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

• Main Authors: Gao, X. (Author), Zhang, X. (Author), Zhao, L. (Author), Zhu, H. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: Bi objectives; bi-objective optimization; Bi-objective optimization; Electric power plant loads; Electric utilities; Energy capture; Evolutionary algorithms; Farm's energy; fatigue load; Fatigue load; Fatigue loads; Heuristic algorithms; Multi-classifier; Optimisations; Pareto principle; Pareto-based optimization; Power quality; wake effect; Wake effect; Wakes; wind farm; Wind farm
• Online Access: View Fulltext in Publisher

 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. © 2023 by the authors.