Fault reconfiguration of ship power system based on improved grey wolf optimization algorithm
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摘要:
目的 为了更好地分析和解决舰船电力系统故障重构问题，并建立以负载失电量、开关操作次数为优化目标的舰船电力系统故障重构模型，提出一种改进灰狼优化算法来对其求解。 方法 针对灰狼优化算法的不足，首先加入Tent混沌映射；其次，用余弦函数改进收敛因子，使收敛因子初期维持较大的值缓慢减小，后期提高衰减速率；然后，加入非支配排序和拥挤度计算改进决策狼选择策略；最后，将灰狼个体进行离散化处理，使其可用于舰船电力系统故障重构问题。 结果 舰船电力系统网络故障重构算例表明，在负载和发电机故障这两种情况下，所提方法得到的Pareto解集中负载失电量、重构开关操作次数与改进差分进化算法、改进粒子群算法及改进遗传算法相比均较小，并在最佳迭代次数即寻优速度上具有一定的优势。 结论 所提方法能获得更好的系统重构方案，可克服传统灰狼优化算法收敛速度慢、初始化种群多样性较差、容易陷入局部最优等问题，更好地保证舰船安全稳定的运行。 Abstract:Objectives In order to analyze and solve the problem of the fault reconfiguration of shipboard power systems (SPSs), a fault reconstruction model with the optimization objectives of load loss and switching operation times is established, and an improved grey wolf optimization (GWO) algorithm is proposed. Methods Aiming at the deficiencies of the traditional GWO algorithm, a chaotic tent map is added; a cosine function is used to improve the convergence factor so that it maintains a large value in the initial stage, then decreases slowly and increases the attenuation rate in the later stage; non-dominated ranking and congestion calculation are added to improve the decision-making grey wolf selection strategy; and the grey wolf individual is discretized so that it can be used for reconfiguration. Results The example of the network fault reconfiguration of a SPS shows that in the case of load and generator faults, the Pareto solution obtained by the proposed method is smaller than the improved differential evolution (DE) algorithm, improved particle swarm optimization (PSO) algorithm and improved genetic algorithm (GA), and has certain advantages in the optimal number of iterations; that is, its optimization speed. Conclusions The proposed method can overcome the problems of the traditional GWO algorithm such as its slow convergence speed, poor diversity of initialization population and susceptibility to falling into local optimization. This study proves that a superior power system reconfiguration scheme can be obtained to better ensure the safe and stable operation of ships. -
图 5 故障1工况下MOGWO算法的多目标函数
Figure 5. Multi-objective function value of MOGWO under fault condition 1
图 7 故障2工况下MOGWO算法的多目标函数值
Figure 7. Multi-objective function value of MOGWO under fault condition 2
表 1 系统负载工作电流及负荷等级
Table 1. Working current and load level of system load
编号 工作电流/A 负荷等级 编号 工作电流/A 负荷等级 $ {L_1} $ 70 1 $ {L_{11}} $ 225 1 $ {L_2} $ 120 3 $ {L_{12}} $ 205 3 $ {L_3} $ 200 2 $ {L_{13}} $ 110 2 $ {L_4} $ 150 3 $ {L_{14}} $ 72 3 $ {L_5} $ 160 2 $ {L_{15}} $ 87 2 $ {L_6} $ 100 1 $ {L_{16}} $ 100 1 $ {L_7} $ 80 3 $ {L_{17}} $ 205 2 $ {L_8} $ 325 1 $ {L_{18}} $ 200 3 $ {L_9} $ 185 3 $ {L_{19}} $ 165 3 $ {L_{10}} $ 44 2 $ {L_{20}} $ 30 2 
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表 2 故障1工况下几种恢复算法的结果对比
Table 2. Comparison of results of several recovery algorithms under fault condition 1
算法 最优解 最小负载失电量/A 最小开关操作数/次 $ GE{N_{{\text{best}}}} $ CMPMDE 11211111011021112111 390 5 4 EPDSDE 11211211011021112111 390 6 4 TSDE 11212112111021112111 205 6 23 MSCPSO 11211111111021111111 无 3 9 GA 11201211111022111101 无 7 7 MOGWO 11011211111001111111 205 4 3
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表 3 故障2工况下几种恢复算法的结果对比
Table 3. Comparison of results of several recovery algorithms under fault condition 2
算法 最优解 最小负载失电量/A 最小开关操作数/次 $ GE{N_{{\text{best}}}} $ CMPMDE 20200111121011121101 1 760 9 4 EPDSDE 20200111121011121101 1 760 9 5 TSDE 20202111011111111001 820 9 29 GA 20202111111111112101 无 7 无 MOGWO 20202111111111112111 475 6 3
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[1] BABAEI M, SHI J, ABDELWAHED S. A survey on fault detection, isolation, and reconfiguration methods in electric ship power systems[J]. IEEE Access, 2018, 6: 9430–9441. doi: 10.1109/ACCESS.2018.2798505 [2] 王锡淮, 李军军, 肖健梅. 求解舰船电力系统网络重构的贪婪DPSO算法[J]. 控制与决策, 2008, 23(2): 157–161. doi: 10.3321/j.issn:1001-0920.2008.02.007WANG X H, LI J J, XIAO J M. Greed DPSO algorithm for network reconfiguration of shipboard power system[J]. Control and Decision, 2008, 23(2): 157–161 (in Chinese). doi: 10.3321/j.issn:1001-0920.2008.02.007 [3] SRIVASTAVA S K, BUTLER-PURRY K L, SARMA N D R. Shipboard power restored for active duty[J]. IEEE Computer Applications in Power, 2002, 15(3): 16–23. doi: 10.1109/MCAP.2002.1018818 [4] LAN H, XIAO Y Y, ZHANG L J. Multi-agent system optimized reconfiguration of shipboard power system[J]. Journal of Marine Science and Application, 2010, 9(3): 334–339. doi: 10.1007/s11804-010-1017-2 [5] 贾利雷. 舰船电力系统网络故障重构研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.JIA L L. Research on fault reconstruction of ship power system network[D]. Harbin: Harbin Engineering University, 2018 (in Chinese). [6] 马理胜, 张均东, 任光. 基于环境Pareto支配选择差分进化算法的舰船电网重构[J]. 大连海事大学学报, 2018, 44(2): 33–38. doi: 10.16411/j.cnki.issn1006-7736.2018.02.006MA L S, ZHANG J D, REN G. Shipboard power grid reconstruction based on environment Pareto dominated selection differential evolution algorithm[J]. Journal of Dalian Maritime University, 2018, 44(2): 33–38 (in Chinese). doi: 10.16411/j.cnki.issn1006-7736.2018.02.006 [7] 马理胜, 张均东, 任光, 等. 基于混沌迁移及无参数变异差分进化算法的舰船电力系统网络重构[J]. 上海海事大学学报, 2015, 36(3): 76–81. doi: 10.13340/j.jsmu.2015.03.013MA L S, ZHANG J D, REN G, et al. Network reconfiguration of ship power system based on chaotic migration and parameterless mutation differential evolution algorithm[J]. Journal of Shanghai Maritime University, 2015, 36(3): 76–81 (in Chinese). doi: 10.13340/j.jsmu.2015.03.013 [8] 张灵杰. 船舶电力系统故障状态下的网络重构算法研究[D]. 大连: 大连海事大学, 2018.ZHANG L J. Algorithm research on network reconfiguration of ship power system under fault condition[D]. Dalian: Dalian Maritime University, 2018 (in Chinese). [9] SHARIATZADEH F, KUMAR N, SRIVASTAVA A K. Optimal control algorithms for reconfiguration of shipboard microgrid distribution system using intelligent techniques[J]. IEEE Transactions on Industry Applications, 2017, 53(1): 474–482. doi: 10.1109/TIA.2016.2601558 [10] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46–61. doi: 10.1016/j.advengsoft.2013.12.007 [11] 张晓凤, 王秀英. 灰狼优化算法研究综述[J]. 计算机科学, 2019, 46(3): 30–38. doi: 10.11896/j.issn.1002-137X.2019.03.004ZHANG X F, WANG X Y. Comprehensive review of grey wolf optimization algorithm[J]. Computer Science, 2019, 46(3): 30–38 (in Chinese). doi: 10.11896/j.issn.1002-137X.2019.03.004 [12] 龙文, 蔡绍洪, 焦建军, 等. 求解高维优化问题的混合灰狼优化算法[J]. 控制与决策, 2016, 31(11): 1991–1997. doi: 10.13195/j.kzyjc.2015.1183LONG W, CAI S H, JIAO J J, et al. Hybrid grey wolf optimization algorithm for high-dimensional optimization[J]. Control and Decision, 2016, 31(11): 1991–1997 (in Chinese). doi: 10.13195/j.kzyjc.2015.1183 [13] SAREMI S, MIRJALILI S Z, MIRJALILI S M. Evolutionary population dynamics and grey wolf optimizer[J]. Neural Computing and Applications, 2015, 26(5): 1257–1263. doi: 10.1007/s00521-014-1806-7 [14] 姜天华. 混合灰狼优化算法求解柔性作业车间调度问题[J]. 控制与决策, 2018, 33(3): 503–508. doi: 10.13195/j.kzyjc.2017.0124JIANG T H. Flexible job shop scheduling problem with hybrid grey wolf optimization algorithm[J]. Control and Decision, 2018, 33(3): 503–508 (in Chinese). doi: 10.13195/j.kzyjc.2017.0124 [15] 顾九春, 姜天华, 朱惠琦. 多目标离散灰狼优化算法求解作业车间节能调度问题[J]. 计算机集成制造系统, 2021, 27(8): 2295–2306. doi: 10.13196/j.cims.2021.08.012GU J C, JIANG T H, ZHU H Q. Energy-saving job shop scheduling problem with multi-objective discrete grey wolf optimization algorithm[J]. Computer Integrated Manufacturing Systems, 2021, 27(8): 2295–2306 (in Chinese). doi: 10.13196/j.cims.2021.08.012 [16] MIRJALILI S. How effective is the Grey Wolf optimizer in training multi-layer perceptrons[J]. Applied Intelligence, 2015, 43(1): 150–161. doi: 10.1007/s10489-014-0645-7 [17] MIRJALILI S, SAREMI S, MIRJALILI S M, et al. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization[J]. Expert Systems with Applications, 2016, 47: 106–119. doi: 10.1016/j.eswa.2015.10.039 [18] 滕志军, 吕金玲, 郭力文, 等. 一种基于Tent映射的混合灰狼优化的改进算法[J]. 哈尔滨工业大学学报, 2018, 50(11): 40–49. doi: 10.11918/j.issn.0367-6234.201806096TENG Z J, LV J L, GUO L W, et al. An improved hybrid grey wolf optimization algorithm based on Tent mapping[J]. Journal of Harbin Institute of Technology, 2018, 50(11): 40–49 (in Chinese). doi: 10.11918/j.issn.0367-6234.201806096 [19] 魏政磊, 赵辉, 李牧东, 等. 控制参数值非线性调整策略的灰狼优化算法[J]. 空军工程大学学报(自然科学版), 2016, 17(3): 68–72.WEI Z L, ZHAO H, LI M D, et al. A grey wolf optimization algorithm based on nonlinear adjustment strategy of control parameter[J]. Journal of Air Force Engineering University (Natural Science Edition), 2016, 17(3): 68–72 (in Chinese). [20] 张兰勇, 孟坤, 刘胜, 等. 基于改进双粒子群算法的舰船电力系统网络故障重构[J]. 电力系统保护与控制, 2019, 47(9): 90–96. doi: 10.7667/PSPC180605ZHANG L Y, MENG K, LIU S, et al. Reconstruction of ship power system network fault based on improved two particle swarm algorithm[J]. Power System Protection and Control, 2019, 47(9): 90–96 (in Chinese). doi: 10.7667/PSPC180605 [21] 赵云涛, 谌竟成, 李维刚. 融合自适应差分进化机制的多目标灰狼优化算法[J]. 计算机科学, 2019, 46(S2): 83–88.ZHAO Y T, CHEN J C, LI W G. Multi-objective grey wolf optimization hybrid adaptive differential evolution mechanism[J]. Computer Science, 2019, 46(S2): 83–88 (in Chinese). [22] 苏丽, 王锡淮, 肖健梅. 基于多目标优化算法的船舶微电网重构[J]. 中国舰船研究, 2020, 15(3): 169–176. doi: 10.19693/j.issn.1673-3185.01534SU L, WANG X H, XIAO J M. Ship micro-grid reconfiguration based on multiobjective optimization algorithm[J]. Chinese Journal of Ship Research, 2020, 15(3): 169–176 (in Chinese). doi: 10.19693/j.issn.1673-3185.01534 [23] 何芳, 蔡兴国. 基于改进遗传算法的舰船电力系统网络重构[J]. 电工技术学报, 2006, 21(9): 25–30. doi: 10.3321/j.issn:1000-6753.2006.09.005HE F, CAI X G. Network reconfiguration of shipboard power system based on improved genetic arithmetic[J]. Transactions of China Electrotechnical Society, 2006, 21(9): 25–30 (in Chinese). doi: 10.3321/j.issn:1000-6753.2006.09.005 -
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