基于八种智能算法的配电网络重构计算平台及其性能

doi:10.11930.2015.8.53

摘要/Abstract

摘要： 采用8种基于群体的智能算法构建了一个求解配电网重构问题的计算平台，以期寻求到一个适合于求解该问题的智能算法。平台中不同算法之间仅算法主体部分不同，配电网重构模型和基本环路搜索等模块完全一致，种群规模、精英数量等公共参数也完全相同。文中给出了各种算法的基本原理和求解步骤，以IEEE 33节点系统为例测试了算法参数的敏感性，对比了各种算法的性能差别，并使用IEEE 16节点和PG&E 69节点测试系统对算法的适应性做了进一步比较。测试得到的目标函数平均值、收敛到最优解的比例、计算时间以及对系统规模适应性等方面的结果表明：螺栓遗传算法（Stud GA）性能最优、生物地理学优化算法（BBO）次之、其他算法在不同的测试系统中表现的性能不一致。Stud GA具有操作简单、参数少、收敛到最优解的概率高、计算时间短等优点，适合用于求解配电网重构问题。

关键词: 电力系统, 智能算法, 配电网重构, 计算平台, 参数敏感性, 螺栓遗传算法, 生物地理学优化算法

Abstract: In order to seek an intelligent algorithm that is suitable for solving the distribution network reconfiguration issue, a computing platform which employs eight population-based intelligent algorithms is constructed. In the platform, for different algorithms, the distribution network reconfiguration model and the basic loop search module are completely consistent, with the parameters such as population size and elite number being identical. The basic principles and calculation procedures of the eight algorithms are given. The sensitivity of the algorithm parameters is tested, and the performance of the eight algorithms is compared using IEEE 33-bus system. In addition, the adaptability of the algorithms is further compared using the IEEE 16-bus and PG&E 69-bus test systems. The results show that Stud GA is the most suitable algorithm and BBO comes the second in terms of the average objective function value, the probability of converging to the best solution, the computation time and the adaptability to the systems of different scales, and the other algorithms have inconsistent performance in different test systems. Stud GA is a suitable algorithm for solving the distribution network reconfiguration issue because of its advantages of simple operation, few parameters, short computing time, and high probability of converging to the optimal solution.

Key words: power system, intelligent algorithm, distribution network reconfiguration, computing platform, parameter sensitivity, stud genetic algorithm, biogeography optimization algorithm

中图分类号:

TM71

高元海，王淳. 基于八种智能算法的配电网络重构计算平台及其性能[J]. 中国电力, 2015, 48(8): 53-60.

GAO Yuanhai, WANG Chun. A Computing Platform for Distribution Network Reconfiguration Based on 8 Intelligent Algorithms and Its Performance[J]. journal1, 2015, 48(8): 53-60.

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https://www.electricpower.com.cn/CN/Y2015/V48/I8/53

参考文献

[1] 袁沔齐，邹振宇，孙凯祺，等．基于遗传算法和微分进化算法的分布式电源优化配置[J]．中国电力，2013，46（1）：16－20．
YUAN Mianqi, ZOU Zhenyu, SUN Kaiqi, et al． Optimal allocation of distributed generation based on genetic algorithms and differential evolution algorithm[J]． Electric Power, 2013, 46(1):16-20．
[2] 盛四清，李兴，范林涛．基于进化策略法的配电网无功规划[J]．中国电力，2014，47（2）：152－155．
SHENG Siqing, LI Xing, FAN Lintao． Reactive power planning of distribution network based on evolutionary strategy theory[J]．Electric Power, 2014, 47(2): 152-155．
[3] 马立新，孙进，彭华坤．多目标差分进化算法的电力系统无功优化[J]．控制工程，2013，20（5）：953－956．
MA Lixin, SUN Jin, PENG Huakun． Multi-objective differential evolution algorithm for reactive power optimization[J]． Control Engineering of China, 2013, 20(5): 953-956．
[4] 李思，宋珺琤，沙立成．基于风险度评价和改进蚁群算法的配电网网架规划[J]．中国电力，2009，42（11）：52－56．
LI Si, SONG Juncheng, SHA Licheng． Power distribution network planning based on risk assessment and improved ant colony algorithm [J]． Electric Power, 2009, 42(11): 52-56．
[5] 姜惠兰，陈平，王敬朋，等．改进粒子群算法在电网无功优化中的应用[J]．中国电力，2011，44（12）：11－15．
JIANG Huilan, CHEN Ping, WANG Jingpeng, et al． Application of improve particle swarm optimization in power system reactive power control [J]． Electric Power, 2011, 44(12): 11-15．
[6] SIMON D. Biogeography-based optimization [J]. IEEE Transactions on Evolutionary Computation, 2008, 12(6): 702-713.
[7] 王淳，程浩忠．基于模拟植物生长算法的配电网重构[J]．中国电机工程学报，2007，27（19）：50－55．
WANG Chun, CHENG Haozhong. Reconfiguration of distribution network based on plant growth simulation algorithm[J]. Proceedings of the CSEE, 2007, 27(19): 50-55.
[8] 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.
[9] BRAZ H D M, SOUZA B A． Distribution network reconfiguration using genetic algorithms with sequential encoding: subtractive and additive approaches [J]． IEEE Transactions on power systems, 2011, 26(2): 582-593．
[10] 欧弟宏，陈皓勇，荆朝霞．基于协同进化算法的配电网络重构方案[J]．电力自动化设备，2012，32（7）：108－113．
OU Dihong, CHEN Haoyong, JING Zhaoxia． Distribution network reconfiguration based on co-evolution algorithm [J]． Electric Power Automation Equipment, 2012, 32(7): 108-113．
[11] 胡国伟，别朝红．一种适用于配电网重构的动态调整策略[J]．西安交通大学学报，2012，46（12）：78－84．
HU Guowei, BIE Zhaohong． Dynamic adjustment strategy for distribution network reconfiguration[J]． Journal of Xi’an Jiaotong University, 2012, 46(12): 78-84．
[12] 刘继栋，王锡淮，肖键梅．基于十进制编码的差分进化算法在配电网重构中的应用[J]．电力自动化设备，2012，32（5）：94－97．
LIU Jidong, WANG Xihuai, XIAO Jianmei． Distribution network reconfiguration based on different evolution algorithm using decimal encoding [J]． Electric Power Automation Equipment, 2012, 32(5): 94-97．
[13] WOLPERT D H, MACREADY W G． No Free Lunch Theorems for Optimization[J]． IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82．
[14] 王淳．一种实用的辐射网潮流算法[J]．继电器，2005，12（33）：13-16．
WANG Chun． A practical algorithm of power flow calculation for radial networks[J]． Relay, 2005, 12(33): 13-16.
[15] 玄光男，程润伟．遗传算法与工程优化[M]．北京：清华大学出版社，2004.
[16] 叶圣永，王晓茹，刘志刚，等．基于支持向量机增量学习的电力系统暂态稳定评估[J]．电力系统自动化，2011，35（11）：15－19．
YE Shengyong, WANG Xiaoru, LIU Zhigang, et al． Power system transient stability assessment based on support vector machine incremental learning method[J]． Automation of Electric Power,2011, 35(11): 15-19．
[17] 董明刚，牛秦洲，杨祥．基于对立策略的螺栓遗传算法[J]．计算机工程，2009，35（20）：239－241．
DONG Mingang, NIU Qinzhou, YANG Xiang． Opposition based stud genetic algorithm[J]． Computer Engineering, 2009, 35(20):239-241．
[18] CARRENO E M, ROMERO R, PADILHA FELTRIN A. An efficient codification to solve distribution network reconfiguration for loss reduction problem[J]． IEEE Transactions on Power Systems, 2008, 23(4): 1542-1551．
[19] ZIN A A M, FERDAVANI A K, KHAIRUDDIN A B, et al．Reconfiguration of radial electrical distribution network through minimum-current circular-updating-mechanism method[J]． IEEE Transactions on Power Systems, 2012, 27(2): 968-974．