Multi-Objective Optimisation for Large-Scale Offshore Wind Farm Based on Decoupled Groups Operation

Abstract

Operation optimization for large-scale offshore wind farms can cause the fatigue loads of single wind turbines to exceed their limits. This study aims to improve the economic profit of offshore wind farms by conducting multi-objective optimization via decoupled group operations of turbines. To do this, a large-scale wind farm is firstly divided into several decoupled subsets through the parallel depth-first search (PDFS) and hyperlink-induced topic search (HITS) algorithms based on the wake-based direction graph. Next, three optimization objectives are considered, including total output power, total fatigue load, and fatigue load dispatch on a single wind turbine (WT) in a wind farm. And then, the combined Monte Carlo and beetle swarm optimization (CMC-BSO) algorithms are applied to solve the multi-objective non-convex optimization problem based on the decentralized communication network topology. Finally, the simulation results demonstrate that the proposed method balances the total power output, fatigue load, and single fatigue loads with fast convergence.