中国电机工程学报Issue(4)：672-677,6.DOI:10.13334/j.0258-8013.pcsee.2014.04.020
云理论在配电网络变电站选址定容中的应用
Application of Cloud Theory to Optimal Planning of Substation Locating and Sizing
摘要
Abstract
The optimizational planning of locating and sizing for a distribution substation is a large combinatorial optimization problem. To solve this problem, a cloud theory optimization algorithm (CTOA) is proposed based on the outstanding characteristics of cloud in transforming a qualitative concept to a set of quantitative numerical values, and in combination with the principle of “survival of the fittest” of the genetic algorithm. The core thought of the algorithm is that the hereditary characteristic of the parent individual first is represented by the expected value of cloud, then the degree of the heredity and mutation is controlled by entropy and hyper-entropy of the cloud, and transformation between the qualitative concepts and their quantitative expression is realized by normal cloud, so as to realize the reproduction. With the instructions of the qualitative knowledge,the extent of the searching space is self-adjusted, and the possibility of prematurity and the probability of trapping in local best optimization are greatly reduced. The proposed CTOA is tested by a realistic planning project to verify the effectiveness and feasibility. The calculation speed and search accuracy of CTOA are obviously superior to those of the improved adaptive genetic algorithm (IAGA) and refined multi-team particle swarm optimization algorithm (RMPSO), without the process of coding and crossover and easy implementation. The method proposed has a promising application in large-scale practical problems.关键词
变电站选址定容/云理论/定性定量转换/遗传算法Key words
substation locating and sizing/cloud theory/transforming between qualitative concepts and their quantitative expression/genetic algorithm分类
信息技术与安全科学引用本文复制引用
李燕青,谢庆,王岭,律方成..云理论在配电网络变电站选址定容中的应用[J].中国电机工程学报,2014,(4):672-677,6.基金项目
国家高技术研究发展计划(863计划)(2011AA05A121) （863计划）
河北省自然科学基金(E2010001703)。@@@@The National High Technology Research and Development Program of China(863 Program)(2011AA05A121) （E2010001703）
Project Supported by the Natural Science Foundation of Hebei Province(E2010001703) （E2010001703）
