@Article{ee.2023.028859,
AUTHOR = {Zhonghao Qian, Hanyi Ma, Jun Rao, Jun Hu, Lichengzi Yu, Caoyi Feng, Yunxu Qiu, Kemo Ding},
TITLE = {Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization},
JOURNAL = {Energy Engineering},
VOLUME = {120},
YEAR = {2023},
NUMBER = {9},
PAGES = {2013--2027},
URL = {http://www.techscience.com/energy/v120n9/53712},
ISSN = {1546-0118},
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.},
DOI = {10.32604/ee.2023.028859}
}