基于混合搜索算法的配电网最优无功补偿

针对目前无功优化算法所存在的问题,提出一种综合三种智能优化方法优点于一身的混合搜索算法。该方法在自适应遗传算法基础上,加入二次邻域变异过程,加快遗传进化速度,引导个体向符合无功优化问题的实际方向发展。然后运用模拟退火进行个体更新,以便增加群的多样性;最后将所得最优解作为禁忌搜索的初始解,进行局部寻优求解过程。以IEEE28-bus系统和一实际配电网系统为例进行优化计算,结果表明混合搜索算法具有较优的性能和求解精度。

A combined algorithm for reactive power optimization of distribution network

The simple genetic algorithm is constrained by its poor converging performance and readily leads to local optimization.So in this paper,a combined GA,which can retain the advantages of GA,simulated annealing(SA) and tabu search(TS),is introduced.At first,according to the value of individual fitness the adaptive crossover and adaptive mutation are performed,and heuristic border upon mutation can speed up the anagenesis.Thus the diversity of group is increased and the local optimum can be avoided by simulated annealing.Then the tabu search with a specifical probability is used to improve the local search capacity.To improve the computation speed and accuracy,decimal integer encoding and reserving the elitist are adopted.One real system is used to test the performance of the combined genetic algorithm.Test results show that the hybrid genetic algorithm possesses following advantages: good computation speed and global convergence and high calculation accuracy.