Parameter Extraction Model of Wind Turbine Based on A Novel Pigeon-Inspired Optimization Algorithm

Abstract

This paper has been designed to address the problems of slow convergence and low convergence accuracy of the pigeon-inspired optimization (PIO) algorithm. The evolutionary mechanism of the PIO algorithm contains two stages, exploration and exploitation, which also exist to solve various numerical optimization problems not well. In order to solve the above problems, this paper proposes a novel pigeon-inspired optimization (NPIO) algorithm, which fuses the two stages of the operator into one stage, where exploitation and exploration are carried out simultaneously, and can assist the algorithm to find the optimal solution better. Numerical optimization problems can be solved with a smaller number of iterations. To verify the performance of the NPIO, standard test functions and practical application scenarios are selected for validation. Firstly, this paper uses 23 test functions to test and cross-sectionally compare with five optimization algorithms. The experimental results show that the NPIO is more competitive than the other five algorithms. Secondly, this paper is based on a high-precision mathematical model commonly used for wind turbines. It uses measurable quantities of wind turbines under actual operating conditions for the theoretical analysis of parameter identifiability. The results show that NPIO has a strong performance in wind turbine parameter identification.