基于遗传禁忌混合算法的电力系统无功优化

为了使遗传算法(GA)和禁忌搜索算法(TS)的优点被保持,缺点被削弱,提出了电力系统无功优化的遗传禁忌混合算法(GATS);针对电力系统无功优化中控制变量的离散性和连续性相混合的特点,提出了混合编码策略并相应地采用启发式算术进行杂交.用GATS算法对IEEE30节点系统进行了无功优化计算,并就优化结果和简单遗传算法(SGA)及二进制编码的禁忌搜索法(TSB)的优化结果进行了比较,结果表明GATS方法具有更好的收敛性和更强的全局寻优能力.

REACTIVE POWER OPTIMIZATION BASED ON GENETIC/TABU SEARCH HYBRID ALGORITHM

To remain the advantages of Genetic Algorithm (GA) and Tabu Search (TS) and weaken their defects, a genetic/Tabu search hybrid algorithm for reactive power optimization is presented. According to the feature that in the reactive power optimization the discreteness of control variables are mixed with their continuity, a hybrid coding strategy is put forward and correspondingly the crossing is performed by heuristic algorithm. Using the presented algorithm the reactive power optimization calculation for IEEE 30-bus system is conducted, the calculation result is compared with the results from the simple genetic algorithm and the binary coded Tabu search, and it is shown that the presented method possesses bettter convergence and stronger global optimization ability.