自适应精英变异粒子群算法在风电场无功优化中的应用

Application of Adaptive Elite Mutation Particle Swarm Algorithm in Reactive Power Optimization of Wind Plants

为提升含风电场的电力系统无功优化效果,基于含风电场多目标无功优化模型进行归一化处理并采用层次分析法确定各优化目标权重构建目标函数,提出一种自适应精英变异粒子群算法对目标函数进行求解。该算法根据粒子在每次迭代进化的实时位置并融合精英粒子变异策略以克服早熟收敛,通过测试函数和含风电场IEEE14测试系统无功优化的仿真实验表明,该算法具有收敛精度高和稳定性好等优点,对含风电场的电力系统多目标无功优化具有良好的无功优化效果。

In order to improve the reactive power optimization effect of power system of wind plants,based on the normalized processing of multi-objective reactive power optimization model of wind plants and the reconstruction of each optimization objective weight determined by analytic hierarchy process(AHP),this paper puts forward an adaptive elite mutation particle swarm algorithm to solve the objective function.According to the real-time position of particles in each iteration,the algorithm overcomes premature convergence by combining elite particle mutation strategy.The simulation results of test function and IEEE14 test system of wind plants show that the algorithm has the advantages of high convergence accuracy and good stability,which has good reactive power optimization effect for multi-objective reactive power optimization of power system of wind plants.