Optimized Control of DFIG-Based Wind Generation Using Sensitivity Analysis and Particle Swarm Optimization

Optimal control of large-scale wind farm has become a critical issue for the development of renewable energy systems and their integration into the power grid to provide reliable, secure, and efficient electricity. Among many enabling technologies, the latest research results from both the power and energy community and computational intelligence (CI) community have demonstrated that CI research could provide key technical innovations into this challenging problem. In this paper, we propose a sensitivity analysis approach based on both trajectory and frequency domain information integrated with evolutionary algorithm to achieve the optimal control of doubly-fed induction generators (DFIG) based wind generation. Instead of optimizing all the control parameters, our key idea is to use the sensitivity analysis to first identify the critical parameters, the unified dominate control parameters (UDCP), to reduce the optimization complexity. Based on such selected parameters, we then use particle swarm optimization (PSO) to find the optimal values to achieve the control objective. Simulation analysis and comparative studies demonstrate the effectiveness of our approach.