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

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.

Automated summary

A novel bi-objective optimal wind farm energy capture (OWFEC) algorithm is proposed in this study, which considers both maximum power output and the balance of fatigue load distribution to reduce maintenance cost. To rapidly obtain high-quality Pareto optimal solutions, a decomposition-based multi-classifier-assisted evolutionary algorithm is designed. Simulations are carried out with three different scales of wind farms, and five familiar Pareto-based meta-heuristic algorithms are introduced for performance comparison to evaluate the effectiveness and performance of the proposed technique.