基于最优效用的配电网多故障抢修任务分配策略

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (351 KB)
输出: BibTeX | EndNote (RIS)

摘要配电网多故障抢修不只是优化故障抢修顺序的问题,多小队协同抢修时,故障任务的合理分配才是抢修工作顺利、高效完成的前提。本文同时考虑抢修小队初始位置不同及负荷本身故障,定义了抢修小队偏好、抢修小队能力向量和故障任务需求向量,然后基于属性向量建立了可解决小队协作问题的效用函数模型,形成效用函数矩阵;最后结合轮盘赌选择方法得到基于最优效用的故障任务分配方案集。采用属性向量的形式量化抢修信息,可以随时改变属性向量维数,解决抢修资源以及现场故障需求的不确定性问题,实现故障任务的动态分配。仿真结果给出了10种任务分配方案,并用改进的细菌群体趋药性（BCC）算法优化得到最优方案及最佳抢修顺序,验证了所提方法的有效性和正确性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	杨丽君
	张晶
	程慧琳
	卢志刚

关键词 ：配电网, 多故障抢修, 属性向量, 效用函数, 任务分配

Abstract：To study the distribution network multi-fault rush repair problem with multiple repair squads, rational allocation of the repair tasks is the premise to complete the repair work smoothly and efficiently before the repair sequence optimization. Considering both the multiple repair squads with different initial positions and load itself failure, this paper defines such attribute vectors as the repair preference and ability vector of the repair squad and the fault demand vector, based on which the utility function model that can solve the collaboration problem is built and the utility function matrix is formed. Then via the roulette wheel selection method, the task allocation scheme set based on optimum utility is obtained. By quantizing the repair information in the form of the attribute vector and changing the attribute vector dimension, the uncertainty problem of the repair resources and fault demand can be resolved and realizing the dynamic allocation. Ten allocation schemes are given in the simulation result and the optimal one is obtained via the improved bacterial colony chemotaxis(BCC) algorithm, together with its corresponding optimal repair sequence, which proved the effectiveness and correctness of the proposed method.

Key words： Distribution network multi-faults rush repair attribute vector utility function task allocation

收稿日期: 2013-06-21 出版日期: 2014-11-05

PACS:

TM727

基金资助:国家自然科学基金（61071201）和秦皇岛市科学技术研究与发展计划（201302A013）资助项目

作者简介: 杨丽君女,1972年生,博士,副教授,主要从事电力系统恢复控制方面的研究。张晶女,1988年生,硕士研究生,研究方向为电力系统故障抢修、恢复等。

引用本文:

杨丽君, 张晶, 程慧琳, 卢志刚. 基于最优效用的配电网多故障抢修任务分配策略[J]. 电工技术学报, 2014, 29(6): 263-270. Yang Lijun , Zhang Jing , Cheng Huilin, Lu Zhigang. A Task Allocation Strategy of Multi-fault Rush Repair for Distribution Network Based on Optimum Utility. Transactions of China Electrotechnical Society, 2014, 29(6): 263-270.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2014/V29/I6/263

[1] Watanab, I Kurihara, I Nakachi, Y. A hybrid genetic algorithm for service restoration problems in power distribution systems[C]. IEEE Congress on Evolutionary Computation, 2006: 3250-3257.
[2] 翁蓝天, 刘开培, 刘晓莉, 等. 复杂配电网故障定位的链表法[J]. 电工技术学报, 2009, 24(5): 190-196. Weng Lantian, Liu Kaipei, Liu Xiaoli, et al. Chain table algorithm for fault location of complicated distribution network[J]. Transactions of China Electrotechnical Society, 2009, 24(5): 190-196.
[3] 梅念, 石东源, 李银红, 等. 计及信息畸变影响的电网故障诊断分级优化方法[J]. 电工技术学报, 2009, 24(9): 178-185. Mei Nian, Shi Dongyuan, Li Yinhong, et al. Power system fault diagnosis based on gradual optimization in consideration of alarm information aberrance[J]. Transactions of China Electrotechnical Society, 2009, 24(9): 178-185.
[4] 黄靖, 张晓锋, 陈雁, 等. 船舶综合电力系统多目标故障恢复模型及应用[J]. 电工技术学报, 2010, 25(3): 130-137. Huang Jing, Zhang Xiaofeng, Chen Yan, et al. Multi-objective optimal model of service restoration for integrated ship power system and its application[J]. Transactions of China Electrotechnical Society, 2010, 25(3): 130-137.
[5] 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.
[6] Zhang Shaomin, Wang Baoyi, Shi Zhancheng. Improved diffluence algorithm and its application to the best repairing path for distribution network[J]. IEEE International Conference on Industrial Technology, 2008: 1-4.
[7] 陈英伟. 电力线路GIS及线路最佳抢修路径的研究: [D]. 保定: 华北电力大学, 2009.
[8] 张晶伟, 张粒子, 黄弦超. 基于遗传拓扑混合算法的配电网多故障抢修策略[J]. 电力系统自动化, 2008, 32(22): 32-35. Zhang Jingwei, Zhang Lizi, Huang Xianchao. A multi-fault rush repair strategy for distribution network based on genetic-topology algorithm[J]. Automation of Electic Power Systems, 2008, 32(22): 32-35.
[9] 卢志刚, 孙波, 刘照拯, 等. 基于改进离散BCC算法的配电网灾后抢修策略[J]. 电力系统自动化, 2011, 35(11): 55-59. Lu Zhigang, Sun Bo, Liu Zhaozheng, et al. A rush repair strategy for distribution networks based on improved discrete BCC after disaster[J]. Automation of Electic Power Systems, 2011, 35(11): 55-59.
[10] 刘照拯. 基于多代理的配电网多故障分阶段抢修策略研究: [D]. 秦皇岛: 燕山大学, 2012.
[11] 卢志刚, 王克胜. 基于多代理方法的配电网故障应急抢修调度[J]. 电网技术, 2013, 37(1): 137-143. Lu Zhigang, Wang Kesheng. Multi-agent based rush repair scheduling for distribution networks[J]. Power System Technology, 2013, 37(1): 137-143.
[12] 李春杰, 程艳从, 赵会茹．基于效用函数的电力普遍服务综合效用评价[J]. 电力系统自动化, 2012, 36(22): 50-54. Li Chunjie, Cheng Yancong, Zhao Huiru. Compr- ehensive utility evaluation of electric power universal service based on utility function[J]. Automation of Electic Power Systems, 2012, 36(22): 50-54.
[13] Drews F, Welch L, Juedes D, et al. Utility-function based resource allocation for adaptable applications in dynamic, distributed real-time systems[C]. Proceedings of IEEE 18th International Parallel and Distributed Processing Symposium, 2004.
[14] Jung Lim Koo, Byung Lim Koo, Yong Hyun Shin. An optimal investment, consumption, leisure, and voluntary retirement problem with Cobb-Douglas utility: Dynamic programming approaches[J]. Applied Mathematics Letters, 2013, 26: 481-486.
[15] Mark Hoogendoorn, Maria Gini. Preferences of agents in decentralized task allocation[J]. AI Communications, 2009, 22: 143-152.
[16] Vancza J, Markus A. An agent model for incentive based production scheduling[J]. Computers in Industry, 2000, 43(2): 173-187.
[17] Zapata C J , Silva S C, Gonzalez H I, et al. Modeling the repair process of a power distribution system[C]. Transmission and Distribution Conference and Exposition, IEEE/PES 2008: 1-7.
[18] Archie C, Rosa Anna Micillo, Ramachandra Koat, et al. Decentralised dynamic task allocation using overlapping potential games[J]. Computer Journal, 2010, 53(9): 1462-1477.
[19] Neeraj Gupta, Rajiv Shekhar, Prem Kumar Kalra. Congestion management based roulette wheel simulation for optimal capacity selection: Probabilistic transmission expansion planning[J]. Electrical Power and Energy Systems, 2012, 43: 1259-1266.
[20] Sibylle D Müller, Jarno Marchetto, Stefano Airaghi, et al. Optimization based on bacterial chemotaxis[J]. IEEE Transactoins on Evolutionary Computation, 2002, 6(1): 16-29.