Service Restoration Reconfiguration in Distribution Systems Based on Pareto Optimizing Path NSGA-Ⅱ

doi:10.11985/2018.06.005

Abstract

Abstract:

Fast non-dominated sorting genetic algorithm (NSGA-Ⅱ) can obtain uniformly distributed individualities in the every evolutionary process and has more convergence and more excellent robustness. In this paper, the defects of the current genetic algorithm (GA) are analyzed, then NSGA-Ⅱ is applied to service restoration reconfiguration in distribution systems, and a method of controlling the selecting operation by the Pareto optimizing path (POP) is provided. On one hand this method can expand the region of searching and avoid to get into local optimum, on the other it can stop evolving duly and reduce the unnecessary calculation. In addition, the final service restoration reconfiguration method is confirmed based on the fuzzy set theory. The simulation results show that POP NSGA-Ⅱ have more ability of optimizing than GA and NSGA-Ⅱ. It should be a new method for service restoration reconfiguration in distribution systems.

Key words: NSGA-Ⅱ, service restoration reconfiguration in distribution systems, POP, fuzzy set theory, POP NSGA-Ⅱ

CLC Number:

TM743

Zhou Shenghai,Yin Hang,Gu Yin,Wang Chong,Bai Feng. Service Restoration Reconfiguration in Distribution Systems Based on Pareto Optimizing Path NSGA-Ⅱ[J]. Journal of Electrical Engineering, 2018, 13(6): 28-35.

Figures/Tables 8

Fig.1

Tab.1

Fig.2

Fig.3

Tab.2

Tab.3

Fig.4

Tab.4

References 26

[1]	张浩, 和敬涵, 尹航 , 等. 电网孤岛重构的云计算策略[J]. 中国电机工程学报, 2011,31(34):77-84.
	Zhang Hao, He Jinghan, Yin Hang , et al. Power grid islands service restoration based on cloud computing[J]. Proceedings of the CSEE, 2011,31(34):77-84.
[2]	张浩, 和敬涵, 薄志谦 , 等. 基于动态规划算法的故障恢复重构[J]. 电工技术学报, 2011,26(12):162-167.
	Zhang Hao, He Jinghan, Bo Zhiqian , et al. Service restoration based on dynamic programming[J]. Transactions of China Electrotechnical Society, 2011,26(12):162-167.
[3]	Lim S I, Lee S J, Choi M S , et al. Service restoration methodology for multiple fault case in distribution systems[J]. IEEE Transactions on Power Systems, 2006,21(4):1638-1644. doi: 10.1109/TPWRS.2006.879275
[4]	Manjunath K, Mohan M R . A new hybrid multi-objective quick service restoration technique for electric power distribution systems[J]. International Journal of Electrical Power & Energy Systems, 2007,29:51-64. doi: 10.1016/j.ijepes.2005.12.012
[5]	Huang C M . Multi-objective service restoration of distribution systems using fuzzy cause-effect networks[J]. IEEE Transactions on Power Systems, 2003,18(2):867-874. doi: 10.1109/TPWRS.2003.811003
[6]	陈竟成, 徐德超, 于尔铿 , 等. 配电网故障恢复系统[J]. 电力系统自动化, 2000,24(4):46-51.
	Chen Jingcheng, Xu Dechao, Yu Erkeng , et al. Distribution fault detection, isolation and restoration system in a distribution management system[J]. Automation of Electric Power Systems, 2000,24(4):46-51.
[7]	Raju G K V, Bijwe P R . An efficient algorithm for minimum loss reconfiguration of distribution system based on sensitivity and heuristics[J]. IEEE Transactions on Power Systems, 2008,23(3):1401-1407. doi: 10.1109/TPWRS.2008.926702
[8]	Shirmohammadi D, Hong H W . Reconfiguration of electric distribution networks for resistive line losses reduction[J]. IEEE Transactions on Power Delivery, 1989,4(2):1492-1498. doi: 10.1109/61.25637
[9]	陈根军, 李继洸, 唐国庆 . 基于Tabu搜索的配电网络重构算法[J]. 中国电机工程学报, 2002,22(10):28-33.
	Chen Genjun, Li Jiguang, Tang Guoqing . A tabu search approach to distribution network reconfiguration for loss reduction[J]. Proceedings of the CSEE, 2002,22(10):28-33.
[10]	徐廷炜, 贾嵘 . 基于人工免疫思想的蚁群算法(AIACS)在配电网重构中的应用[J]. 电力系统保护与控制, 2010,38(18):89-93. doi: 10.7667/j.issn.1674-3415.2010.18.018
	Xu Yanwei, Jia Rong . Application of artificial immune theory-based ant colony system (AIACS) in reconfiguration of distribution networks[J]. Power System Protection and Control, 2010,38(18):89-93. doi: 10.7667/j.issn.1674-3415.2010.18.018
[11]	于永哲, 黄家栋 . 基于改进模拟植物生长法的配电网络重构[J]. 电力系统保护与控制, 2010,38(2):40-43. doi: 10.7667/j.issn.1674-3415.2010.02.009
	Yu Yongzhe, Huang Jiadong . Reconfiguration of distribution network based on improved plant growth simulation algorithm[J]. Power System Protection and Control, 2010,38(2):40-43. doi: 10.7667/j.issn.1674-3415.2010.02.009
[12]	李振坤, 陈星莺, 余昆 , 等. 配电网重构的混合粒子群算法[J]. 中国电机工程学报, 2008,28(31):35-41.
	Li Zhenkun, Chen Xingying, Yu Kun , et al. Hybrid particle swarm optimization for distribution network reconfiguration[J]. Proceedings of the CSEE, 2008,28(31):35-41.
[13]	黄弦超, 舒隽, 张粒子 , 等. 免疫禁忌混合智能优化算法在配电网检修优化中的应用[J]. 中国电机工程学报, 2004,24(11):96-100.
	Huang Xianchao, Shu Jun, Zhang Lizi , et al. Distribution maintenance scheduling using an intelligent optimal approach mixed with immune algorithm and tabu search[J]. Proceedings of the CSEE, 2004,24(11):96-100.
[14]	唐斌, 罗安, 王击 . 改进遗传算法的编码策略及其在配电网重构中的应用[J]. 继电器, 2004,32(13):35-39.
	Tang Bin, Luo An, Wang Ji . The improved encoding strategy of genetic algorithm and its application in reconfiguration of distribution networks[J]. Relay, 2004,32(13):35-39.
[15]	邹必昌, 龚庆武, 李勋 . 基于负荷平衡的配电网重构遗传算法研究[J]. 电力系统保护与控制, 2011,39(6):80-93. doi: 10.7667/j.issn.1674-3415.2011.06.016
	Zou Bichang, Gong Qingwu, Li Xun . Research on network reconfiguration GA in distribution system based on load balancing[J]. Power System Protection and Control, 2011,39(6):80-93. doi: 10.7667/j.issn.1674-3415.2011.06.016
[16]	王韶, 马晶晶, 周鑫 , 等. 配电网重构的蜜蜂型遗传算法[J]. 电力系统保护与控制, 2010,38(16):62-67. doi: 10.7667/j.issn.1674-3415.2010.16.012
	Wang Shao, Ma Jingjing, Zhou Xin , et al. Bee evolution genetic algorithm for distribution network reconfiguration[J]. Power System Protection and control, 2010,38(16):62-67. doi: 10.7667/j.issn.1674-3415.2010.16.012
[17]	周辉, 王击, 罗安 , 等. 克隆遗传算法与模拟退火算法相结合的配电网重构[J]. 继电器, 2007,35(7):41-45.
	Zhou Hui, Wang Ji, Luo An , et al. Distribution network reconstruction based on the combination of CGA and SA[J]. Relay, 2007,35(7):41-45.
[18]	王林川, 梁栋, 于冬皓 , 等. 基于遗传和禁忌搜索混合算法的配电网重构[J]. 电力系统保护与控制, 2009,37(6):27-31. doi: 10.7667/j.issn.1674-3415.2009.06.006
	Wang Linchuan, Liang Dong, Yu Donghao , et al. Distribution network reconfiguration based on genetic/tabu search hybrid algorithm[J]. Power System Protection and Control, 2009,37(6):27-31. doi: 10.7667/j.issn.1674-3415.2009.06.006
[19]	Sbalzarini I F, Muller S, Koumoutsakos P. Multiobjective optimization using evolutionary algorithms[C]. Center for Turbulence Research, Proceedings of the Summer Program, 2000: 63-74.
[20]	Srinivas N, Deb K . Multiobjective function optimization using nondominated sorting genetic algorithms[J]. Evol. Comput., 1995,2(3):221-248. doi: 10.1162/evco.1994.2.3.221
[21]	Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal , et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002,6(2):182-197. doi: 10.1109/4235.996017
[22]	黄弦超 . 含分布式电源的配电网故障恢复模型[J]. 电力系统保护与控制, 2011,39(19):52-57. doi: 10.7667/j.issn.1674-3415.2011.19.008
	Huang Xianchao . Model of service restoration of distribution systems with distributed generation[J]. Power System Protection and Control, 2011,39(19):52-57. doi: 10.7667/j.issn.1674-3415.2011.19.008
[23]	冯士刚, 艾芊 . 带精英策略的快速非支配排序遗传算法在多目标无功优化中的应用[J]. 电工技术学报, 2007,22(12):146-151.
	Feng Shigang, Ai Qian . Application of fast and elitist non-dominated sorting generic algorithm in multi-objective reactive power optimization[J]. Transactions of China Electrotechnical Society, 2007,22(12):146-151.
[24]	王秀丽, 李淑慧, 陈皓勇 , 等. 基于非支配遗传算法及协同进化算法的多目标多区域电网规划[J]. 中国电机工程学报, 2006,26(16):11-15.
	Wang Xiuli, Li Shuhui, Chen Haoyong , et al. Multi-objective and multi-district transmission planning based on NSGA-II and cooperative co-evolutionary algorithm[J]. Proceedings of the CSEE, 2006,26(16):11-15.
[25]	陈水利, 李敬功, 王向公 . 模糊集理论及其应用[M]. 北京: 科学出版社, 2005.
[26]	刘健, 毕鹏翔, 董海鹏 . 复杂配电网简化分析与优化[M]. 北京: 中国电力出版社, 2002.

权重分配	0.8/0.1/0.1	0.7/0.2/0.1	0.6/0.2/0.20	0.5/0.2/0.3
f₁/kW	681.493 9	668.387 6	630.834 2	586.741 2
f₂/kvar	431.234 4	422.587 4	411.685 3	388.286 4
f₃/次	6.57	9.33	8.75	8.15
时间/s	6.587 8	6.674 8	6.536 6	6.672 7
稳定性(%)	81	76	69	65

算法	NSGA-Ⅱ	POP NSGA-Ⅱ	TSCGA (0.8/0.1/0.1)
f₁/kW	695.8	701.5	681.493 9
f₂/kvar	460.1	469.6	431.234 4
f₃/次	6.994	6.369	9.576
时间/s	6.753 4	6.286 4	6.287 8
迭代次数	68.668	59.482	78.659
F₁中个体数	8.159	11.615	-
稳定性(%)	91	99	81

算法	NSGA-Ⅱ	POP NSGA-Ⅱ
F₁中最优解个数	9	14
最终方案动作开关	1, 2, 4, 5, 15, 21, 24	1, 2, 4, 12, 21, 24
恢复重构效果	f₁= 695.8kW	f₁= 703.8kW
	f₂= 478.6kvar	f₂= 484.1kvar
	f₃= 7	f₃= 6