Implementation of reactive compensator for voltage balancing using AI based models and novel performance index

Dana Ragab, Jasim Ghaeb


Voltage-unbalance is one of the power quality deficiencies that degrades electrical power systems performance. In this work, voltage unbalance problem is tackled through two stages; evaluation using a novel performance index and mitigation using a thyristor-controlled reactor (TCR) compensator with artificial intelligent (AI) based models. Unlike standard performance indices that rely on voltages' root mean square (RMS) values, the proposed index depends on the space vector (SV) signal amplitude for voltage unbalance evaluation. This signal depends on the instantaneous values of the three-phase voltages and has twice the system frequency. Therefore, the proposed index entitled as space vector unbalance factor (SVUF) reflects the amount of voltage unbalance and reduces the time necessary for evaluation by half. Subsequently, advanced models based on several algorithms are proposed to generate the required firing angles for TCR compensator to restore voltage balance, including radial basis functions networks (RBFNs), hybrid-RBFNs (H-RBFNs), polynomials (PNs), and simplified neural networks (NNs). Models' structure, prediction capability, and response time are analyzed. Results show that the time required for voltage unbalance mitigation is reduced. Moreover, the models used to generate the firing angles are simplified significantly while maintaining high accuracy.


neural network; power quality; radial basis functions networks; space vector; thyristor controlled reactor; voltage unbalance

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