改进遗传算法结合灵敏度分析的无功优化

针对标准遗传算法(SGA)在用于实际大规模电网的无功优化时存在搜索空间大、计算时间长的问题,提出一种结合灵敏度分析的改进遗传算法(IGACSA)。IGACSA算法对SGA的交叉和变异操作进行改进,改进交叉操作使得算法具有快速局部微调能力,而改进变异操作则结合灵敏度产生新的个体,最后由灵敏度对IGACSA的结果进行调整。在无功优化中采取两步简单的措施,将IGACSA应用于对新加无功补偿设备容量的确定,使其更适用于实际电力系统。对实际电网的无功优化表明,所提算法缩短了无功优化的时间,并可得到更好的优化结果。

Application of Improved Genetic Algorithm Combining Sensitivity Analysis to Reactive Power Optimization

Genetic algorithm applied to reactive power optimi- zation of large scale power networks has some problems,such as high searching space and time consuming.So an improved ge- netic algorithm combining sensitivity analysis (IGACSA) is in- truduced.The crossover and mutation of SGA are improved in IGACSA.The improved crossover give fast local adjustment for this-algorithm,and the improved mutation combined sensitivity analysis is used to generate new individuals.Furthermore,the results of this algorithm are mini-adjusted by sensitivity analysis. In order to apply IGACSA in determinating the capacity of new installed reactive-power compensation,two simple steps are a- dopted.The analysis of practical power network show the pro- posed algorithm can decrease the calculating time and obtain bet- ter optimization results.