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云理论在配电网络变电站选址定容中的应用

DOI: 10.13334/j.0258-8013.pcsee.2014.04.020, PP. 672-677

Keywords: substationlocatingandsizing,cloudtheory,基金项目:国家高技术研究发展计划(863计划)(2011AA05A121),河北省自然科学基金(E2010001703)。

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Abstract:

变电站选址与定容优化规划属于大规模组合优化问题,基于云在定性概念描述与定量数值表示之间转换过程中的优良特性,借鉴遗传算法“优胜劣汰,适者生存”的进化思想,提出一种基于云理论的优化算法(cloudtheoryoptimizationalgorithm,CTOA),并将其应用于电力系统配电网络变电站站址、站容的优化规划研究中。在该算法中,用云的期望代表父代个体的优良特征,用云的熵和超熵控制遗传和变异的程度,用正态云算子完成概念空间到数值空间的转换,产生种群,实现遗传操作。CTOA在定性知识的指导下能实现空间范围的自适应控制搜索,可以有效改善智能优化算法易陷入局部最优解和早熟收敛等问题。最后结合某装备制造基地变电站选址定容实例,分别采用改进自适应遗传算法(improvedadaptivegeneticalgorithm,IAGA)、改进多组织粒子群优化算法(refinedmulti-teamparticleswarmoptimizationalgorithm,RMPSO)和CTOA算法对其进行了优化规划研究。结果表明,CTOA在收敛时间,搜索精度性能指标方面优于IAGA、RMPSO算法,且该算法无需编码,操作流程简单,易于实现,能更好的满足配电网络中大规模变电站规划的需求。

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