Adaptive Neural Network for a Stabilizing Shunt Active Power Filter in Distorted Weak Grids

Abstract

Harmonics destructively impact the performance and stability of power systems. This paper proposes the development of a stable shunt active power filter (SAPF) for harmonics mitigation. The proper and stable operation of the SAPF control system requires the determination of the current reference, phase angle synchronization, and DC-link voltage regulation. This paper uses an artificial neural network (ANN) and one of its sub-methods, the adaptive linear neuron (ADALINE), to determine the current reference. However, determining the current reference requires providing a stable phase angle, which is a fundamental challenge in distorted grids because harmonics created in the grid cause phase angle synchronization problems, due to malfunction of the conventional phase-locked loop (PLL). These things considered, the weak grid connection imposes an instability issue due to the poor performance of the conventional PLL when the grid impedance is high. In this paper, a robust synchronous filter (RSF) is adopted, which separates the harmonic from the main component to provide harmonics-free signals for the PLL. Using RSF, a robust synchronizer quasi-static filter (RSQSF) PLL model is designed, which is effective in dealing with harmonics in weak-grid conditions. MATLAB Simulink was used to check the validation and effectiveness of the proposed control structure. The results show a reduction in harmonics generated in the grid by 86.7% for nonlinear load with a balanced source, 84% for nonlinear load with an unbalanced source under grid impedance, and 80.46% for the nonlinear load with an unbalanced source under weak-grid conditions.