Simulation and Real‐Time Implementation for Power Quality Enhancement Systems Using Adaptive Neural Network Strategy

Abstract

To address the requirements of current tracking control and power quality improvement for active power filters (APF), this paper proposes a nested terminal sliding mode control scheme based on hippocampal neural network to overcome the performance limitations of existing APF control methods. First, the circuit structure of the APF is elaborated, and the mathematical model including lumped system uncertainties is derived. Then, a nested terminal sliding mode surface is designed to ensure that the tracking error converges to zero in finite time, which achieves performance improvement compared with traditional linear sliding mode control that can only realize asymptotic convergence. Afterward, a hippocampal‐inspired neural network that mimics the human hippocampal information processing mechanism is introduced for the first time to learn the unknown nonlinear terms in the sliding mode controller, effectively weakening the adverse effects of system uncertainties on control performance. A novel feature selection mechanism is proposed to extract and process key information within the network, greatly reducing the network computational overhead. A dual‐loop structure is designed in the neural network to improve the processing efficiency of time‐varying harmonic signals, and the online adaptive update law of network parameters is derived based on Lyapunov theorem to guarantee system stability. Finally, simulation and hardware experimental results verify the effectiveness of the proposed algorithm. This method reduces the total harmonic distortion (THD) of the grid source current to 1.29% in simulation and 3.03% in experiment. Compared with mainstream methods, it exhibits excellent current tracking ability, strong robustness, and superior grid harmonic suppression performance.