基于GIS空间分析与改进粒子群算法的变电站全寿命周期成本规划  

Substation Life Cycle Cost Planning Based on the GIS Spatial Analysis and Improved PSO Algorithm

作　　者：苏海锋[1] 张建华[1] 梁志瑞[1] 张硕[1] 牛胜锁[1] 

机构地区：[1]新能源电力系统国家重点实验室(华北电力大学),河北省保定市071003

出　　处：《中国电机工程学报》2012年第16期92-99,共8页Proceedings of the CSEE

基　　金：国家自然科学基金项目(50877026)~~

摘　　要：市场环境下,变电站设计方案的全寿命周期经济性是变电站规划的重要因素之一。建立基于设备全寿命周期成本的配电网变电站选址定容新模型,该模型考虑了规划变电站上级输电网、下级配电网及变电站的初始投资、运行成本、维护成本、故障成本和废弃成本,使得规划方案在满足可靠性要求的同时达到经济性最优。利用ArcGIS的空间分析功能来处理选址定容过程中地理因素的限制,提高了规划效率。提出将k均值聚类分析与多种群随机粒子群算法相结合的改进算法对上述模型进行求解,该算法克服了传统粒子群优化算法的早熟现象,全局寻优能力得到显著提高。最后规划实例证明了所提出的模型和方法是正确和有效的,其具有很高的实用价值。The life cycle economy of the planning scheme is one of the important factors in substation locating and sizing planning in power markets, so the new model of substation locating and sizing planning based on the life cycle cost of equipment was presented. The initial investment, operation and maintenance cost, fault cost and disposal cost of the high voltage transmission lines, the lower voltage distribution networks and the substation equipment were considered. The certain reliability and the optimal economy of the planningscheme were considered in the model. The ArcGIS spatial analysis function was used to overcome the constraint of geographical factors of planning substation, and the planning efficiency was improved. The improved algorithm based on the k-mean clustering algorithm and multi-swarms stochastic particle swarm optimization （KMSPSO） algorithm was presented. The new algorithm＇s global searching capability was improved tremendously. The model and the improved algorithm are tested by a realistic planning project to verify the efficiency and feasibility, and have high practical value.

关 键 词：变电站选址定容 全寿命周期成本 均值聚类分析 随机粒子群算法 配电网络规划 

分 类 号：TM73[电气工程—电力系统及自动化]