Multi-objective proportional-integral-derivative optimization algorithm for parameters optimization of double-fed induction generator-based wind turbines

The meta-heuristic algorithm inspired by natural may reduce the optimization performance due to excessive imitation. This paper proposes a novel multi-objective proportional-integral-derivative optimization algorithm inspired by mathematical thought to provide a better non-dominated solution for multi-objective problems. The idea of the proportional-integral-derivative control algorithm is introduced to cooperate with multi-objective optimization problems for the first time. The proposed algorithm is employed to store and maintain non-dominated solutions. Two groups of controllers of the proposed algorithm are designed for the multi-objective optimization problems, i.e., exploitative controllers aim to obtain the local optimal solution; explorative controllers aim to obtain the global optimal solution. To verify the effectiveness of the multi-objective proportional-integral-derivative optimization algorithm, eight comparison algorithms are compared with eight benchmark functions; five comparison algorithms are compared under the multi-objective parameters optimization problem of double-fed induction generator-based wind turbines. The results of benchmark functions show that the multi-objective proportional-integral-derivative optimization algorithm has superior convergence performances and outperforms other comparison algorithms. The proposed algorithm has excellent optimization performance to obtain the minimum deviation of rotor speed and reactive power for the wind power system controller. (C) 2021 Elsevier B.V. All rights reserved.