摘要
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.
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 restora- tion cannot be violated, the power system security will be ensured. The simulation result shows the feasibility and availability of the algorithm.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第7期102-104,共3页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(60573168)
关键词
电力系统恢复
改进遗传算法
组合优化
负荷恢复
power system restoration
modified genetic algorithm
combinational optimization
load restoration
