Optimized control of DFIG based wind generation using swarm intelligence

In this paper, a particle swarm optimization with ε-greedy (ePSO) algorithm and group search optimizer (GSO) algorithm are compared with the classic PSO algorithm for the optimal control of DFIG wind generation based on small signal stability analysis (SSSA). In the modified ePSO algorithm, the cooperative learning principle among particles has been introduced, namely, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other best particles according to certain probability. The proposed ePSO algorithm has been tested on benchmark functions and demonstrated its effectiveness in high-dimension multi-modal optimization. Then ePSO is employed to tune the controller parameters of DFIG based wind generation. Results obtained by ePSO are compared with classic PSO and GSO, demonstrating the improved performance of the proposed ePSO algorithm.

[1]  Haibo He,et al.  Reactive power control of grid-connected wind farm based on adaptive dynamic programming , 2014, Neurocomputing.

[2]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[3]  Niraj Kshatriya,et al.  Optimized Partial Eigenstructure Assignment-Based Design of a Combined PSS and Active Damping Controller for a DFIG , 2010, IEEE Transactions on Power Systems.

[4]  Jon Clare,et al.  Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation , 1996 .

[5]  S. Mishra,et al.  Small-Signal Stability Analysis of a DFIG-Based Wind Power System Under Different Modes of Operation , 2009, IEEE Transactions on Energy Conversion.

[6]  Wei Qiao,et al.  Computational intelligence for control of wind turbine generators , 2011, 2011 IEEE Power and Energy Society General Meeting.

[7]  Yong Yan,et al.  Group search optimizer based optimal location and capacity of distributed generations , 2012, Neurocomputing.

[8]  Kit Po Wong,et al.  Optimal controller design of a doubly-fed induction generator wind turbine system for small signal stability enhancement , 2010 .

[9]  Q. Henry Wu,et al.  Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior , 2009, IEEE Transactions on Evolutionary Computation.

[10]  Xiao-Ping Zhang,et al.  Small signal stability analysis and optimal control of a wind turbine with doubly fed induction generator , 2007 .

[11]  C. Y. Chung,et al.  Coordinated Damping Control Design for DFIG-Based Wind Generation Considering Power Output Variation , 2012, IEEE Transactions on Power Systems.

[12]  Q. Henry Wu,et al.  Optimal placement of FACTS devices by a Group Search Optimizer with Multiple Producer , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[13]  Ryan Wiser,et al.  2011 Wind Technologies Market Report , 2012 .