Adaptive RBF Neural Network Control for Three-Phase Active Power Filter

Abstract

An adaptive radial basis function (RBF) neural network control system for three‐phase active power filter (APF) is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non‐linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achieve the desired tracking task. The simulation results demonstrate good performance, for example showing small current tracking error, reduced total harmonic distortion (THD), improved accuracy and strong robustness in the presence of parameters variation and nonlinear load. It is shown that the adaptive RBF neural network control system for three‐phase APF gives better control than hysteresis control

Adaptive Backstepping Current Control of Active Power Filter Using Neural Compensator

“…Adaptive neural controller was put forward to treat the harmonics in APF and improve the power grid quality in [22][23][24]. In this work, an adaptive neural backstepping scheme is designed to guarantee the current tracking and improve the system robustness.…”