邓吉祥,丁晓群,张杭,何健,蒋丹.基于量子人工蜂群算法的风电场多目标无功优化[J].电测与仪表,2015,52(3):. DENG Ji-xiang,DING Xiao-qun,ZHANG Hang,HE Jian,JIANG Dan.Multi-Objective Reactive Power Optimization for Wind Farm Based on Quantum Artificial Bee Colony Algorithm[J].Electrical Measurement & Instrumentation,2015,52(3):.
基于量子人工蜂群算法的风电场多目标无功优化
Multi-Objective Reactive Power Optimization for Wind Farm Based on Quantum Artificial Bee Colony Algorithm
In order to analyze the impact of uncertain output of wind driven generators on power grid operation, a probabilistic model of wind farm is established, and the two point estimation method is used for the probabilistic load flow calculation. Then, a multi-objective reactive power optimization model is established, including the network losses, the voltage offset and static voltage stability margin, and the weights are all determined by the AHP algorithm, avoiding the subjective nature. Then the quantum artificial bee colony algorithm (QABC) is proposed, and it is used in the reactive power optimization in wind farm with the probabilistic load flow model. At last, taking the IEEE14 nodes system as an example, the wind farm is connected into this system, conducting reactive power optimization, and the results show that the QABC algorithm is better and has higher convergence precision, effectively avoiding the premature, compared with the traditional artificial bee colony algorithm(ABC).
