Decomposition-Based Multi-Classifier-Assisted Evolutionary Algorithm for Bi-Objective Optimal Wind Farm Energy Capture

Hongbin Zhu, Xiang Gao (), Lei Zhao and Xiaoshun Zhang
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Hongbin Zhu: College of Engineering, Shantou University, Shantou 515063, China
Xiang Gao: Industrial Training Centre, Shenzhen Polytechnic, Shenzhen 518055, China
Lei Zhao: College of Engineering, Shantou University, Shantou 515063, China
Xiaoshun Zhang: Foshan Graduate School of Innovation, Northeastern University, Foshan 528311, China

Energies, 2023, vol. 16, issue 9, 1-22

Abstract: With the wake effect between different wind turbines, a wind farm generally aims to achieve the maximum energy capture by implementing the optimal pitch angle and blade tip speed ratio under different wind speeds. During this process, the balance of fatigue load distribution is easily neglected because it is difficult to be considered, and, thus, a high maintenance cost results. Herein, a novel bi-objective optimal wind farm energy capture (OWFEC) is constructed via simultaneously taking the maximum power output and the balance of fatigue load distribution into account. To rapidly acquire the high-quality Pareto optimal solutions, the decomposition-based multi-classifier-assisted evolutionary algorithm is designed for the presented bi-objective OWFEC. In order to evaluate the effectiveness and performance of the proposed technique, the simulations are carried out with three different scales of wind farms, while five familiar Pareto-based meta-heuristic algorithms are introduced for performance comparison.

Keywords: wind farm; wake effect; fatigue load; Pareto-based optimization; bi-objective optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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