Multi-objective high-dimensional multi-fractional-order optimization algorithm for multi-objective high-dimensional multi-fractional-order optimization controller parameters of doubly-fed induction generator-based wind turbines

Abstract

Aiming at the operation control of doubly-fed induction generator-wind energy systems, this study firstly introduces multi-dimensional information feedback and fractional-order theory and designs a high-dimensional multi-fractional-order controller in the rotor side converter. Secondly, for the tuning optimization of controller parameters of doubly-fed induction generators, this study proposes a high-dimensional multi-fractional-order optimization. Finally, this study proposes a multi-objective high-dimensional multi-fractional-order optimization algorithm for the multi-objective optimization problems of control parameters tuning and optimization. This study adopts an adaptive grid strategy to update non-dominated solutions for external archives with crowded conditions. Furthermore, this study adopts a roulette strategy to select the optimal global position in the iterative process. This study utilizes multi-objective test functions to verify the effectiveness of the proposed multi-objective optimization. The Pareto optimal solutions and five types of statistical data from two indexes show that the proposed multi-objective optimization has improved convergence, diversity, and operational stability. Compared with five algorithms, the proposed multi-objective optimization can obtain Pareto optimal solutions with uniform distribution and good diversity in the multi-objective tuning of control parameters of the designed multi-objective controllers for doubly-fed induction generators. Therefore, decision-makers can choose the control parameters according to the actual needs. The test results show that the designed multi-objective controller optimized by the proposed multi-objective optimization has more accurate power tracking ability and superior control performance.