Strategy of An Adaptive ANN Based Efficiency Optimization for DTC Electric Motor Drive System

Sim Sy Yi


Electric motor drive systems (EMDS) have been recognized as one of the most promising motor systems recently due to their low energy consumption and reduced emissions. With only some exceptions, EMDS are the main source for the provision of mechanical energy in industry and accounts for about 60% of global industrial electricity consumption. Large energy efficiency potentials have been identified in EMDS with many saving options showing very short payback period and high-cost effectiveness. Typical motor drive has good efficiencies while operating at its rated condition. However, in many applications, a motor operates far from its rated operating point, particularly at light load, which impairs the efficiency. Therefore, a conventional drive system should be associated with a loss minimization strategy to maximize the drive efficiency for a wider range of operation. For a given operating condition, the losses can be minimized by adjusting to the appropriate flux level instead of conventionally keeping constant at its rated flux value. Consequently, this research proposes an online learning Artificial Neural Network (ANN) controller with the aim to generate an adaptive flux level to optimize the efficiency at any different operating points, especially at light load condition. The entire proposed strategic drive system would be verified under the MATLAB/Simulink software environment. It is expected that with the proposed online learning Artificial Neural Network controller efficiency optimization algorithm can achieve better energy saving compared with traditional blended strategies. 


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