混合有源电力滤波器的新型电流迭代学习控制

混合有源电力滤波器可以动态抑制电网谐波电流和补偿容性无功功率,改善电网电能质量。针对传统PI型迭代学习控制算法在并联有源电力滤波器应用中的不足,算法收敛性严重依赖于学习控制的初始输入,迭代学习控制器的参数是定常值,会影响有源滤波系统的控制性能。本文提出一种新型PI迭代学习控制算法,将其应用于混合有源电力滤波器系统的电流反馈控制中,得到了应用迭代算法的收敛性条件,并采用一种改进的Ziegler-Nichols方法对控制器参数进行了优化,以提高系统的控制精度。为了提高系统的动态响应性能,提出一种谐波电流误差的反馈-前馈控制策略,其中电流误差信号的D型前馈控制环节用于实现滤波器系统的电流快速补偿,同时利用一个三层BP神经网络对前馈控制增益进行优化。仿真和实验结果证明了该迭代算法及控制策略的可行性与有效性。

A Novel Iterative Learning Control for Current Tracking of Hybrid Active Power Filter

The traditional PI-type iterative learning control has some defects such as the convergence of control algorithm depending heavily on initial input of learning control and the parameters of iterative controller being constant value.A new algorithm of PI-type iterative learning control is proposed in this paper for current feedback control of hybrid active power filter,and the convergence condition of applying iterative algorithm in filter system is obtained.An improved Ziegler-Nichols method is used to optimize control parameters of feedback controller for improving current compensation precision.In order to improve the dynamic response performance of filter system,a feed-forward link based D-type learning law of harmonic current error signal as control input is added to feedback controller,which constitutes a feedback-feed-forward control strategy of harmonic current error.A neural network with three-layer based on BP algorithm is applied for optimizing control gain of feed-forward link.Simulation and experimental results confirm the value of the proposed iterative algorithm and control strategy.