Abstract：In the traditional way to design the energy storage spring of the circuit breaker the method of experience trial calculation is mainly adopted, which may easily lead to unreasonable parameters of the spring structure, large volume of circuit breaker and poor breaking performance. Therefore, An improved cloud particle swarm optimization algorithm combined with catfish effect was applied to optimize the parameters of energy storage spring of circuit breaker. Firstly, according to the working principle of energy storage springs, the mathematical optimization design model of the energy storage springs and the constraints of the spring parameter design were deduced. Then, improving the algorithm based on the optimization model, on the basis of the traditional particle swarm optimization algorithm, catfish effect strategy was introduced to produce various candidate solutions, avoiding the algorithm falling into local optimal value and the optimization speed weighting factor was adjusted combined with the cloud model to speed up the convergence of the algorithm and improve the ability of global search solutions. Finally, the improved algorithm was used to simulate the optimization model of the energy storage spring of circuit breakers and calculate the corresponding spring parameters. The results show that the improved particle swarm optimization algorithm can achieve miniaturization and better breaking performance of circuit breakers.
石丽莉, 夏克文, 戴水东, 鞠文哲. 改进的粒子群优化算法对断路器储能弹簧的优化设计[J]. 计算机应用, 2019, 39(5): 1540-1546.
SHI Lili, XIA Kewen, DAI Shuidong, JU Wenzhe. Optimal design of energy storage spring in circuit breaker based on improved particle swarm optimization algorithm. Journal of Computer Applications, 2019, 39(5): 1540-1546.
GHOLIAN A, MOHSENIAN-RAD H, HUA Y B. Optimal industrial load control in smart grid[J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2305-2316.
[2]
LIU F, LIU W J, ZHA X M. Solid-state circuit breaker snubber design for transient overvoltage suppression at bus fault interruption in low-voltage DC microgrid[J]. IEEE Transactions on Power Electronics, 2017, 32(4): 3007-3021.
[3]
MAQSOOD A, OVERSTREET A, CORZINE K. Modified z-source DC circuit breaker topologies[J]. IEEE Transactions on Power Electronics, 2016, 31(10): 7394-7403.
[4]
朱童,余占清,曾嵘,等.混合式直流断路器模型及其操作暂态特性研究[J].中国电机工程学报,2016,36(1):18-30.(ZHU T, YU Z Q, ZENG R, et al. Transient model and operation characteristics researches of hybrid DC circuit breaker[J]. Proceedings of the CSEE, 2016, 36(1): 18-30.)
[5]
PEI X Z, CWIKOWSKI O, SMITH A C. Design and experimental tests of a superconducting hybrid DC circuit breaker[J]. IEEE Transactions on Applied Superconductivity, 2018, 28(3): 1-5.
[6]
田园,胡炎.计及保护和断路器动作不确定性的隐性故障检测模型[J].电网技术,2016,40(9):2896-2903.(TIAN Y, HU Y. Analytic model of hidden failure detection model considering uncertainty of protection and circuit breaker tripping[J]. Power System Technology, 2016, 40(9): 2896-2903.)
[7]
MAJI T K, ACHARJEE P. Multiple solutions of optimal pmu placement using exponential binary PSO algorithm for smart grid applications[J]. IEEE Transactions on Industry Applications, 2017, 53(3): 2550-2559.
[8]
LIU C L, WEI D, ZHANG B. On novel methods for characterizing the arc/contact movement and its relation with the current/voltage in low-voltage circuit breaker[J]. IEEE Transactions on Plasma Science, 2017, 45(5): 882-888.
[9]
李鹏飞,周文俊,曾国,等.高压断路器合闸弹簧动态特性及储能状态检测方法[J].电工技术学报,2016,31(3):104-112.(LI P F, ZHOU W J, ZENG G, et al. The dynamic characteristics and energy storage state detection method of high-voltage circuit breaker closing spring[J]. Transactions of China Electrotechnical Society, 2016, 31(3): 104-112.)
[10]
赵思洋,汪安本,周文俊,等.基于模糊综合评判的断路器操动机构弹簧储能状态评估[J].高压电器,2016,52(6):187-192.(ZHAO S Y, WANG A B, ZHOU W J, et al. State assessment of circuit breaker actuator's spring based on fuzzy comprehensive evaluation[J]. High Voltage Apparatus, 2016, 52(6): 187-192.)
[11]
BONYADI M R, MICHALEWICZ Z. Stability analysis of the particle swarm optimization without stagnation assumption[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(5): 814-819.
[12]
BANERJEE S, GHOSH A, RANA N. An improved interleaved boost converter with PSO based optimal type-Ⅲ controller[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2017, 5(1): 323-337.
[13]
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.(WANG H, OUYANG H B, GAO L Q. An improved global particle swarm optimization algorithm[J]. Control and Decision, 2016, 31(7): 1161-1168.)
[14]
JIANG S Y, YANG S X. An improved multiobjective optimization evolutionary algorithm based on decomposition for complex pareto fronts[J]. IEEE Transactions on Cybernetics, 2016, 46(2): 421-437.
[15]
张栋华,李征,蔡旭.基于量子行为粒子群优化算法的为电网优化配置[J].计算机仿真,2014,31(8):120-124,208.(ZHANG D H, LI Z, CAI X. Micro-grid optimization allocation problem based on quantum-behaved particle swarm optimization[J]. Computer Simulation, 2014, 31(8): 120-124, 208.)
[16]
周海鹏,高芹,蒋丰千,等.自适应混沌量子粒子群算法及其在WSN覆盖优化中的应用[J].计算机应用,2018,38(4):1064-1071.(ZHOU H P, GAO Q, JIANG F Q, et al. Application of self-adaptive chaotic quantum particle swarm algorithm in coverage optimization of wireless sensor network[J]. Journal of Computer Applications, 2018, 38(4): 1064-1071.)
[17]
FONG S M, WONG R, VASILAKOS A V. Accelerated PSO swarm search feature selection for data stream mining big data[J]. IEEE Transactions on Services Computing, 2016, 9(1): 33-45.
[18]
陈大鹏,张九根,梁星.基于免疫粒子群算法的中央空调冷冻水系统优化控制[J].计算机应用,2017,37(9):2717-2721.(CHEN D P, ZHANG J G, LIANG X. Optimal control of chilled water system in central air-conditioning based on artificial immune and particle swarm optimization algorithm[J]. Journal of Computer Applications, 2017,37(9): 33-45.)
[19]
范成礼,邢清华,李响,等.带反向预测及斥力因子的改进粒子群优化算法[J].控制与决策,2015,30(2):311-315.(FAN C L, XING Q H, LI X, et al. Improved particle swarm optimization algorithm with reverse forecast and repulsion[J]. Control and Decision, 2015, 30(2): 311-315.)
[20]
黄松,田娜,纪志成.基于自适应概率粒子群优化算法的研究[J].系统仿真学报,2016,28(4):874-879.(HUANG S, TIAN N, JI Z C. Study of modified particle swarm optimization algorithm based on adaptive mutation probability[J]. Journal of System Simulation, 2016, 28(4): 874-879.)
[21]
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.(LI G D, HU J P, XIA K W. Intrusion detection using relevance vector machine based on cloud particle swarm optimization[J]. Control and Decision, 2015, 30(4): 698-702.)
[22]
MASDARI M, SALEHI F, JALALI M, et al. A survey of PSO-based scheduling algorithms in cloud computing[J]. Journal of Network and Systems Management, 2017, 25(1): 122-158.
[23]
CHEN S M, CHIOU C H. Multiattribute decision making based on interval-valued intuitionistic fuzzy sets, PSO techniques, and evidential reasoning methodology[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(6): 1905-1916.
[24]
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.(QIU F Y, MO L P, JIANG B, et al. Multi-objective particle swarm optimization algorithm using large scale variable decomposition[J]. Chinese Journal of Computers, 2016, 39(12): 2598-2613.)
[25]
鞠文哲,夏克文,戴水东.改进的云粒子群优化算法及其断路器优化应用[J].计算机应用研究, 2018, 25(7):2084-2087.(JU W Z, XIA K W, DAI S D. Improved cloud particle swarm optimization algorithm and its application in circuit breaker optimization[J].Application Research of Computers, 2018, 25(7):2084-2087.)
[26]
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.(WANG S S, YANG J J, CHAI S. Artificial bee colony algorithm with chaotic catfish effect and its application[J]. Acta Electronica Sinica, 2014, 42(9): 1731-1737.)
[27]
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.(LIU Y, DIAO X C, CAO J J, et al. Catfish bat algorithm-ant colony optimization for subset problems[J]. Systems Engineering and Electronics, 2016, 38(10): 2441-2448.)
[28]
GE H W, SUN L, TAN G Z. Cooperative hierarchical PSO with two stage variable interaction reconstruction for large scale optimization[J]. IEEE Transactions on Cybernetics, 2017, 47(9): 2809-2823.
2019, Vol. 39 
