多目标无功优化的向量评价自适应免疫粒子群算法

为了克服粒子群算法在高维复杂问题寻优时容易陷入局部搜索的现象,提出了一种自适应免疫粒子群算法。该算法利用引入免疫系统的免疫信息处理机制和自动调整动量系数的自适应因子,从整体上达到系统的最佳控制方案。并将基于目标向量的个体评价方法与自适应免疫粒子群算法相结合,提出了基于向量评价的自适应免疫粒子群算法(vector evaluated adaptive immune particle swarm optimization,VEAIPSO)来解决多目标无功优化问题。通过引入静态电压稳定指标,建立了以系统有功损耗最小、节点电压偏移量最小及静态电压稳定裕度最大为目标的多目标无功优化模型。IEEE30和IEEE118节点系统算例仿真结果表明,该算法能有效地解决多目标无功优化问题,并具有良好的收敛稳定性和较高的寻优精度。

Vector Evaluated Adaptive Immune Particle Swarm Optimization Algorithm for Multi-objective Reactive Power Optimization

An adaptive immune particle swarm optimization（AIPSO） algorithm is presented to solve the problem that the conventional particle swarm optimization（PSO） algorithm is easy to fall into a locally optimized point.In the proposed algorithm,the optimal control mode on the whole is achieved by applying the immune system with immune information transacting mechanism and PSO with adaptive factor for automatic adjustment of momentum coefficient.Object vector individual evaluation methods and adaptive immune particle swarm optimization are combined to produce the vector evaluated adaptive immune particle swarm optimization（VEAIPSO） to solve multi-objective reactive power optimization problem.With the introduction of the static voltage stability index,multi-objective reactive power optimization model is established,which can gain the object including the minimum of system active power loss,the minimum of node voltage offset and the maximum of static voltage stability margin.The simulation results of the standard IEEE-30-bus and IEEE-118-bus power system indicate that the algorithm is efficient in solving multi-objective reactive power optimization problem and has favorable convergence stability and higher optimum searching precision.