有源电力滤波器神经终端滑模控制

NEURAL TERMINAL SLIDING MODE CONTROL FOR ACTIVE POWER FILTER

以电力电子装备为接口的高渗透率可再生能源并网已成为未来配电网的显著特性。可再生能源具有随机性和间歇性,作为其并网接口的电力电子装备也会导致电能质量恶化等问题。为提高电能质量,该文提出一种有源电力滤波器神经终端滑模控制方法。首先,结合分数阶思想和滑模控制理论设计一种分数阶终端滑模控制器,以保证误差有限时间收敛,并引入边界层技术降低抖振。然后,利用自组织模糊神经网络构造一种无模型控制方案以更好地应对各种不确定因素。所设计的自组织模糊神经网络控制器用于学习分数阶终端滑模控制器,不仅从根源上解决抖振问题,而且可继承原控制器的有限时间收敛性能,并满足李雅普诺夫理论框架下的稳定控制性能。仿真与实验结果表明:所提出的控制方法能有效解决可再生能源发电系统中的谐波问题。

High permeability renewable energy interconnection taking power electronic equipment as interface has become a prominent feature of future power distribution networks. Renewable energy possesses randomness and intermittence,and power electronic equipment as grid-connection interfaces also will lead to the deterioration of power quality. In order to improve power quality,a neural terminal sliding mode control for active power filter is proposed in this paper. Firstly,using fractional order scheme and sliding mode theory,a fractional-order terminal sliding mode control(FOTSMC)is designed to ensure the finite-time error convergence,and the boundary layer technique is utilized to eliminate the chattering problem. Moreover,a model-free control scheme is designed using selforganizing fuzzy neural network(SOFNN)to better handle uncertainties. The SOFNN controller is designed for learning FOTSMC,which not only solves the chattering problem fundamentally,but also inherits the finite time convergence performance of FOTSMC and satisfies the stability control performance in the framework of Lyapunov theory. Simulation and experimental results demonstrate that the proposed control methods can effectively solve the harmonic problem in the renewable energy generation system.