摘要
针对以往牵引供电系统的设计过于依赖专业人员的经验及难以全局寻优的问题,并且为满足牵引供电系统精细化设计的要求,采用车—网耦合系统模型和负荷潮流分布计算仿真的方法,以主要设计原则为约束条件、以全线总容量和平均有功功率损耗最小为目标,构建牵引供电系统多目标优化模型;为提高模型求解过程中的全局搜索能力和收敛速度,设计基于Pareto熵的混沌多目标粒子群优化算法;应用模糊隶属度函数计算Pareto解集中各个目标函数对应的满意度值,以确定最终的系统优化方案。以某高速铁路牵引供电系统设计为例,用给出的模型及算法进行系统的优化设计,并与用ELBAS/WEBANET软件和传统的供电计算方法得到的系统优化方案比较,验证了模型和算法的正确性和可行性。
The conventional design of traction power supply system heavily relies on the professional experience of designers and global optimization is difficult to be achieved. Train-network coupling system model and load flow distribution simulation method are used to meet the requirements for the fine design of traction power supply system and a multi-objective optimization model of traction power supply system is built, in which the main design principles are used as constraints and two objective functions are the mini- mum total power supply capacity of whole line and minimum average active power loss. In order to improve the global search ability and convergence speed in solving process, a multi-objective chaotic particle swarm optimization algorithm based on Pareto entropy is designed. In addition, fuzzy membership function is used for calculating the satisfaction degree corresponding to each objective function in Pareto solution set to a- chieve the final optimal scheme. Taking the design of the power supply system for certain high speed rail- way as an example, the proposed model and algorithm are used for system optimization. The optimization scheme is compared with those obtained from traditional calculation method and ELBAS/WEBANET soft- ware, which has verified the correctness and feasibility of the proposed method.
出处
《中国铁道科学》
EI
CAS
CSCD
北大核心
2016年第1期85-92,共8页
China Railway Science
基金
国家自然科学基金资助项目(51307143
51307142)
中国铁路总公司科技研究开发计划项目(2014J009-B)
教育部中央高校基本科研业务费专项资金资助项目(2682013CX019)
关键词
牵引供电系统
优化设计
多目标粒子群优化算法
混沌算法
Pareto熵
Traction power supply system
Optimal design
Multi-objective particle swarm optimization
Chaos algorithm
Pareto entropy
