宋坤隆;谢云云;殷明慧;邹云;周前;汪成根;

• 1:南京理工大学自动化学院
• 2:江苏省电力公司电力科学研究院

摘要(Abstract)：

在停电系统恢复路径优化中,智能算法因具有较好的寻优性能而得到广泛的应用。以线路状态为编码的恢复路径智能优化方法,由于迭代过程中的随机性,新生成个体中非连通个体比重非常大,影响了寻优效率。对此,已有研究提出了对非连通个体进行修正的思路和方法,但新增连通性修正环节也会增加每一步迭代的运算时间,而加快该环节将有助于提高恢复路径优化的寻优速度。为此,考虑到在迭代过程中新生成个体的投运线路数量和平均非连通区域数较少的特点,提出了基于凝聚层次聚类法和Prim算法的连通性修正算法,使非连通个体更快速修正为连通个体,在保证寻优结果精度的基础上提高智能算法计算速度。最后,以遗传算法为例,IEEE标准系统的多组仿真算例验证了所提方法的有效性。

关键词(KeyWords)： 电力系统恢复;路径优化;连通子图;连通性修正

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金资助项目(51507080,61673213);; 江苏省博士后基金资助项目(1402042C)~~

作者(Author): 宋坤隆;谢云云;殷明慧;邹云;周前;汪成根;

Email:

DOI:

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