Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization

作     者：Zhonghao Qian Hanyi Ma Jun Rao Jun Hu Lichengzi Yu Caoyi Feng Yunxu Qiu Kemo Ding 

作者机构：State Grid Jiangsu Nantong Electric Power Co.Ltd.Nantong226000China College of Energy and Electrical EngineeringHohai UniversityNanjing211100China 

出 版 物：《Energy Engineering》 (能源工程（英文）)

年 卷 期：2023年第120卷第9期

页      面：2013-2027页

核心收录：

学科分类：080703[工学-动力机械及工程] 080704[工学-流体机械及工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：This work was supported by Technology Project of State Grid Jiangsu Electric Power Co. Ltd. China(J2022114 Risk Assessment and Coordinated Operation of Coastal Wind Power Multi-Point Pooling Access System under Extreme Weather) 

主　　题：Offshore wind farms improved particle swarm optimization reactive power optimization adaptive weight asynchronous learning factor voltage stability 

摘      要：The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind *** improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind ***,the power flow of offshore wind farms is modeled,analyzed and *** improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning *** the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering ***,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of *** the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is ***,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.