用改进遗传算法求解电力系统负荷恢复

对系统恢复过程中最后一个阶段的负荷恢复问题进行了研究.考虑系统恢复过程中负荷对电力需求优先级的不同,将电力系统的负荷恢复问题建模为多约束条件的组合优化问题,并用改进的遗传算法对问题进行求解.在选择策略中采用稳态策略、精英策略和重叠种群策略,提高了遗传算法搜索的遍历性并使算法具有群体爬山性.将各种约束条件与目标函数融合在一起,建立一种偏序关系来处理负荷恢复中的约束条件.求解的过程满足了系统的约束条件,不会出现系统的越限.算例结果表明了算法的有效性.

Modified genetic algorithm for load restoration in power systems

The load restoration at the last stage of the power system restoration was studied Considering the prior of the load's need of the power,the load restoration was modeled as a combination optimization with many constrained conditions and a modified genetic algorithm was designed to resolve it.By using the robust superposition-reproduction elitism selection strategy,the ergodicity of genetic algorithm was improved and the population hill-climbing ability was achieved.The constrained conditions and the objective functions were combined and a partial-order relation was defined to dispose the constrained conditions during the load restoration.Since the constraint conditions of the load restoration cannot be violated,the power system security will be ensured.The simulation result shows the feasibility and availability of the algorithm.