Open Access

ARTICLE

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

Zhonghao Qian¹, Hanyi Ma¹, Jun Rao², Jun Hu¹, Lichengzi Yu^2,*, Caoyi Feng¹, Yunxu Qiu¹, Kemo Ding¹

1 State Grid Jiangsu Nantong Electric Power Co., Ltd., Nantong, 226000, China
2 College of Energy and Electrical Engineering, Hohai University, Nanjing, 211100, China

* Corresponding Author: Lichengzi Yu. Email:

(This article belongs to the Special Issue: Utilizing Particle Swarm Optimization Algorithm for Energy Management Application)

Energy Engineering 2023, 120(9), 2013-2027. https://doi.org/10.32604/ee.2023.028859

Received 11 January 2023; Accepted 06 April 2023; Issue published 03 August 2023

Abstract

The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms. To improve the voltage stability and reactive power economy of wind farms, the improved particle swarm optimization is used to optimize the reactive power planning in wind farms. First, the power flow of offshore wind farms is modeled, analyzed and calculated. To 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 factor. Taking 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 needs. Finally, a reactive power optimization model based on Static Var Compensator is established in MATLAB to consider the optimal compensation capacity, network loss, convergence speed and voltage amplitude enhancement effect of SVC. Comparing the compensation methods in several different locations, the compensation scheme with the best reactive power optimization effect is determined. Meanwhile, 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.

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