改进的云粒子群优化算法及其断路器优化应用
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摘要
针对断路器的能耗优化设计中复杂的多维参数设定常采用经验选取的方式,易导致断路器自身能耗过大。为此,提出一种结合鲶鱼效应与云模型的改进粒子群优化算法对其多维相关参数进行优化选取,先将传统的粒子群优化算法与云模型相结合,对多维寻优粒子加以分类,控制不同粒子群在不同搜索状态下的快速寻优;再引入鲶鱼效应扰动机制增加寻优粒子多样性,提高寻优精度;最后采用该改进算法对断路器能耗模型优化仿真以及断路器参数进行设定。结果表明,提出的改进方法可以实现断路器低能耗设计要求,并能有效提高其设计效率。
In the design of the energy consumption of the circuit breaker,the complex multi-dimensional parameters setting is often used in the way of empirical selection,and therefore the circuit breaker produced more energy consumption of itself. Accordingly,this paper proposed an improved particle swarm optimization algorithm combining catfish effect and cloud model(CE-CPSO) to select its multidimensional relative parameters. First,the traditional particle swarm optimization algorithm combined the cloud model to classify the multidimensional optimal particles and controled different particle swarms in different search states to search the best fitness quickly; and then this paper introduced a catfish effect disturbance mechanism to increase the diversity of the optimized particles and improved the search precision. Finally,it applied the improved algorithm to optimize the energy consumption and the parameters of the circuit breakers. The results show that the improved method can realize the design requirement of low energy consumption of circuit breaker and improve its design efficiency.
In the design of the energy consumption of the circuit breaker,the complex multi-dimensional parameters setting is often used in the way of empirical selection,and therefore the circuit breaker produced more energy consumption of itself. Accordingly,this paper proposed an improved particle swarm optimization algorithm combining catfish effect and cloud model(CE-CPSO) to select its multidimensional relative parameters. First,the traditional particle swarm optimization algorithm combined the cloud model to classify the multidimensional optimal particles and controled different particle swarms in different search states to search the best fitness quickly; and then this paper introduced a catfish effect disturbance mechanism to increase the diversity of the optimized particles and improved the search precision. Finally,it applied the improved algorithm to optimize the energy consumption and the parameters of the circuit breakers. The results show that the improved method can realize the design requirement of low energy consumption of circuit breaker and improve its design efficiency.
引文
[1]Wang Jibo,Rong Hailiang,Dong Yanzong.Multi-protocol communications for intelligent low-voltage circuit breaker[C]//Proc of International Conference on Information Science.Piscataway,NJ:IEEE Press,2014:2104-2107.
[2]杨洋,肖湘宁,王昊,等.电力系统数字混合仿真技术综述与展望[J].电力自动化设备,2017,37(3):203-223.
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[5]史丽萍,汤家升,王攀攀,等.采用最优小波数和改进BP神经网络的感应电动机定子故障诊断[J].电工技术学报,2015,30(24):38-46.
[6]陈赛赛,李可军,王卓迪,等.新型混合SVC模型及其基于人工免疫算法的控制策略[J].电力自动化设备,2016,36(4):93-99.
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[8]马英钧,孙晓娜,赵东方.基于改进粒子群优化算法的航班降落调度问题研究[J].计算机应用研究,2015,32(7):2035-2038.
[9]张艳梅,姜淑娟,陈若玉,等.基于粒子群优化算法的类集成测试序列确定方法[J].计算机学报,2018,49(4):931-945.
[10]张栋华,李征,蔡旭.基于量子行为粒子群优化算法的微电网优化配置[J].计算机仿真,2014,31(8):120-124.
[11]李俊,汪冲,李波,等.基于扰动的精英反向学习粒子群优化算法[J].计算机应用研究,2016,33(9):2584-2591.
[12]李文,伍铁斌,赵全友,等.改进的混沌粒子群算法在TSP中的应用[J].计算机应用研究,2015,32(7):2065-2067.
[13]邱飞岳,莫雷平,江波,等.基于大规模变量分解的多目标粒子群优化算法研究[J].计算机学报,2016,39(12):2598-2613.
[14]张艳琼.改进的云自适应粒子群优化算法[J].计算机应用研究,2010,27(9):3250-3252.
[15]李德毅,刘常昱.论正态云模型的普适性[J].中国工程科学,2004,6(8):28-34.
[16]李国栋,胡建平,夏克文.基于云PSO的RVM入侵检测[J].控制与决策,2015,30(4):698-702.
[17]王生生,杨娟娟,柴胜.基于混沌鲶鱼效应的人工蜂群算法及应用[J].电子学报,2014,42(9):1731-1737.
[18]刘艺,刁兴春,曹建军,等.求解子集问题的鲶鱼效应蝙蝠蚁群优化[J].系统工程与电子技术,2016,38(10):2441-2448.
[19]王志刚,基于动态搜索策略的人工蜂群算法[J].计算机工程与科学,2015,37(4):734-739.
[20]李俊,汪冲,李波,等.基于多策略协同作用的粒子群优化算法[J].计算机应用,2016,36(3):681-686.
[21]黄泽霞,俞攸红,黄德才.惯性权自适应调整的量子粒子群优化算法[J].上海交通大学学报,2012,46(2):228-232.
[22]张朝龙,余春日,江善和,等.基于自适应云粒子群算法的Wiener模型辨识[J].计算机应用研究,2012,29(11):4041-4049.
[23]戴水东.万能式断路器能量损耗的分析[J].电工电气,2009(9):40-41.
[2]杨洋,肖湘宁,王昊,等.电力系统数字混合仿真技术综述与展望[J].电力自动化设备,2017,37(3):203-223.
[3]李兴文.低压断路器研发新技术综述[J].电器与能效管理技术,2015(9):1-14.
[4]陈德桂.低压电器仿真与数字化设计技术的进展[J].电器与能效管理技术,2014(15):1-23.
[5]史丽萍,汤家升,王攀攀,等.采用最优小波数和改进BP神经网络的感应电动机定子故障诊断[J].电工技术学报,2015,30(24):38-46.
[6]陈赛赛,李可军,王卓迪,等.新型混合SVC模型及其基于人工免疫算法的控制策略[J].电力自动化设备,2016,36(4):93-99.
[7]王皓,欧阳海滨,高立群.一种改进的全局粒子群优化算法[J].控制与决策,2016,31(7):1161-1168.
[8]马英钧,孙晓娜,赵东方.基于改进粒子群优化算法的航班降落调度问题研究[J].计算机应用研究,2015,32(7):2035-2038.
[9]张艳梅,姜淑娟,陈若玉,等.基于粒子群优化算法的类集成测试序列确定方法[J].计算机学报,2018,49(4):931-945.
[10]张栋华,李征,蔡旭.基于量子行为粒子群优化算法的微电网优化配置[J].计算机仿真,2014,31(8):120-124.
[11]李俊,汪冲,李波,等.基于扰动的精英反向学习粒子群优化算法[J].计算机应用研究,2016,33(9):2584-2591.
[12]李文,伍铁斌,赵全友,等.改进的混沌粒子群算法在TSP中的应用[J].计算机应用研究,2015,32(7):2065-2067.
[13]邱飞岳,莫雷平,江波,等.基于大规模变量分解的多目标粒子群优化算法研究[J].计算机学报,2016,39(12):2598-2613.
[14]张艳琼.改进的云自适应粒子群优化算法[J].计算机应用研究,2010,27(9):3250-3252.
[15]李德毅,刘常昱.论正态云模型的普适性[J].中国工程科学,2004,6(8):28-34.
[16]李国栋,胡建平,夏克文.基于云PSO的RVM入侵检测[J].控制与决策,2015,30(4):698-702.
[17]王生生,杨娟娟,柴胜.基于混沌鲶鱼效应的人工蜂群算法及应用[J].电子学报,2014,42(9):1731-1737.
[18]刘艺,刁兴春,曹建军,等.求解子集问题的鲶鱼效应蝙蝠蚁群优化[J].系统工程与电子技术,2016,38(10):2441-2448.
[19]王志刚,基于动态搜索策略的人工蜂群算法[J].计算机工程与科学,2015,37(4):734-739.
[20]李俊,汪冲,李波,等.基于多策略协同作用的粒子群优化算法[J].计算机应用,2016,36(3):681-686.
[21]黄泽霞,俞攸红,黄德才.惯性权自适应调整的量子粒子群优化算法[J].上海交通大学学报,2012,46(2):228-232.
[22]张朝龙,余春日,江善和,等.基于自适应云粒子群算法的Wiener模型辨识[J].计算机应用研究,2012,29(11):4041-4049.
[23]戴水东.万能式断路器能量损耗的分析[J].电工电气,2009(9):40-41.