlled and uncontrolled rectifiers); control algorithms and techniques applied to power electronics; electromagnetic and thermal performance of electronic power converters and inverters; power quality and utility applications; renewable energy; electric machines; modelling, simulation, analysis, design and implementations of the application of power circuit components (power semiconductors, inductors, high frequency transformers, capacitors), EMI/EMC considerations; power devices and components; integrated and packaged; induction motor drives; synchronous motor drives; synchronous motor drives; permanent magnet motor drives; ASDs (adjustable speed drives); multi-phase machines and converters; applications in motor drives; electric vehicles; wind energy systems; solar; battery chargers; uninterruptible power supplies (UPS); and other applications. The IJPEDS uses a rolling submission process, allowing authors to submit at any time during the year without time restraints. This journal is a SCOPUS and ScimagoJR indexed journal, CiteScore: 3.2, SJR: 0.270, and SNIP: 0.639. POWER UP YOUR RESEARCH: Amplify Your Impact in Power Electronics and Drive Systems It conveys the journal's dedication to empowering researchers to enhance their research, increase their influence, and contribute to the advancement of power electronics and drive systems. IJPEDS Journal positions as a platform that supports and amplifies the impact of research in this field. Get to Know Us Better BECOMING AN AUTHOR SUBMISSION TEMPLATE SUBMISSION GUIDELINES IJPEDS POLICIES MEET OUR EDITOR CONTACT US
Institute of Advanced Engineering and Science
Optimization of two-stage DTMOS operational transconductance amplifier with Firefly algorithm
This paper presents a methodology for optimizing dynamic threshold MOSFET (DTMOS) two-stage operational transconductance amplifiers (OTAs) tailored for biomedical applications through the utilization of the Firefly algorithm. The optimization process focuses on enhancing key performance metrics such as gain, bandwidth, and power efficiency, which are critical for biomedical signal processing, neural interfaces, and wearable healthcare devices. The methodology encompasses circuit architecture definition, Firefly algorithm implementation, fitness evaluation, and result analysis. The optimization results reveal a significant enhancement in performance metrics. Specifically, the number of transistors in the design is 25. The initial overall gain was 76.65 V/V, with a power efficiency (µ) of 1.6. After optimization, the overall gain was significantly improved to 84.029 dB using the Firefly algorithm, demonstrating superior performance compared to existing algorithms. The power efficiency (µ) was also enhanced to 1.702, underscoring the efficiency improvements achieved through optimization. Simulation results and statistical analysis confirm that the Firefly algorithm effectively achieves optimal configurations, improving the robustness of OTA designs against parameter variations. These enhancements validate the algorithm's efficacy in addressing power-performance trade-offs and its suitability for diverse biomedical applications. Physical prototyping of the optimized design further demonstrates real-world functionality, underscoring its practical applicability.
Copyright (c) 2025 Udari Gnaneshwara Chary, Swathi Mummadi, Kakarla Hari Kishore http://creativecommons.org/licenses/by-sa/4.0
Navigating the future of energy storage: insights into lithium-ion battery technologies
Lithium-ion batteries are now considered essential technology for a wide range of contemporary applications due to the growing need for effective and sustainable energy storage solutions. The various lithium-ion battery chemicals that are covered in detail in this paper are lithium iron phosphate (LFP), lithium nickel manganese cobalt (NMC), lithium nickel cobalt aluminum oxide (NCA), lithium-ion manganese oxide (LMO), lithium-ion cobalt oxide (LCO), and lithium titanate oxide (LTO). Based on critical performance metrics such as energy density, life cycle, charge/discharge rates, cost, and operational temperature range, each kind is assessed. Additionally, the paper discusses the future potential of lithium-ion technologies, with a focus on advancements in energy density, safety, sustainability, and recycling. By assessing the strengths and limitations of various lithium-ion chemicals, this paper seeks to provide valuable insights into the rapidly evolving field of battery technology, highlighting their indispensable role in the transition to sustainable energy systems. Lithium ion batteries have the potential to significantly enhance the efficiency and dependability of energy storage systems in a variety of applications with further research and development.
Copyright (c) 2025 Kalagotla Chenchireddy, Perattur Nagabushanam, Radhika Dora, Vadthya Jagan, Shabbier Ahmed Sydu, Varikuppala Manohar http://creativecommons.org/licenses/by-sa/4.0
Accurate state of health estimation using hybrid algorithm for electric vehicle battery pack performance and efficiency enhancement
Temperature fluctuations, overcharging, and over-discharging are all issues that can cause fast deterioration, capacity loss, and thermal runaway in lithium-ion batteries (LIBs). To overcome these challenges, a hybrid model combining a stacked recurrent neural network (SRNN) and bidirectional long short-term memory (biLSTM) is presented for a reliable state of health (SoH) estimate. This model finds subtle patterns in battery data using SRNN layers to capture sequential dependencies and biLSTM modules to solve long-term temporal correlations while avoiding vanishing gradient concerns. The effectiveness of model is assessed by performance measures such as root mean square error (RMSE), mean absolute error (MAE), and maximum error (MAX), which demonstrate its superiority for precise SoH estimation. The stacked RNN-based SoH estimation achieves superior accuracy, with RMSE, MAE, and MAX error levels of 1.5%, 0.8%, and 4.84%, respectively, compared to GRU’s higher errors (3.8%, 3%, and 5.5%). Stacked RNN hierarchically processes sequential battery data, effectively capturing complex temporal relationships, and ensuring accurate and reliable SoH estimation for LIBs.
Copyright (c) 2025 Rajesh Kumar Prakhya, Puvvula Venkata Rama Krishna http://creativecommons.org/licenses/by-sa/4.0
Dual-aware EV charging scheduling with traffic integration
Electric vehicle adoption is a trend in many countries, and the demand for charging station infrastructure is at a rapid pace. The placement of charging stations is the key research topic of many researchers, but charging scheduling is also a problem that is going to rise in the near future. The proper charger utilization, maintaining coordination between charging stations, and satisfying users' demands are some of the key challenges. The traffic pattern is uncertain, coordination of distances between charging stations and users is done by Euclidean distance. The traffic-aware fair charging scheduling (TAFCS) strategy is proposed, which will have a balance on charger utilization and user prioritization, and keep the fairness by equal distribution of electric vehicles among all the charging stations having a centralized charging system monitored by an aggregator. The distribution of the traffic pattern of electric vehicles is performed by Monte Carlo simulation. The proposed system is tested on the IEEE 33 bus standard system using the predefined voltage limits of each bus and limiting power loss to lessen its burden. The discharging process of 50 electric vehicles (V2G) is performed by optimal placement by obtaining the weakest buses, which makes it an intelligent distribution system. This proposed charging framework is validated on MATLAB R2020a.
Copyright (c) 2025 Maneesh Yadav, Satyaranjan Jena, Chinmoy Kumar Panigrahi, Ranjan Keshari Pati, Jayanta Kumar Sahu http://creativecommons.org/licenses/by-sa/4.0
Solar cell-based garden light automation for environmentally friendly technology learning
This research aims to: 1) Produce a prototype design for a solar cell-based automatic garden lighting system; 2) Determine the relationship between current, power, and voltage and light intensity; 3) Describe the feasibility of an environmentally friendly technology practicum guidebook; and 4) Describe teacher and student responses to the environmentally friendly technology practice guidebook. This research is R&D type Analysis, Design, Development, Implementation and Evaluation (ADDIE) Analysis, design, development, implementation and evaluation. The research sample used 44 class IX students at MTS Rahmatullah. According to students, aspects of teaching materials, aspects of content, and difficulty of teaching materials at school are inadequate at 84.25%, 80% and 82.5%. Student interest in environmentally friendly technology practicum guidebooks was 84.25%. The higher the light intensity, the higher the current, power, and voltage. Expert validation shows; the prototype of an automatic garden lighting system based on solar cells and a practical guidebook on environmentally friendly technology are very suitable for use (89.14% and 90.75%). The use of environmentally friendly technology practicum guidebooks increased students' critical thinking skills in the high category (N-Gain = 0.7937) and received responses from teachers and students in the "almost all" category (91.50% and 89.9%).
Copyright (c) 2025 Afrizal Mayub, Fahmizal, Lazfihma http://creativecommons.org/licenses/by-sa/4.0
Optimizing slow-charging EV loads with a two-layer strategy to enhance split-phase voltage quality and mitigate issues in PDNs
Power distribution networks (PDN) were mostly affected by the voltage imbalances created by the slow charging of electric vehicles (EV), were there random load into the PDN system, causing split-phase voltage quality (SPVQ) issues. Hence, to mitigate the problems associated with EVs’ slow charge in distributed phases of the power system, a multi-layer charging strategy is proposed considering the following constraints in the system: voltage deviation (VD) and voltage harmonics (VH) in split phase (SP). Further multi-layer control is associated with an inner layer equipped with hybrid non-dominated sorting genetic algorithm (NSGA-II) to select the optimal phase for charging the EV and send it to the output layer where a SP current algorithm is utilized so that voltage quality can be fed in loop to inner layer so that iterations were performed to satisfy the convergence condition. Simulation results in MATLAB demonstrate a voltage unbalance (VU) reduction of up to 32.81%, a maximum VD reduction of 9.11%, and a VH reduction of 6.25% at key grid nodes. The proposed method significantly enhances PDN efficiency and maintains voltage quality within national standards across 1,000 to 5,000 EV connections. The generated results reflected the optimal improvement in SPVQ, and the harmonics content reduced further; PDN operational efficiency also improved to a greater extent.
Copyright (c) 2025 Attada Durga Prasad, Manickam Siva, Alla Srinivasa Reddy http://creativecommons.org/licenses/by-sa/4.0
Composite least mean fourth algorithm (CLMF) based dynamic voltage restorer for enhancement of power quality
This paper introduces the composite least mean fourth control algorithm (CLMF) with a dynamic voltage restorer (DVR) to address power quality problems linked to voltage at the source side and supply clean voltage to the distribution network's sensitive loads. The performance of the two least mean fourth adaptive filters combined convexly by this control technique is better than that of the filters working independently. When comparing the suggested control to conventional synchronous reference frame-based vector control, phase-locked loops, abc to dq transformations, and dq to abc transformations are all practically eliminated. When compared to standard least mean square (LMS) and least mean fourth (LMF) control approaches, the proposed CLMF's features—simple computation, ease of implementation, reduced settling time, and increased reliability—show that the suggested controller is more efficient. The proposed CLMF controller excels in terms of rise time, 0.082 sec., and less settling time, 0.092 sec., respectively, with a peak overshoot of 2.33% compared with the aforementioned control algorithms. Different voltage-related PQ issues have been corrected successfully by the proposed CLMF. Through simulation using MATLAB/Simulink, system performance has been verified.
Copyright (c) 2025 Tummala Kranti Kiran, Balakrishnan Rajagopal, Yerramilli Butchi Raju http://creativecommons.org/licenses/by-sa/4.0
The road feeling control system on the steer by wire system uses fuzzy logic control based on swarm optimization
This paper presents an optimal control system for enhancing road feel in a steer-by-wire (SbW) system using fuzzy logic control (FLC) optimized with modified quantum particle swarm optimization (MQPSO). The objective is to improve the driver experience by providing realistic torque feedback, thereby replicating the steering sensations typically generated by road conditions. This feedback is essential for conveying information about vehicle dynamics and road surface variations through opposing torque applied to the steering interface. An artificial intelligence-based control system utilizing FLC was developed to manage the road feel feedback within the SbW system. The inputs to the FLC include steering angle, vehicle speed, and steering ratio, as well as key physical factors such as inertia and friction, all of which influence the generation of steering torque. The FLC parameters were optimized using MQPSO to achieve a more accurate and responsive road feel torque output. A Simulink model was constructed to simulate the proposed system. The simulation results demonstrate that the optimized FLC significantly improves the performance of the steering motor torque feedback mechanism. This study contributes to the advancement of steer-by-wire technology by proposing an optimal torque control framework and highlighting the effectiveness of integrating FLC with MQPSO in enhancing road feel dynamics.
Copyright (c) 2025 Fachrudin Hunaini, Gigih Priyandoko, Gatot Subiyakto, Purbo Suwandono http://creativecommons.org/licenses/by-sa/4.0
DC bus control strategy and implications for voltage source converter system
Significantly, the use of power electronic devices in residential and industrial settings has grown significantly in the last several years. Recent advancements in power semiconductors and microelectronics may be the main reason of their growing use in power systems for filtering, conditioning, and compensating. Additionally, the proliferation of semiconductor switches appropriate for high-power applications, and the enhancement of microelectronics enable mixed signal processing and control mechanisms. Furthermore, the concentration on renewable energy sources within the electric utility industry has emphasized the incorporation of power electronic converters into power systems. The operation and control of the regulated DC-voltage power port are examined in this work, a key part in different applications, such as STATCOM, dual mode HVDC converter systems, and aerodynamic wind energy converters with adaptive-speed optimization, emphasizing its significance in upholding a stable voltage level throughout the DC bus. The research also highlights the importance of power electronic converters within contemporary power systems, emphasizing their crucial role in facilitating effective and reliable power distribution. The obtained simulation results confirmed the efficacy of feed forward compensation in stabilizing the voltage responses of the DC bus.
Copyright (c) 2025 Haider Fadel Abbas, Ahmed Abdulredha Ali, Mustafa Jameel Hameed http://creativecommons.org/licenses/by-sa/4.0
Permanent magnet generator performance comparison under different topologies and capacities
This paper compares the magnetic, electrical, and mechanical characteristics of two permanent magnet generator topologies: single-gap axial flux and single-gap inner rotor radial flux. The study aims to identify how the key parameters fluctuate at each power capacity and investigate the trends in their values as power changes. The power capacities observed are 300 W, 600 W, 900 W, 1200 W, and 1500 W. Simulations used with the help of Ansys Maxwell software to obtain: i) magnetic characteristics without load, including air gap flux density, flux linkage, and induced voltage, ii) electrical performance, consisting of armature current, terminal voltage, voltage regulation, total harmonic distortion, core loss and output power, and iii) mechanical performance, including shaft torque and cogging torque. The last step compares the power density of both topologies. The simulation results show that the axial flux permanent magnet generator (AFPMG) has better air gap flux density, voltage regulation, total harmonic distortion (THD), efficiency, electromagnetic torque, and power density characteristics. Meanwhile, the radial flux permanent magnet generator (RFPMG) is superior in induced voltage and output power. These results conclude that, in general, AFPMG is exceptional from a technical point of view and is more economical when applied to hydro or wind energy systems.
Copyright (c) 2025 Ketut Wirtayasa, Muhammad Kasim, Puji Widiyanto, Anwar Muqorobin, Sulistyo Wijanarko, Pudji Irasari http://creativecommons.org/licenses/by-sa/4.0
Enhance the performance of 3-phase induction motors with the utilization of a 9-phase winding design
Because of its sturdy design, affordability, and convenience of use, the three phase induction motor is a common kind of electric motor, especially in the industrial sector. This motor design is still being improved to make it work better. For example, permanent magnets are being added to the rotor, control systems are getting better, the number of phases is being increased, and motor winding designs are developing. Nevertheless, the creation of the motor winding design is the least expensive aspect of all these endeavors. By creating a nine-phase winding configuration with a three-layer design in the motor, this study aims to enhance the performance of a three-phase induction motor. This study examined output power, speed, mechanical torque, and motor efficiency in a 3-phase system operation. The study's results indicated that the new design motor worked better, with higher output power (4.27%), rotor speed (0.61%), and mechanical torque (3.47%), despite a minor reduction in efficiency relative to conventional 3-phase induction motors (-0.24%).
Copyright (c) 2025 Zulkarnaini, Sepannur Bandri, Erhaneli, Zuriman Anthony, Yusreni Warmi, Arfita Yuana Dewi Rachman http://creativecommons.org/licenses/by-sa/4.0
Torque sharing function optimization for switched reluctance motor control using ant colony optimization algorithm
Switched reluctance motors (SRMs) are gaining popularity in industrial and automotive applications due to their robust design, fault tolerance, and high torque density, particularly in wide-speed-range operations. However, SRM performance is often limited by torque ripple, speed oscillations, and inefficiencies, which can lead to mechanical stress, vibration, and acoustic noise. Addressing these challenges requires the effective optimization of control strategies. This study aims to enhance the performance of SRM drives by employing an ant colony optimization (ACO) algorithm to optimize the torque sharing function (TSF). The proposed method iteratively tunes TSF parameters to minimize torque ripple and improve speed stability under varying operating conditions. Simulation results demonstrate significant improvements: torque ripple is reduced from a range of –20 Nm to 10 Nm without optimization to below 10 Nm with ACO-based optimization. Similarly, current peaks decrease from 60 A to 5.5 A, ensuring smoother motor operation and enhanced efficiency. Comparative analysis confirms that the ACO-based TSF provides superior tracking of speed set points, reduced mechanical stress, and improved reliability, making it well-suited for high performance applications in both industrial and automotive sectors.
Copyright (c) 2025 Dhiyaa Mohammed Ismael, Thamir Hassan Atyia http://creativecommons.org/licenses/by-sa/4.0
Improvement direct torque control of induction motor using robust intelligence artificial ANFIS speed controller
This paper proposes a study aimed at improving the conventional direct torque control (DTC) technique applied to induction motors (IM). The primary aim is to reduce the harmonic distortions and fluctuations associated with the electrical current, flux variations, and generated torque, while ensuring accurate speed reference tracking and ensuring optimal dynamic performance of the drive, especially under variable speed conditions. To achieve this, we introduce an intelligent control system that utilizes a hybrid neuro-fuzzy inference model (ANFIS), through the application of the back propagation method. The DTC-ANFIS technique is compared with the traditional DTC-PI method and simulated using MATLAB/Simulink in different scenarios. The obtained results reveal a significant improvement in performance over DTC-PI, with superior results over a wide speed range.
Copyright (c) 2025 Laoufi Abdelhaq, Chergui Moulay-Idriss, Soufiane Chekroun http://creativecommons.org/licenses/by-sa/4.0
Improved hybrid DTC technology for eCAR 4-wheels drive
This article deals with the design of a hybrid controller (HyC). It combines fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS). It is combined with direct torque control (DTC). This HyC-DTC combination is designed to improve the technical performance of a 04-wheel drive electric vehicle (EV). A stress test is identically applied to the DTC combined with the FL (FDTC) and to the HyC-DTC in order to certify the suitability of this new control following a cross-validation. This is based on dynamic stability criteria (overshoot, rise time, accuracy), analysis of torque and flux oscillations, and the EV's robustness symbol. The EV's magnetic quantities are managed by a master-slave module (VMSC). Simulations are carried out using MATLAB/Simulink software. The HyC-DTC achieves near-zero accuracy like the FDTC, with overshoot around 0.2% less than the FDTC, and torque oscillation amplitude around 4 times less than the FDTC. However, its rise time is 0.045% greater than that of the FDTC. It is therefore slower, but more precise and suitable for EV transmission systems in terms of safety and comfort.
Copyright (c) 2025 Njock Batake Emmanuel Eric, Nyobe Yome Jean Maurice, Ngoma Jean Pierre, Ndoumbé Matéké Max http://creativecommons.org/licenses/by-sa/4.0
Direct torque control of induction motor using a novel sliding mode control
Direct torque control (DTC) for induction motor (IM) drive systems is recognized as a powerful control method known for its fast response and simple structure. However, this control method often suffers from several limitations, such as significant torque and current ripple, and sensitivity to variations in motor parameters. To address these issues, this paper proposes a novel sliding mode control strategy for the outer speed loop to improve the quality of DTC-based IM drive systems. Unlike previous approaches, we propose a novel adaptive parameter higher-order sliding mode (HOSM) controller for IM speed control. This approach enhances the drive system's performance by reducing torque ripple (a common issue in DTC), improving dynamic response, eliminating overshoot during transients, and increasing overall system stability. To ensure system stability, Lyapunov stability theory is used to design the control signals. The efficiency of the control law proposed in this paper is evaluated based on simulations performed on MATLAB-Simulink. The results obtained demonstrate that: First, the proposed control model for fast torque and speed responses, ensuring the drive system converges to the desired operating point during transients without encountering the phenomenon of exceeding the threshold. Second, the system maintains stable operation, even in the presence of load disturbances. Third, this method significantly reduces torque ripple, a common problem in IM drive systems using DTC techniques.
Copyright (c) 2025 Ngoc Thuy Pham, Duc Thuan Le, Phu Diep Nguyen http://creativecommons.org/licenses/by-sa/4.0
A new approach for optimal sizing and allocation of distributed generation in power grids
This paper presents a methodology for optimizing the allocation and sizing of distributed generators (DG) in electrical systems, aiming to minimize active power losses on transmission lines and maintain bus voltages within permissible limits. The approach consists of two stages. First, a sensitivity based analysis is used to identify the optimal candidate bus or buses for DG placement. In the second stage, a new random number generation method is applied to determine the optimal DG sizing. Moreover, a ranking for the optimal locations and sizes is given in case the optimal location is unavailable in real-world scenarios. The proposed methodology is demonstrated through a straightforward algorithm and tested on the IEEE 14-bus and IEEE 30-bus networks. Numerical simulations in MATLAB illustrate the effectiveness of the proposed approach in finding the optimal allocation of DG and the amount of active power to be allocated at the candidate buses, considering the inequality constraints regarding voltage limits and DG allowable power. The paper concludes with results, discussions, and recommendations derived from the proposed approach.
Copyright (c) 2025 Hudefah Alkashashneh, Ayman Agha, Mohammed Baniyounis, Wasseem Al-Rousan http://creativecommons.org/licenses/by-sa/4.0
Low voltage fault ride-through operation of a photo-voltaic system connected utility grid by using dynamic voltage support scheme
This research suggests a control technique that makes use of a microgrid's energy storage and to enable low voltage ride through (LVRT) process with a flexible dynamic voltage support (DVS) system. First, the requirements for the microgrid's maximum DVS are stated, together with an explanation of how these requirements depend on the characteristics of the analogous network that the microgrid sees. In order to create a flexible DVS regardless of the changing system circumstances, reference signals for currents that are derived from maximum voltage tracking technique are suggested in this research. These signals take into account the challenges involved with real time parameter assessment in the context of transient voltage disruptions. Second, a control scheme is suggested to allow a microgrid's energy storage-based LVRT operation. Thirdly, a novel approach to energy storage sizing for LVRT operation is offered, taking into account the corresponding network characteristics, grid code requirements, and the rated current value of the power electronic converter. Real-time MATLAB simulations for low-voltage symmetrical faults are used to validate the suggested control technique.
Copyright (c) 2025 Satyanarayana Burada, Kottala Padma http://creativecommons.org/licenses/by-sa/4.0
Smart energy management in renewable microgrids: integrating IoT with TSK-fuzzy logic controllers
Hybrid microgrids powered by renewable energy sources are gaining popularity globally. Photovoltaic (PV) and permanent magnet synchronous generator (PMSG)-based wind energy systems are widely used due to their ease of installation. However, wind and solar energy are unpredictable, leading to fluctuating power generation. Simultaneously, load demand varies randomly, making it necessary to integrate storage devices to maintain a balance between generation and consumption. To enhance system economy, a small battery is combined with a hydrogen-based fuel cell and electrolyzer for efficient energy storage and management. A robust energy management system (EMS) is critical to ensure power quality and reliability across all microgrid components. Maximum power point trackers (MPPTs) are employed to maximize renewable energy utilization. Frequency stability and ensuring power balance is important in autonomous microgrids, especially during rapid load or source variations. This paper presents a novel fuzzy rule-driven Takagi-Sugeno-Kang (TSK) controller for the EMS, ensuring fast, precise responses and improved microgrid reliability.
Copyright (c) 2025 Moazzam Haidari, Vivek Kumar http://creativecommons.org/licenses/by-sa/4.0
Predictive machine learning for smart grid demand response and efficiency optimization
This paper explores the evolution of smart grids (SGs) and how they enable consumers to schedule household appliances based on demand response programs (DRs) provided by distribution system operators (DSOs). This study looks at and compares four distinct regression models: linear regression, random forest regressor, gradient boosting regressor, and support vector regressor. This is being done because more and more people are using machine learning (ML) methods to make this process better. The models are trained and tested using a dataset that includes a variety of parameters, such as humidity, temperature, and the amount of power used by appliances. Mean squared error (MSE) and R-squared values are two important performance measures that are used to judge these models and see how well they can make predictions. These results reveal that the gradient boosting regressor was the most accurate model for figuring out how much energy smart homes use. This algorithm could be a great tool for better managing energy use because it can figure out the complicated connections between the things that are input and the amount of energy that appliances use. This study makes a big difference in the creation of strong regression models by emphasizing how important it is to be accurate when making predictions. This, in turn, helps to enhance energy sustainability and economic stability in smart home environments.
Copyright (c) 2025 J. C. Vinitha, J. Sumithra, M. J. Suganya, P. Aileen Sonia Dhas, Ramalingam Balaji, Sivakumar Pushparaj http://creativecommons.org/licenses/by-sa/4.0
Islanding detection of integrated DG system using rate of change of frequency over reactive power
This paper offers a passive islanding detection method that is effective for distributed generation. When a distributed generator (DG) keeps a location powered even when access to the external electrical grid is lost, this circumstance is referred to as islanding. The power distribution system currently includes distributed generators (DGs), which provide inexpensive electricity and have fewer environmental impacts. Sometimes, these DGs continue to supply the nearby loads because of line outages and islands made by system separations. As a result, there are scenarios with unacceptable power quality. The islanding is identified if the result of the rate of change of frequency over reactive power exceeds the threshold value. The MATLAB test results from this study demonstrate the effectiveness of the suggested approach for different islanding and non-islanding scenarios.
Copyright (c) 2025 B. V. Seshu Kumari, Ambati Giri Prasad, S. Sai Srilakshmi, Karri Sairamakrishna Buchireddy, Ch. Rami Reddy http://creativecommons.org/licenses/by-sa/4.0
Modeling, tuning, and validating of exciter and governor in combined-cycle power plants: a practical case study
Exciter and governor systems are critical to regulating power output and maintaining stability in power systems. Despite their significance, there is a lack of practical methodologies that leverage real power plant data for modeling, tuning, and validation. This research paper seeks to fill this gap by presenting a methodology that utilizes a transfer function and control algorithms for tuning and validation. The proposed approach is demonstrated through a case study of a practical combined-cycle power plant in Malaysia. The control algorithm's effectiveness is verified through MATLAB and Simulink simulations. Post-tuning assessments confirm the method’s ability to accurately determine tunable control parameter settings, meeting system requirements while ensuring grid stability and reliability. This versatile approach can be applied to various power plant configurations, making it a valuable tool for optimizing operations.
Copyright (c) 2025 Saleh Ba-swaimi, Renuga Verayiah, Tan Yi Xu, Nagaraja Rupan Panneerchelvan, Aidil Azwin Zainul Abidin, Marayati Marsadek, Agileswari K. Ramasamy, Izham Zainal Abidin, W. Mohd Suhaimi Wan Jaafar http://creativecommons.org/licenses/by-sa/4.0
Synchronous generator system identification via dynamic simulation using PSS/E: Malaysian case
The synchronous generator (SG) plays a crucial role in power systems by serving as a stable and reliable source of electrical energy. The performance of an SG hinges on its standard parameters, which can be derived through dynamic tests. This study introduces a method for determining the standard parameters of an SG from dynamic tests conducted via power system simulation for engineering (PSS/E). The proposed method entails conducting several key tests on the generator, including a direct-load rejection test, excitation removal test, quadrature-axis load rejection test, arbitrary axis load rejection test, and open-circuit saturation test. The results obtained from these tests are then utilized to calculate the standard parameters of the SG accurately. To validate the effectiveness of the method, simulation data from the SG, as well as the designed initial data, are utilized. Statistical analysis reveals that the maximum relative error is equal to or less than 2.7% of the design values for all standard parameters, emphasizing the robustness and accuracy of the proposed method. The methodology presented in this study can complement field or site measurements, as it enables the verification of system parameters through dynamic simulations.
Cancellation of periodic disturbances for dual start induction drives based on a novel robust adaptive control strategy
The disturbance cancellation has always been an important area that has received much attention, especially for the nonlinear drive systems as the dual start induction motor (DSIM). In this paper, a new robust adaptive hybrid strategy based on an improved variable-gain quasi-continuous third order sliding mode (VGQSTOSM) algorithm integrated with RC and a load torque disturbance estimator helps to reduce chattering, cancel the periodic and extended load disturbances, and enhance tracking performance effectively. By using third-order sliding mode with variable gain dependent on the magnitude of the sliding variable, this proposal aims to be adaptive. It provides higher gain when far from the sliding surface (is large), leading to faster convergence and lower gain when close to the sliding surface (is small), potentially reducing chattering further and decreasing control effort near the equilibrium. The robustness of the proposed controller is improved because the adaptive gain mechanism effectively compensates for uncertainties or disturbances. Furthermore, a plug-in RC is integrated into the improved high-order sliding mode structure (DRVGQSTOSM), and an estimated load torque disturbance value is also used to help identify and proactively eliminate disturbances. The system stability is assured using Lyapunov theory the virtual control vectors' outputs are chosen based on Lyapunov theory. Simulation results obtained using the MATLAB software confirm the tracking and harmonic disturbance rejection performance as well as the robustness of the proposed control strategy.
Copyright (c) 2025 Ngoc Thuy Pham, Phu Diep Nguyen http://creativecommons.org/licenses/by-sa/4.0
A model predictive control strategy for enhance performance of totem-pole PFC rectifier
This paper proposed a simple but effective finite control set-based model predictive control (FCS-MPC) method to control a totem-pole bridgeless boost PFC rectifier (TBBR). The control algorithm selects from the possible switching states an appropriate one that fulfills a predefined cost function. This method also successfully eliminates the zero-crossing current distortion so that the grid current can synchronize well with the grid voltage. The theoretical analysis was presented and verified by simulation. Finally, a 3.3 kW/400 Vdc prototype was fabricated and investigated through various working conditions to realize the effectiveness of the proposed control strategy. Both simulation and experimental results show that the proposed control method can ensure accurate control of DC link output voltage and sinusoidal input current with unity power factor.
Copyright (c) 2025 Le Chau Duy, Nguyen Dinh Tuyen http://creativecommons.org/licenses/by-sa/4.0
Variable frequency drive based on full-bridge class D for single-phase induction motor
The issue with induction motors lies in speed regulation, which can be addressed by adjusting the motor voltage; however, this affects torque. In contrast, a variable frequency drive (VFD) changes the motor frequency while maintaining a constant voltage. A VFD controller with constant sinusoidal voltage and adjustable frequency can be implemented using an Arduino and a class D full-bridge MOSFET amplifier inverter. This paper discusses the electronic speed control (ESC) of induction motors using VFD regulation, demonstrating how changes in frequency affect motor speed. The system involves an induction motor controlled by a VFD comprising three main components: an AC-to-DC converter, a class-D full-bridge MOSFET inverter, and a variable-frequency sinusoidal signal source. VFDs operate with constant voltage and variable frequency. This method includes the design and testing of VFD hardware and software. The VFD components include: a class-D full-bridge switching inverter, a sinusoidal signal frequency generator (30–70 Hz), an Arduino with custom software, an SMPS power supply, and a step-up transformer. The results indicate that the class-D full-bridge inverter can effectively regulate motor speed through VFD control. The motor speed is almost directly proportional to the frequency: at 30 Hz, the speed is 860 RPM; at 50 Hz, 1472 RPM; and at 70 Hz, 2035 RPM.
Copyright (c) 2025 Budi Pramono Jati, Jenny Putri Hapsari, Muhamad Haddin, Sri Arttini Dwi Prasetyowati http://creativecommons.org/licenses/by-sa/4.0
Optimization of ANN-based DC voltage control using hybrid rain optimization algorithm for a transformerless high-gain boost converter
This paper introduces an adaptive voltage regulation technique for a transformerless high-gain boost converter (HGBC) integrated within standalone photovoltaic systems. A neural network controller is trained and fine-tuned using the rain optimization algorithm (ROA) to achieve improved dynamic behavior under variable solar conditions. The proposed ROA-ANN framework continuously updates the duty cycle to ensure output voltage stability in real time. Validation was carried out using MATLAB–OrCAD co-simulation under multiple scenarios. Comparative results highlight superior performance of the ROA-ANN controller in terms of convergence speed, overshoot minimization, and steady-state response, outperforming conventional PID and ANN-based methods.
Copyright (c) 2025 Mohcine Byar, Abdelouahed Abounada http://creativecommons.org/licenses/by-sa/4.0
Study of parallel operation single phase H-bridge CSI and H-bridge VSI
In some applications, parallel operation of some single-phase inverters with different characteristics is a necessity, such as in a photovoltaic power conversion system. Each power inverter with its power source works, delivering power to a common load which cannot be supplied by a single power inverter. This paper proposed a novel parallel operation of two different power inverter circuit types. H-bridge voltage source inverter (HB VSI) and H-bridge current source inverter (HB-CSI), supplying AC power to a common load. The proposed inverter system was examined and its operation characteristics were analyzed using computer simulation. Moreover, a laboratory prototype of the inverter system was made and examined to validate some principal characteristics of the inverter system experimentally. Test results showed that by combining the HB-VSI and HB CSI, a lower distortion of load current was achieved, specifically, total harmonic distortion (THD) of Iload was less than 1%. This phenomenon happens even the THD of AC currents generated by HB-VSI and HB-CSI at 6.95% and 6.18%, respectively.
Copyright (c) 2025 Suroso, Winasis, Retno Supriyanti http://creativecommons.org/licenses/by-sa/4.0
Design and analysis of seven-level hybrid modified H-bridge multilevel inverter
This paper introduces a novel boosting multilevel inverter that utilizes switched capacitors. Current multilevel inverters (MLIs) face several issues, such as complex structures, intricate switching controls, and challenges in generating gate pulses, numerous components, and high voltage stress on semiconductors. The increase in the number of levels adds to the complexity and cost of the circuit and can reduce reliability in some cases. The proposed topology creates a 7-level voltage waveform using 9 switches, 1 diode, and 2 capacitors, and it triples the voltage gain. The capacitors maintain self balanced operation without the need for additional circuits. A simple logic gate-based pulse-width modulation (PWM) technique is presented to ensure power balancing of the capacitors. The proposed 7-level switched capacitor boosting multilevel inverter features a reduced switch count, lower voltage stress, and built-in fault tolerance. The paper includes a comprehensive comparison of various related topologies. The proposed topology is simulated in PSIM, with simulation results presented for different parameters.
Copyright (c) 2025 Arpan Dwivedi, Raman Kumar, Sailesh Sourabh, Vikash Rajak, Vikash Kumar Singh, Maruti Nandan Mishra http://creativecommons.org/licenses/by-sa/4.0
Analysis of cascaded H-Bridge multilevel inverters using SPWM with multi-sinusoidal reference
Multilevel inverters have become the preferred choice for medium voltage and high-power applications due to their superior waveform quality, reduced stress on switching components, and overall enhanced performance. Among these, the cascaded H-bridge inverter stands out for its simpler control and modulation techniques, as well as its greater efficiency compared to other multilevel inverter topologies. This paper presents the design and performance evaluation of a cascaded H-bridge multilevel inverter (CHMI) for five, seven, nine, eleven, thirteen, and fifteen levels, utilizing sinusoidal pulse width modulation (SPWM) in MATLAB Simulink. The proposed technique, the multi-sinusoidal reference, is implemented by comparing multiple sinusoidal wave signals with a carrier triangular signal, with the resulting comparison pulses used to control the inverter's switching. The output results indicate that as the number of levels in multilevel inverters increases, the total harmonic distortion (THD) decreases, and the output voltage improves.
Copyright (c) 2025 Azrita Alias, Wahidah Abdul Halim, Maaspaliza Azri, Jurifa Mat Lazi, Muhammad Zaid Aihsan http://creativecommons.org/licenses/by-sa/4.0
Wireless charging Class-E inverter for zero-voltage switching over coupling coefficient range
A novel and practical methodology is presented in this study for designing contactless wireless energy systems using resonant-mode Class-E converters, aiming to sustain efficient soft-transition switching under various levels of magnetic coupling, even under coil misalignment. The approach integrates the wireless power transfer (WPT) circuit with the inverter’s series resonant network and analytically derives the relationship between the coupling coefficient and impedance phase angle to identify zero voltage switching (ZVS) conditions. A key contribution is the use of the maximum expected coupling coefficient as a critical design point to ensure ZVS across practical variations. A complete step-by-step design procedure is provided. Simulation and experimental results confirm that the inverter achieves and maintains ZVS for coupling values in the range 0 < k ≤ kdesigned, with efficiencies reaching up to 95%. This work supports the advancement of soft-switching inverter design to enable robust and efficient WPT systems under practical misalignment conditions.
Copyright (c) 2025 Anon Namin, Chuchat Donloei, Ekkachai Chaidee http://creativecommons.org/licenses/by-sa/4.0
Dead time control signal for non-isolated synchronous buck DC-DC converter
This study introduces a simple dead-time control signal for the non-isolated synchronous buck DC-DC converter, incorporated alongside maximum power point tracking (MPPT) for a stand-alone photovoltaic (PV) system. Dead-time control in non-isolated DC-DC converters is challenging due to difficulties in accurately sensing and predicting errors, especially during the transition between switching modes. The introduction of the dead-time control method resulted in optimal efficiency for the stand-alone PV system. The dead-time control was implemented in the hardware prototype using a bootstrap technique. Power generation from the PV module was optimized through the DC converter's implementation of an improved perturb and observe (P&O) MPPT approach. According to the results, the proposed design achieved an overall system efficiency of 80%. Moreover, the enhanced P&O MPPT algorithm prototype was observed to produce a maximum output power of 60 W.
Copyright (c) 2025 Muhammad Hafeez Mohamed Hariri, Noor Dzulaikha Daud, Tole Sutikno, Nor Azizah Mohd Yusoff, Mohd Khairunaz Mat Desa http://creativecommons.org/licenses/by-sa/4.0
A versatile three-level CLLC resonant converter for off-board EV chargers with wide voltage adaptability contribution
The vehicle-to-grid (V2G) concept has gained significant attention in the last decade due to its potential to enhance direct current (DC) microgrid stability and reliability. Electric vehicles (EVs) play a central role in distributed energy storage systems, optimizing efficiency and enabling the integration of renewable energy sources. This study offers a unique three level CLLC resonant converter developed for off-board EV chargers to promote bidirectional power transfer between DC microgrids and EVs. The suggested converter uses resonant CLLC components and two three-level full bridges to effectively handle a broad range of EV battery voltages (200 V–700 V). To ensure effective power conversion, the first harmonic approximation (FHA) model is used to analyse the converter's resonant frequency characteristics. The proposed system achieves high efficiency (>95%), with voltage stability maintained at 750 V under various load conditions. The converter's performance was validated through MATLAB based simulations, comparing proportional integral (PI) and proportional integral derivative (PID) control strategies. The PID-controlled system demonstrated superior dynamic response, reduced current ripples, and enhanced voltage regulation compared to the PI-controlled system. This study demonstrates the viability of implementing a three-level CLLC resonant converter for efficient, bidirectional, and wide-voltage adaptation in EV charging infrastructure, thereby contributing to grid stability and renewable energy integration.
Copyright (c) 2025 Chandra Babu Guttikonda, Pinni Srinivasa Varma, Malligunta Kiran Kumar, Kambhampati Venkata Govardhan Rao, Rakesh Teerdala, Santoshi Kanagala http://creativecommons.org/licenses/by-sa/4.0
Intelligent control solutions for enhancing dual-fold Luo converter efficiency in EVs
This research proposes the design and application of a smart controller for a dual-fold Luo converter tailored specifically for E-vehicle applications. The dual-fold Luo converter, known for its ability to efficiently step up and step down voltage levels with reduced components, is augmented with a smart control strategy to enhance its performance in the context of electric vehicles. The smart controller utilizes advanced techniques, such as artificial neural networks or fuzzy logic, to adaptively regulate the converter's operation, thereby improving efficiency, transient response, and overall reliability. By leveraging real-time data from the E-vehicle system, the controller dynamically adjusts key parameters to optimize performance under varying load and operating conditions. Key design considerations include the selection and training of the smart controller to achieve desired voltage regulation, efficiency, and robustness in the face of uncertainties inherent in E-vehicle operation. The proposed design methodology is validated through simulation studies, demonstrating superior performance compared to conventional control techniques. The results illustrate the efficacy of the smart controller in enhancing the dynamic response of the dual-fold Luo converter, making it a promising solution for E-vehicle power management systems. This research contributes to the advancement of power electronics in electric transportation, facilitating the development of more efficient and reliable E-vehicle systems in the pursuit of sustainable mobility.
Copyright (c) 2025 P. Siva Subramanian, B. Marisekar, P. Mohana Karthiga, R. Ramya http://creativecommons.org/licenses/by-sa/4.0
Advancing power quality via distributed power flow control solutions
The growing demand for enhanced power quality and reliable transmission has driven advancements in power flow control technologies. The distributed power flow controller (DPFC) represents an advancement over the unified power flow controller (UPFC). In contrast to the UPFC, the DPFC removes the DC link connecting the shunt and series converters, and redistributes the series converters along the transmission line as single-phase static series compensators. This modification enhances grid performance while maintaining full power flow control capabilities. The DPFC offers several advantages over the UPFC, including higher reliability, improved controllability, and greater cost-effectiveness. The system comprises a shunt converter in conjunction with multiple series converters, each with its own control circuit, all managed by a central control unit. This article presents the implementation of a DPFC model in MATLAB/Simulink. The simulation outcomes indicate that the DPFC significantly contributes to improved voltage stability and enhanced power transfer capability, thereby reinforcing system performance and reliability.
Copyright (c) 2025 Abdelkader Yousfi, Fayçal Mehedi, Khelifa Khelifi Otmane, Youcef Bot http://creativecommons.org/licenses/by-sa/4.0
An approach of battery adaptation in wireless sensor network with resource aware in extreme environmental area
A wireless sensor network (WSN) is a distributed wireless system that employs sensor nodes to perform various tasks, including sensing, monitoring, data transmission, and delivering information to users via internet communication. Resource availability in WSNs is a critical factor influencing data delivery performance. One of the main challenges is the rapid depletion of resources, particularly batteries, which play a pivotal role in the system’s operational sustainability. This study evaluates the impact of battery adaptation through four testing scenarios. The results show that implementing battery adaptation significantly extends system lifespan compared to scenarios without adaptation. In the scenario without both a classification algorithm and adaptation, the system lasts approximately 270 minutes. When battery adaptation is applied without a classification algorithm, the lifespan increases to 330 minutes and 30 seconds. In contrast, the scenario using a classification algorithm without adaptation yields a lifespan of about 185 minutes, while combining the classification algorithm with adaptation extends it to approximately 252 minutes. The findings demonstrate that battery adaptation enhances the longevity and resource efficiency of WSN systems. However, the use of a classification algorithm tends to reduce operational time compared to scenarios that do not employ such algorithms.
Copyright (c) 2025 Jumadi Mabe Parenreng, Muhammad Reza Shiraj, Satria Gunawan Zain, Abdul Wahid http://creativecommons.org/licenses/by-sa/4.0
Modeling and simulation of klystron-modulator for linear accelerators in PRTA
Approximately 70% of commercial industries worldwide use electron accelerator technology for various irradiation processes. The advantages of irradiation processes compared to thermal and chemical processes are higher output levels, reduced energy consumption, less environmental pollution, and producing superior product quality and having unique characteristics that cannot be imitated by other methods. Research Center for Accelerator Technology (PRTA), BRIN, Indonesia is developing standing wave LINAC (SWL) for food irradiation applications at S-band frequencies (±2856 MHz), electron energy of 6-18 MeV, and an average beam power of 20 kW. This paper aims to model, simulate, and analyze the klystron modulator in the RF linear accelerator (LINAC). The klystron modulator is the main component of the RF LINAC, which functions to supply klystron power with the order of megawatt peak DC, so that the klystron can amplify the low-level RF signal from the RF driver into a high-power RF signal with a power of 2-6 MW peak. The klystron modulator modeling is carried out based on mathematical modeling, then simulated using LTspice to analyze the system performance of the klystron modulator. The results of the klystron modulator modeling simulation show stable system performance and dynamic response. So that it meets the specifications of the 6-18 MeV SWL LINAC being developed by PRTA-BRIN.
Copyright (c) 2025 Wijono, Dwi Handoko Arthanto, Galih Setiaji, Angga Dwi Saputra, Taufik, Andang Widi Harto http://creativecommons.org/licenses/by-sa/4.0
Resonant converter for fast-charging applications
Resonant converters (RCs) are gaining attention from the research community due to their significant contributions to the architecture of electric vehicle (EV) charging infrastructure. The primary part of RC is responsible for enabling constant-current (CC) charging, which helps lower inrush current, decrease losses, and improve efficiency. While the load current stays constant during charging using the CC approach, the source current grows linearly with charging time. However, pulling a high source current increases the rating of the inverter switches, which stresses them, raises their temperature, increases heat sink demand, and causes conduction loss—all of which are undesirable. Consequently, the rated CC is provided by the P2 topology of RC, which has a lower peak current source than other topologies and will improve charger performance. However, this assertion must be verified by mathematical modeling, design with theoretical calculations, specifications, and MATLAB simulation before execution. By providing a constant load current of 5 A at a DC source voltage of 200 V, the P2 RC and the conventional LCL RC are designed to compare source current values.
Copyright (c) 2025 Remala Geshma Kumari, Narahari Krishna Kumari, Kankipati Shravya http://creativecommons.org/licenses/by-sa/4.0
Optimal placement of recloser for the improvement of reliability indices in radial distribution system using hybrid PSO-firefly algorithm
Electricity outages are frequently caused due to problems in the electric distribution system (EDS). The method presented in this research describes a comprehensive dual-phased designed to enhance the electric network efficiency and reliability. A hybrid particle-firefly optimization method is applied in the first phase to allocate reclosers and sectionalizer in an optimal accessible path. Furthermore, in the second phase, the Medium distribution voltage Systems that comprises five main circuit breaker and one power source are taken into consideration, as well as automatic load shift to an alternative power supply and the secondary circuit breaker shut down under normal conditions. The authors provide a streamlined technique based on swapping out loads discrete to determine the reduction value of the anticipated energy not-supplied (ENS) and cost of energy not-supplied (CENS) to customers after installing sectionalizer and recloser in APO radial substation network. The optimized CENS with protective device of the distribution system is tremendously reduce compared to the CENS of the conventional state which has no protective scheme.
Copyright (c) 2025 Jacob Kehinde Ogunjuyigbe, Evans Chinemezu Ashigwuike, Timothy Oluwaseun Araoye, Oluyinka Olugbenga Aina, Onyekachukwu Denis Ozulu, Sardauna Ibrahim, Isaac Ojochogwu Onuh, Ikenna Chuddy Mbamalu http://creativecommons.org/licenses/by-sa/4.0
An analytical technique for failure analysis and reliability assessment of grid daily outage performance in distributed power system
This paper modeled and analyzed the reliability performance of the 132/33 kV substation in Abuja, Nigeria through the historical data collected from the APO substation using MATLAB 2021b. The probability distribution model was applied to determine the daily feeder’s outage using Reliability, availability, mean time to repair (MTR), Failure rate, distribution indices, and mean time between failures (MTBF). Due to the application of smart energy meters, the use of prepaid energy meters has helped to regulate energy demand, reduce network overloading especially during peak hours, and minimize the cost of energy consumed. There are more forced failures in the distribution system due to the switchgear and Transformer failures. There are more forced failures in the distribution system since 2013, which caused a reduction in the number of interruptions even with an increase in several customers linked to the transmission network. The result shows that the system was most available in the year 2015 with an average service availability index (ASAI) value of 98.9971%. The system was least available in year 2011 with an ASAI value of 98.6558%. The paper recommended that there should be interconnections between different feeders through proper configuration of switches or reclosers, to reduce failure occurrence in the network.
Copyright (c) 2025 Jacob Kehinde Ogunjuyigbe, Evans Chinemezu Ashigwuike, Kafayat Adeyemi, Ngang Bassey Ngang, Timothy Oluwaseun Araoye, Isaac Ojochogwu Onuh, Benson Stephen Adole, Solomon Bala Okoh, Iboi Endurance http://creativecommons.org/licenses/by-sa/4.0
Bibliometric visualization of metal-air battery research trends
Metal-air batteries are rechargeable secondary batteries with high energy density, typically using carbon electrodes. However, carbon waste poses environmental risks. Fly ash, a byproduct of coal combustion, offers a sustainable alternative due to its high electrical conductivity. This study analyzes research trends on metal-air batteries and fly ash from 2019 to 2023 using bibliometric visualization of Scopus-indexed publications. The keyword search was refined from 'Battery' to 'Air Battery' and, finally, 'Air Battery' with 'Fly Ash,' yielding 60 relevant articles. Using the VOSviewer, research patterns, key focus areas, and collaboration networks were identified. The results indicate a 14.87% increase in publications from 2019 to 2023, with significant growth from 2019 to 2021 before declining after 2022. This fluctuation suggests shift in research interests toward other battery technologies. Fly ash demonstrates potential as a carbon substitute for air batteries, promoting sustainability. However, further research is needed to optimize its application and address technical challenges. Bibliometric visualization highlights a growing interest in fly ash for environmentally friendly battery development due to its abundance and sustainability.
Copyright (c) 2025 Satria Pinandita, Rustam Asnawi, Mochamad Syamsiro http://creativecommons.org/licenses/by-sa/4.0
Improving electrical energy efficiency through hydroelectric power and turbine optimization at the El Oued water demineralization plant in Algeria
This paper presents an investigation into the energy potential of the Albian aquifer in the Algerian Sahara at the El Oued water demineralization plant, focusing on its capacity to generate electrical power due to its high-pressure and high-temperature water reserves. We designed and implemented a turbine-generator system to convert hydraulic energy into electricity, achieving an average annual energy output of 1,804,560 kWh, which translates to a financial gain of approximately 345,888,600 DZD per year from energy savings. The selection of a Francis turbine was justified based on its efficiency, which ranges from 90% to 95%, and the system design was simulated using MATLAB-Simulink, demonstrating its robustness and effectiveness in managing the electrical network parameters. Our economic analysis indicates a high return on investment, confirming the feasibility of utilizing the Albian aquifer as a strategic asset for clean and reliable energy production in the region.
Copyright (c) 2025 Khaled Miloudi, Ali Medjghou, Ala Eddine Djokhrab, Mosbah Laouamer, Souhaib Remha, Yacine Aoun http://creativecommons.org/licenses/by-sa/4.0
Modelling and optimization of hybrid renewable energy system using SBLA-MAT algorithm
In order to enhance the reliability and economic feasibility of power systems, this research presents a hybrid control method for the optimal design of hybrid renewable energy sources (RES), including fuel cells, solar photovoltaic (PV), and wind power. Optimization of the power system to enhance efficiency and reduce downtime is achieved using the side blotched lizard optimization with multi-objective artificial tree algorithm (SBL MAT). The research intends to reduce costs in wind, PV, and FC scenarios and make it reliable for load delivery at a low cost and high level of dependability. While a mathematical model of SBL behavior demonstrates the need to discover and implement global optimizing approaches, the MAT algorithm resolves the supervised classification challenge. Possible benefits of the proposed technology include increased reliability and decreased maintenance costs for electrical systems. The proposed approach enables cost-effective and reliable load generation from PV, wind, and fuel cell systems, regardless of the volatility of the weather. Using MATLAB/Simulink, the assessment of parameters like recall, specificity, accuracy and precision is carried out and the results were 99.91%, 99.85%, 99.65%, and 99.325%, respectively. The parameters loss of load expectation (LOLE) and loss of energy expectation (LOEE) are calculated for analysis using both current and future technology.
Copyright (c) 2025 Arun Kumar Udayakumar, P. Ashok, Mohan Das Raman, Krishnakumar Ramasamy, Mohammad Amir http://creativecommons.org/licenses/by-sa/4.0
Investigation of optimal tilt, orientation, and tracking of a solar PV system in Iraq
This paper examines the effect of tilt angle and tracking modes on energy performance of a PV system under Iraqi weather conditions. A 5-kWdc rooftop residential PV system is modeled and simulated using system advisor model (SAM) to investigate its optimal configuration of tilt angle and tracking axes for maximum energy extraction. The system is simulated with meteorological datasets for all 18 Iraqi provinces. The effect of soiling losses due to dust accumulation on incident irradiance and energy generation is considered as most Iraqi territories suffer from frequent dust storms yearly. The system annual AC energy and optimal tilt angles are evaluated and compared in five different scenarios including fixed-axis with tilt at latitude, fixed-axis with tilt at annual optimal angle, fixed-axis with tilt at monthly annual angle, one-axis tracking and dual-axis tracking. The results showed that considerable amount of energy is left unharnessed in fixed-axis scenarios when tilt angles are adjusted at latitude and optimal annual values. Using optimal monthly tilt with fixed-axis improved energy extraction by 5-6% for all locations. Energy performance is further improved with one axis tracking. Dual-axis tracking achieved highest energy yield compared to other scenarios. Overall, mid-south provinces provided highest energy opportunities among others.
Copyright (c) 2025 Ahmed Zurfi, Ali Abdul Razzaq Altahir, Ali Ibrahim http://creativecommons.org/licenses/by-sa/4.0
Intelligent MPPT system improved with sliding mode control
The sharp rise in global energy demand over recent decades has necessitated the exploration of alternative energy sources. Solar energy, known for being both pollution- and fuel-free, stands out as a preferred choice. However, its efficiency is sensitive to factors like temperature fluctuations and solar irradiation. To optimize energy extraction, a maximum power point tracking algorithm is crucial for photovoltaic systems. This paper proposes a robust sliding mode control enhanced with an artificial neural network to achieve the Maximum Power Point in a stand-alone PV system. The artificial neural network determines the reference voltage, which is then regulated by the sliding mode control to match the photovoltaic array voltage. The performance of the suggested controller is compared to that of a proportional integral-based neural network controller and the perturb and observe method using MATLAB/Simulink. The results show that the suggested method provides excellent tracking performance and rapid convergence even under quickly changing weather conditions, highlighting its efficiency and robustness.
Copyright (c) 2025 Said Dani, Asmaa Drighil, Khadija Abdouni, Khalid Sabhi http://creativecommons.org/licenses/by-sa/4.0
Cluster-based routing protocol in wireless sensor network
Wireless sensor networks (WSNs) play a crucial role in various domains, including military, industrial, and environmental applications, due to their capability to monitor and transmit data efficiently. However, one of the major challenges in WSNs is energy consumption, as sensor nodes rely on limited power sources for data acquisition, processing, and communication. Efficient energy management is essential to prolong network lifespan and maintain performance. To address this issue, several energy-efficient routing techniques have been developed. Among these, the low-energy adaptive clustering hierarchy (LEACH) has gained significant attention for its ability to optimize power consumption through hierarchical clustering. This study investigates the performance of the LEACH protocol under different deployment configurations. We proposed and evaluate a circular sensing field as an alternative to the traditional square and rectangular field. Simulation results show that the circular field achieves better energy efficiency and network longevity across various packet sizes and base station (BS) locations. These findings highlight the importance of deployment geometry in enhancing WSN sustainability.
Copyright (c) 2025 Shireen Bashar Ghanem, Aws Zuheer Yonis http://creativecommons.org/licenses/by-sa/4.0
Evaluating shading effects on photovoltaic modules: Mathematical modeling with ideal, single, and double diodes
Among the issues that solar systems face is partial shadowing that can be caused by many factors, such as trees, buildings, or clouds. A shaded module will produce less energy, which reduces the power supplied by a solar system based on PV panels. The purpose of this study is to model and simulate photovoltaic modules based on an ideal single and double diode. After that, we will simulate five configurations formed by nine photovoltaic solar panels: series (S), parallel (P), series-parallel (SP), bridge-link (BL), and total-cross-tied (TCT) under uniform and non-uniform cases (center, diagonal, and frame). These five PV solar configurations are compared in terms of short circuit currents (ISC), open circuit voltages (VOC), peak powers (PMP), the voltage and current values corresponding to maximum power (VMP, IMP), mismatch power loss (MPL), fill factor (FF), efficiency ratio (ER), and overall maximum power (OMP). The six PV configurations are simulated, considering the parameters of the STM6-40/36 PV module.
Copyright (c) 2025 Mohcine Abouyaakoub, Mbarek Chahboun, Ali Ait Ali, Aziz El Mrabet, Hicham Hihi, Souhail Barakat http://creativecommons.org/licenses/by-sa/4.0
Photovoltaic energy harvesting for the power supply of medical devices
The increasing demand for sustainable and reliable power sources in portable and implantable medical devices has led to growing interest in photovoltaic (PV) energy harvesting. Traditional power sources, such as batteries, are limited by finite energy capacity and frequent replacement or recharging needs, particularly in implantable devices where surgical intervention is required for battery replacement. Photovoltaic energy harvesting, which converts light into electrical energy, offers a promising alternative, especially in environments with consistent light exposure. This review provides an in-depth analysis of the advancements in PV technologies for powering medical devices. It covers various types of PV materials, design innovations, and the integration of energy storage systems. Additionally, the review highlights the application of PV systems in both external and implantable medical devices, while addressing critical challenges such as ensuring biocompatibility, optimizing performance in low-light conditions, and miniaturizing PV systems for implantation. The potential of PV energy harvesting to improve device longevity and reduce the need for invasive procedures is emphasized. This review concludes by outlining the current challenges and future directions needed to achieve widespread clinical adoption, aiming to contribute to the development of sustainable power solutions in healthcare.
Copyright (c) 2025 Hamza Abu Owida, Basem Abu Izneid, Nidal Turab http://creativecommons.org/licenses/by-sa/4.0
Comparative reliability and performance analysis of PV inverters with bifacial and monofacial panels
In the realm of solar energy systems, the reliability and performance of photovoltaic (PV) inverters play a critical role in ensuring efficient energy conversion and long-term operation. This study delves into a comprehensive reliability-oriented performance assessment of PV inverters, with a particular focus on the comparative analysis between bifacial and monofacial panels. Reliability evaluation is carried out by considering a yearly mission profile with a one-minute sample at Hyderabad, India. A test case of a 3-kW PV system for grid-connected applications is considered. By integrating reliability metrics with performance indicators, we aim to provide a holistic evaluation of PV inverters operating under varying conditions inherent to both panel types. The research methodology involves detailed simulations and field data analysis to capture the nuances of inverter performance influenced by the unique characteristics of bifacial panels, such as their ability to capture light from both sides, compared to the traditional monofacial panels. In this paper, performance parameters such as junction temperature, MCS, and B10 lifetime (system level (SL) and component level (CL)) are evaluated. Key findings highlight the impact of these differences on inverter reliability. The Bi-PV panel exhibits a decreasing trend. In India, CL reliability (B10) is decreased from 34 years to 1.5 years, and SL reliability (B10) is decreased from 24 years to 1 year. In comparison with monofacial panels, the thermal stress on the PV inverter due to the bifacial panel is increased, and reliability is decreased.
Copyright (c) 2025 Muneeshwar Ramavath, Rama Krishna Puvvula Venkata http://creativecommons.org/licenses/by-sa/4.0
Advances in medical power electronics: applications and challenges
Power electronics plays a crucial role in modern medical applications by providing efficient power management, conversion, and regulation across a wide range of devices. In high-power systems, such as medical imaging equipment, power electronics ensure precise control, stable operation, and optimal performance, which are essential for accurate diagnostic imaging. On the other hand, in low-power devices such as wearable health monitors and implantable medical devices, power electronics focus on enhancing energy efficiency and miniaturization. This is vital for extending battery life, reducing the need for frequent recharging or replacement, and improving patient comfort and mobility. This review examines the role of power electronics in diverse medical applications, highlighting its importance in enabling stable performance in critical life-support systems, therapeutic devices, and portable health monitors. Key technologies and power management integrated circuits are explored for their contribution to improving the efficiency, reliability, and longevity of medical devices. The review also addresses significant challenges, including miniaturization, energy efficiency, and regulatory compliance. Future trends such as the development of advanced semiconductor materials, innovations in energy harvesting techniques, and wireless power transfer technologies are also discussed. These advancements are expected to revolutionize the field, driving the next generation of medical devices and shaping the future of healthcare technology.
Copyright (c) 2025 Hamza Abu Owida, Jamal I. Al-Nabulsi, Nidal Turab, Muhammad Al-Ayyad http://creativecommons.org/licenses/by-sa/4.0
Fuzzy logic-based energy management system for a microgrid with hybrid energy storage: design, control, and comparative analysis
This paper presents a fuzzy logic-based energy management controller for a microgrid with a hybrid energy storage system. The microgrid integrates intermittent renewable energy sources. To provide high quality, reliable and sustainable power, the microgrid depends on energy storage devices. The proposed fuzzy logic-based energy management controller controls the energy storage system’s power electronic converters by generating switching pulses based on the generation availability, load requirement, SOCs of battery, and supercapacitor. Additionally, a fuzzy logic-based energy management system is planned in such a way that high power needs are satisfied by supercapacitors and high energy needs are satisfied by batteries. To highlight the key benefits of utilizing a fuzzy logic-controlled hybrid energy storage system over PI -a controller-based cascaded dual loop energy management system, a comparative study is carried out. The results of the same is discussed elaborately in this paper. These studies were simulated using the MATLAB/Simulink software package.
Copyright (c) 2025 Suganthi Neelagiri, Pasumarthi Usha, Siddalingappagouda Biradar http://creativecommons.org/licenses/by-sa/4.0
Fuzzy logic-based adaptive virtual inertia control for enhancing frequency stability in low-inertia microgrids
The increasing integration of renewable energy sources (RES) is accelerating the shift from traditional synchronous machine-based power systems to inverter-dominated grids. This transition poses significant frequency stability challenges, as power electronic interfaces lack the inherent kinetic energy storage of conventional generators, resulting in low system inertia. To address these challenges, this study proposes an adaptive virtual inertia control system based on fuzzy logic, which offers notable advancements in frequency dynamics. The proposed controller dynamically adjusts the virtual inertia constant in real-time by leveraging inputs such as frequency and the rate of change of frequency (RoCoF). This adaptive approach overcomes the limitations of fixed inertia systems, ensuring improved frequency stability and superior transient performance during load disturbances. Simulation results validate the system's effectiveness, showing reduced frequency overshoots, minimized deviations, and faster recovery to nominal frequency compared to conventional fixed inertia methods. By rapidly damping oscillations and enhancing transient stability, the proposed system significantly outperforms traditional techniques. Moreover, the study reviews current virtual inertia strategies, control topologies, and explores future research directions for integrating advanced virtual inertia into modern grids. These findings demonstrate the robustness of fuzzy logic based adaptive inertia for stabilizing low-inertia microgrids with high RES penetration.
Copyright (c) 2025 Omar Boutfarjoute, Hamid Chekenbah, Yassir Maataoui, Rafik Lasri http://creativecommons.org/licenses/by-sa/4.0
Enhancement of power quality of grid integrated photo voltaic system using active power filter
The world's population's energy needs are growing daily, while at the same time, fossil fuels are being reduced at an alarming rate. Fossil fuel burning also increases pollution and causes global warming. Renewable energies are now being extensively used to generate electricity, so the dependence on fossil fuels is considerably reduced. Among the primary sources of alternative energy used to create power is photovoltaic (PV) technology. A grid connected PV system is the most widely recommended. When PV is linked to the grid, two main issues are the maximum power that can be taken out of it and the quality of the electricity placed into it. With the help of neural networks, the maximum power point tracking (MPPT) technology has been developed to increase the PV array's power harvesting. An active power filter (APF) had been created and analyzed using Instantaneous Reactive Power Theory, including the Chebyshev II low-pass filter. As required by IEEE 519, the total harmonic distortion (THD) with injected source current has been confirmed well within 5%. These results demonstrate that this method is a simple and efficient way to inject harmonic-free currents into the grid.
Copyright (c) 2025 Praveen Kamat, Anant Naik http://creativecommons.org/licenses/by-sa/4.0
Oxygen/sulphur self-doped tunnel-like porous carbon from yellow bamboo for advanced supercapacitor applications
The 3D hierarchical pore structure with tunnel-like pores is essential to the performance of porous activated carbon (AC) materials used in symmetric supercapacitors. This study aimed to effect of adding (0.3, 0.5, and 0.7) M KOH reagent and heat treatment on the formation of 3D porous, tunnel-like AC derived from yellow bamboo (YB) through N2-CO2 pyrolysis at 850 °C. The AC produced had a high concentration of nanopores, becoming a valuable storage medium with favorable physical-electrochemical properties. The results showed that 0.5-YBAC had the best physical and electrochemical properties, with a carbon purity, 89.16%, micro crystallinity of 7.374 Å, and excellent amorphous porosity. Furthermore, 3D hierarchical pore structure, enriched naturally occurring heteroatoms, dopant of oxygen (10.14%) and sulfur (0.10%). A maximum surface area of 421.99 m² g⁻¹, along with a dominant combination of micro-mesopores. The electrochemical performance test of the 0.5-YBAC electrode showed a Csp of 214 F g⁻¹, with Esp 24.7 Wh kg⁻¹ and Psp 19.2 W kg⁻¹. In conclusion, this study showed the potential of YB stems to enhance the development of supercapacitors, offering superior porosity characteristics for efficient energy storage applications.
Copyright (c) 2025 Erman Taer, Novi Yanti, Rahma Lia Putri, Apriwandi Apriwandi, Awaludin Martin, Julnaidi Julnaidi, Nidya Chitraningrum, Ahmad Fudholi, Rika Taslim http://creativecommons.org/licenses/by-sa/4.0
Cost-effective optimization of unified power quality conditioner in wind energy conversion systems using a hybrid EnHBA-GWO algorithm
The rapid integration of wind energy conversion systems (WECS) into modern power networks has led to pressing power quality concerns, including voltage instability, harmonic distortion, and reactive power imbalance. To address these challenges, this study introduces a hybrid optimization strategy that combines the global search capabilities of the enhanced honey badger algorithm (EnHBA) with the local exploitation strengths of the grey wolf optimizer (GWO) for the best operational parameters of a unified power quality conditioner (UPQC). Extensive simulations in MATLAB Simulink demonstrate significant improvement in performance. The proposed method achieves 95% energy efficiency, a power factor of 0.99, and total harmonic distortion (THD) down to 5%, meeting IEEE 519-2022 standards. This outcome reflects an effective balance between cost and power quality performance, highlighting the potential of hybrid optimization to improve grid stability and efficiency in renewable energy environments.
Copyright (c) 2025 Shaziya Sultana, Umme Salma http://creativecommons.org/licenses/by-sa/4.0
Machine learning techniques for solar energy generation prediction in photovoltaic systems
For photovoltaic (PV) systems to be as effective and dependable as they possibly can be, it is vital to make an accurate prediction of the amount of power that will be generated by the sun. Using machine learning, it is now much simpler to forecast the amount of solar energy that will be generated. These approaches are more accurate and are able to adapt to the ever changing conditions of the nature of the environment. We take a look at the most recent machine learning algorithms for predicting solar energy and examine their methodology, as well as their strengths and drawbacks, in this paper. Using performance metrics like root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) makes it possible to evaluate important algorithms like support vector machines, decision trees, and linear regression. The results show that machine learning could help make predictions more accurate, lower the amount of uncertainty in operations, and help people make decisions in real time for PV systems. The study also points out important areas where research is lacking and suggests ways to move forward with the use of machine learning in systems that produce renewable energy.
Copyright (c) 2025 J. Sumithra, J. C. Vinitha, M. J. Suganya, M. Anuradha, P. Sivakumar, R. Balaji http://creativecommons.org/licenses/by-sa/4.0
Design and optimization of hybrid microgrid renewable energy system for electricity sustainability in remote area
Off-grid hybrid electrical systems have become a viable option for sustainable energy solutions, meeting the energy supply needs of rural communities. These systems use a broad approach to tackle sustainability, dependability, and environmental protection problems. The suggested hybrid system combines battery storage, biogas generators, and solar photovoltaic (PV) to provide a reliable and strong energy source for Ivoko Village in Enugu State using particle swarm optimization (PSO) and HOMER Pro Software. The paper compares three different configurations of sustainable power systems (HRES) to determine the best architecture that is suitable for rural areas. The result shows that case-1 (biogas/PV/bat) is the best option, with net present cost (NPC) and cost of energy (COE) values of $1,225,914 and 0.2865$/kWh, respectively. The results show that the PSO-based hybrid power system is more cost-effective than the HOMER-based optimizer. The NPC and lower COE for meeting peak demands emphasize the increasing role of biogas system generators as a cost-effective local power source. This highlights the PSO's potential in maximizing hybrid renewable power systems for rural areas, offering a financially viable and sustainable energy solution.
Copyright (c) 2025 Theresa Chinyere Ogbuanya, Taiwo Felix Adebayo http://creativecommons.org/licenses/by-sa/4.0
Digital twin-based performance evaluation of a photovoltaic system: A real-time monitoring and optimization framework
The digital twin (DT) technology implementation in photovoltaic (PV) systems provides an innovative approach to real-time performance monitoring and predictive maintenance. In this paper, an end-to-end DT framework for real-time performance analysis, fault detection, and optimization of a 250 W PV system is proposed. A physics-based equation and AI-based prediction hybrid DT model is developed through MATLAB/Simulink, trained from real data acquired by means of a testbed. The DT simulates the dynamic physical PV system behavior and adjusts itself using self-correcting algorithms to enhance precision in prediction and forecast power output at high fidelity. Results indicate that the DT gives the true response of the PV system with very small differences attributable to model approximations and sensor faults, 95% error minimization after compensation, and a root mean square error (RMSE) of 2.8 W, indicating its applicability for real-time monitoring and predictive main-maintenance. The work here focuses on the feasibility of applying DTs towards the autonomous optimization of distributed renewable energy systems.
Copyright (c) 2025 Mustafa Fadel, Fajer M. Alelaj http://creativecommons.org/licenses/by-sa/4.0
The Jordanian passage to sustainable electrical power: case study of challenges and opportunities
As the global energy sector faces significant challenges due to limited conventional resources and environmental concerns, many countries have adopted precautionary measures to secure and develop new energy resources. For instance, Jordan faces a severe shortage of natural conventional energy resources, compounded by rapid population growth driven by both locals and refugees. With over 90% of its energy imported, Jordan heavily depends on neighboring and international suppliers, leaving the country vulnerable and insecure due to political and economic fluctuations. To overcome these challenges, Jordan must establish comprehensive policies and plans to achieve energy production, conservation, and sustainability. This case study explores Jordan’s energy sources and security, highlighting strategies for long-term sustainable electrical energy development. The analysis focuses on addressing challenges, proposing alternative solutions, and advancing efficient plans for energy expansion. Key strategies include embracing renewable energy sources, enhancing conservation, and leveraging technological advancements to improve efficiency and a resilient energy sector.
Copyright (c) 2025 Emad Awada http://creativecommons.org/licenses/by-sa/4.0
Enhanced performance of PV systems using a smart discrete solar tracker with fuzzy-ant colony controller
A solar tracker is a combination of mechanical and electrical systems that can be used to move a solar panel to follow the sun's direction. This solar tracker system is expected to optimize the output power of photovoltaics. Based on existing research, many solar tracking systems have been developed using active tracking methods to increase the power consumption of the components of solar trackers. Therefore, a passive solar tracking system was used to reduce the solar tracker's internal energy consumption. In this study, a passive smart discrete solar tracker was designed with 3 positions and 5 tracking positions based on a fuzzy-ant colony controller (ACO). The design of a passive solar tracker based on a fuzzy-ACO has a performance index (average) with a rise time of 0.45 s, a settling time of 0.701 s, a maximum overshoot of 0.5%, and a steady-state error of 0.05%. From the design, the 3-position passive solar tracker with fuzzy-ACO control can increase efficiency with a gross energy gain of 42.79% for 10 hours compared to a fixed PV. The 5-position passive solar tracker using fuzzy-ACO control increased the efficiency with a gross energy gain of 43.99%.
Copyright (c) 2025 Imam Abadi, Najela Rafia Elchoir, Ali Musyafa’, Harsono Hadi, Dwi Nur Fitriyanah http://creativecommons.org/licenses/by-sa/4.0
Micro short circuit fault diagnosis in Li-ion cell
Micro short circuits (MSCs) in lithium-ion battery cells are a critical safety concern, potentially leading to thermal runaway, internal short circuits, overheating, and battery degradation. Compared to normal cells, MSC fault cells exhibit reduced capacity with each charge-discharge cycle and an increasing state of charge (SOC) deviation over time. To differentiate normal cells from MSC fault cells, a fault diagnosis method based on remaining charge capacity (RCC) estimation is proposed. After each charge discharge cycle, the cell’s RCC is compared to a safe threshold value. The method uses the charge cell voltage curve (CCVC) of a fully charged reference cell to estimate RCC via standard CCVC hypothetical conversion. This approach’s accuracy is validated in constant power and constant current charging scenarios. MSC leakage current is calculated by incrementing RCC after each charge, and then converted to MSC resistance. A MATLAB/Simulink model of a battery pack with an MSC fault was developed to test the method across various charge cut-off voltages. The diagnostic procedure’s applicability to ageing cells, constant power, and multi-step charging is further confirmed through experiments with external resistance, enhancing MSC detection before thermal runaway becomes unmanageable.
Copyright (c) 2025 S. Gomathy, Boopathi Dhanasekaran, Sivabalakrishnan Ramasamy, Radha Jayaram, Sabarimuthu Muthusamy, A. T. Sankara Subramanian http://creativecommons.org/licenses/by-sa/4.0
Quantum machine learning ensemble for surface crack detection
By identifying the aspects of manual inspection methods in the context of industrial production, which are described within the undertaken research, the development of an automated visual inspection technology is driven. This causes more time to be spent on performing the checks, thus adding to the labor cost. The efficiency of the operations is reduced, and there is a tendency for errors due to fatigue in checking 24/7. The proposed solution for a new product is designed to change the approach of the existing manufacturing process by using the automated system to self-inspect the surface and notify of its defects during manufacturing. As an enhancing advancement, this new development aims to address apprehensions pertaining to manual examination as the world transitions into the fault tolerant period. Lastly, this approach fits the universal grail of further developing industrial capacities, with the resulting thought process extending to the incorporation of technologies such as quantum computing with the current requirements of manufacturing. Other potential applications of this approach, including aerospace applications of ultrasonic testing or thermography in the detection of surface cracks, might also help improve this approach in the future.
Copyright (c) 2025 A. Sankaran, N. Palanivel, S. Dhamotharan, K. Nivas, V. Merwin Raj, M. Shivaprakash http://creativecommons.org/licenses/by-sa/4.0
Predicting transmission losses using EEMD – SVR algorithm
This work introduces a predictive model for evaluating transmission losses in the Java-Bali electrical system using ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) techniques. Transmission losses, a critical aspect of energy efficiency, are affected by several operational aspects, such as load flow, energy composition, peak load, and meteorological factors such as transmission line temperature. Transmission losses data were decomposed into many intrinsic mode functions (IMFs) by EEMD, effectively capturing both high-frequency (short-term) and low-frequency (long-term) trends. The SVR algorithm, utilizing a radial basis function (RBF) kernel, was subsequently employed to predict the deconstructed IMFs, facilitating accurate predictions of transmission losses. The proposed EEMD-SVR model achieved a mean absolute error (MAE) of 5.43%, with the highest error observed during the period of abrupt load shifts. These results confirm the model’s strength in identifying long-term transmission loss patterns, making it suitable for system planning and operational forecasting. While the model exhibited high prediction accuracy, especially in recognizing long-term trends, it faced limitations in accurately predicting abrupt changes in transmission losses. Therefore, future improvements should aim to enhance responsiveness to sudden changes in the system dynamics. The result suggests that the EEMD SVR model can proficiently assist power system operators in monitoring and mitigating transmission losses.
Copyright (c) 2025 Hesti Tri Lestari, Catherine Olivia Sereati, Marsul Siregar, Karel Octavianus Bachri http://creativecommons.org/licenses/by-sa/4.0
Inverter transient response improvement using grey wolf optimizer for type-2 fuzzy control in HVDC transmission link
High voltage direct current (HVDC) on transmission-link becomes a new prominent technology in recent years. The HVDC is applied to transmit amount of electrical energy from power plant to consumers. This method makes reactive power losses on transmission devices decrease significantly and stability level of generator increases. However, inverter HVDC transmission system can produce slow and high inverter transient current (ITC) response at high value of the up-ramp rate. This ITC phenomenon can be serious problem at starting time. So grey wolf algorithm is proposed to optimize input-output parameters of interval type-2 fuzzy control (IT2FC) in inverter-side HVDC. The proposed control performance’s is assessed by integral time squared error (ITSE) and peak overshoot (Mp) approaches. Simulation results show that small ITSE and low Mp of transient response are given by the IT2FC. The IT2FC is successful applied on inverter HVDC with better results compared to conventional PI control scheme.
Copyright (c) 2025 I Made Ginarsa, Agung Budi Muljono, I Made Ari Nrartha, Ni Made Seniari, Sultan, Osea Zebua http://creativecommons.org/licenses/by-sa/4.0