Parameters tuning of doubly fed induction generator systems with static synchronous compensator based on Chaos orthogonal particle swarm optimization

2018 International Conference on New Energy and Future Energy System

Wei, W.^1 ; Shi, L.J.^1 ; Shi, J.F.^1 ; Lin, K.M.^1 ; Wu, F.^1 ; He, W.G.^2 ; Wang, C.^2

College of Energy and Electrical Engineering, Hohai University, Jiangsu Province Nanjing

211100, China^1

State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems, China Electric Power Research Institute, Jiangsu Province Nanjing

210003, China^2

关键词: Double fed induction generator;    Doubly fed induction generator (DFIG);    Dynamic performance;    Integral time absolute errors;    Optimization algorithms;    Point of common coupling;    Proportional integral controllers;    Static synchronous compensator (STATCOM);   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/188/1/012048/pdf
DOI  :  10.1088/1755-1315/188/1/012048

来源: IOP

【 摘 要 】

For Double-Fed Induction Generator (DFIG) wind farm systems with Static Synchronous Compensator (STATCOM), by optimizing multiple proportional integral (PI) controllers parameters of DFIG and STATCOM, the performance of the wind power system is significantly improved and the voltage of point of common coupling (PCC) is quickly restored when a low voltage due to a grid fault. Chaos orthogonal particle swarm optimization is proposed to make the integral time absolute error (ITAE) of active power of DFIG, DC-link voltage of converter of DFIG and voltage of PCC minimum. The orthogonal method is used to get the range of parameters and identify the weight relations between different indicators. And then the chaos algorithm is used to initialize the algorithm. Finally, the particle swarm optimization (PSO) can effectively improve the efficiency of the optimization. Based on MATLAB/Simulink, the simulations of wind farm containing STATCOM incorporated into the infinite system under different conditions both show that the application optimized PI controllers have good dynamic performance, which validates the effectiveness of the optimization algorithm.