Neural Networks Based Shunt Hybrid Active Power Filter for Harmonic Elimination

The growing use of nonlinear devices is introducing harmonics in power system networks that result in distortion of current and voltage signals causing damage to power distribution systems. Therefore, in power systems, the elimination of harmonics is of great significance. This paper presents an efficient techno-economical approach to suppress harmonics and improve the power factor in power distribution networks using Shunt Hybrid Active Power Filters (SHAPF) based on neural network algorithms like Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Recurrent Neural Network (RNN). The objective of the proposed algorithms for SHAPF is to enhance system performance by reducing Total Harmonic Distortion (THD). In our filter design approach, we tested and compared conventional pq0 theory and neural networks to detect the harmonics present in the power system. Moreover, for the regulation of DC supply to the inverter of the SHAPF, the conventional PI controller and neural networks-based controllers are used and compared. The applicability of the proposed filter is tested for three different nonlinear load cases. The simulation results show that the neural networks-based filter control techniques satisfy all international standards with minimum current THD, neutral wire current elimination, and small DC voltage fluctuations for voltage regulation current. Furthermore, the three neural network architectures are tested and compared based on accuracy and computational complexity, with RNN outperforming the rest.

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