Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems  

作　　者：Dianwei Qian Guoliang Fan 

机构地区：[1]School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China [2]Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2018年第3期706-717,共12页自动化学报（英文版）

基　　金：supported by National Natural Science Foundation of China(60904008,61273336);the Fundamental Research Funds for the Central Universities(2018MS025);the National Basic Research Program of China(973 Program)(B1320133020)

摘　　要：This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.This paper addresses a terminal sliding mode control（T-SMC） method for load frequency control（LFC） in renewable power systems with generation rate constraints（GRC）.A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks（RBF NNs） are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.

关 键 词：Generation rate constraint(GRC) load frequency control(LFC) radial basis function neural networks(RBF NNs) renewable power system terminal sliding mode control(T-SMC) 

分 类 号：TP[自动化与计算机技术]