遗传算法在配电网重构中的应用研究
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摘要
配电网络重构是一个非线性组合优化问题，智能计算方法目前被认为是最有效的求解途径。本文基于改进遗传算法求解配电网络优化重构和故障恢复重构的最佳结构。
论文首先阐述了智能计算方法及其在电力系统和配电网络重构领域中的研究现状。接着从理论上多角度地分析遗传算法，阐述了遗传算法的基本内容、工作机理，并基于Markov链对遗传算法进行了全局收敛性分析。
针对目前遗传算法在配电网络重构应用中的不足，论文着重从选择算子、交叉算子、变异算子和收敛准则等方面进行了改进，把最优保存策略和两两竞争相结合的方法作为新的选择算子，采用随最优个体相对保留代数自适应变化的交叉和变异算子，把最优个体最少保留代数作为算法的终止条件。这些改进提高了改进遗传算法（IGAs）解算配电网络重构问题的收敛性和计算效率。
论文对配电网络优化重构模型进行了一定的研究和探讨，采用考虑负荷平衡约束的以网损和停电损失最小为目标的多目标优化重构模型，并通过变加权系数理论，将配电网多目标优化问题转化为单目标优化。
将改进遗传算法（IGAs）用于解算配电网络优化重构问题。解算时，对配电网络优化重构问题的染色体编码作了深入的研究，同时对初始化、约束条件的处理、适应度函数的构造以及基因操作等方面作了较为深入的研究。给出了基于IGAs的配电网络优化重构算法的基本步骤和流程框图。算例分析结果表明，IGAs具有良好的全局搜索能力和较高的计算速度。
论文对改进遗传算法（IGAs）在配电网络故障恢复重构中的应用进行了初步探讨。探讨了配电网络故障恢复重构的数学模型，深入研究了配电网络故障恢复重构的染色体编码方案以及适应度函数。详细讨论了基于IGAs的配电网络故障恢复重构算法的基本步骤。算例的仿真结果表明：IGAs能够用于解算配电网络故障恢复重构问题，而且比SGA，具有较好的收敛性和较高的计算效率，拓展了遗传算法在配电网络故障恢复重构中的应用前景。
通过以上的分析表明，论文针对配电网络重构问题对遗传算法的改进是有效的，改进后的遗传算法（IGAs）比SGA具有更好的收敛性和更高的计算效率。
Network reconfiguration is a nonlinear and very troublesome optimization problem, and the computational intelligence method is possibly the most ideal way to solve the problem. The dissertation has successfully searched the best structure of the distribution system by the improved genetic algorithms.
The dissertation first elaborates on the computational intelligence method and its current situation in the field of power system and distribution network reconfiguration. Then it analyses genetic algorithms theoretically, and elaborates on the basic contents and the working principles of the genetic algorithms, and makes a global convergence analysis based on the Markov Chains.
Based on the single genetic algorithms and the features of the distribution network reconfiguration, this dissertation makes a further study on such aspects as selection operator, crossover operator, mutation operator, termination conditions and etc, thus, puts forward improved genetic algorithms.
The dissertation makes certain study on the optimization reconfiguration model of distribution network. It puts forward a multi-objective model and according to the theory of variable weight coefficients transforms the multi-objective problem into a single-objective one.
It applies the IGAs to the solving of the distribution network optimization reconfiguration, making a further probe into such areas as chromosome encoding, initialization, constraints handling, construction of fitness function and the gene operation. As the optimization results show, the improved genetic algorithms has fine ability of global searching and good solution speed.
The dissertation makes a basic exploration in the application of IGAs for the service restoration through network reconfiguration for distribution network. It explores its mathematical model, studys the chromosome encoding plan and constructs the fitness function. It also explores into its basic procedures in detail. As the example imitation results show, IGAs can be applied to the solving of the distribution network service restoration, and has a better convergence and a higher computation efficiency, therefore, extending prospects in the application.
Such an analysis indicates that IGAs has a successful application to the distribution network reconfiguration. IGAs bears a better convergence and a higher efficiency than SGA.
论文首先阐述了智能计算方法及其在电力系统和配电网络重构领域中的研究现状。接着从理论上多角度地分析遗传算法，阐述了遗传算法的基本内容、工作机理，并基于Markov链对遗传算法进行了全局收敛性分析。
针对目前遗传算法在配电网络重构应用中的不足，论文着重从选择算子、交叉算子、变异算子和收敛准则等方面进行了改进，把最优保存策略和两两竞争相结合的方法作为新的选择算子，采用随最优个体相对保留代数自适应变化的交叉和变异算子，把最优个体最少保留代数作为算法的终止条件。这些改进提高了改进遗传算法（IGAs）解算配电网络重构问题的收敛性和计算效率。
论文对配电网络优化重构模型进行了一定的研究和探讨，采用考虑负荷平衡约束的以网损和停电损失最小为目标的多目标优化重构模型，并通过变加权系数理论，将配电网多目标优化问题转化为单目标优化。
将改进遗传算法（IGAs）用于解算配电网络优化重构问题。解算时，对配电网络优化重构问题的染色体编码作了深入的研究，同时对初始化、约束条件的处理、适应度函数的构造以及基因操作等方面作了较为深入的研究。给出了基于IGAs的配电网络优化重构算法的基本步骤和流程框图。算例分析结果表明，IGAs具有良好的全局搜索能力和较高的计算速度。
论文对改进遗传算法（IGAs）在配电网络故障恢复重构中的应用进行了初步探讨。探讨了配电网络故障恢复重构的数学模型，深入研究了配电网络故障恢复重构的染色体编码方案以及适应度函数。详细讨论了基于IGAs的配电网络故障恢复重构算法的基本步骤。算例的仿真结果表明：IGAs能够用于解算配电网络故障恢复重构问题，而且比SGA，具有较好的收敛性和较高的计算效率，拓展了遗传算法在配电网络故障恢复重构中的应用前景。
通过以上的分析表明，论文针对配电网络重构问题对遗传算法的改进是有效的，改进后的遗传算法（IGAs）比SGA具有更好的收敛性和更高的计算效率。
Network reconfiguration is a nonlinear and very troublesome optimization problem, and the computational intelligence method is possibly the most ideal way to solve the problem. The dissertation has successfully searched the best structure of the distribution system by the improved genetic algorithms.
The dissertation first elaborates on the computational intelligence method and its current situation in the field of power system and distribution network reconfiguration. Then it analyses genetic algorithms theoretically, and elaborates on the basic contents and the working principles of the genetic algorithms, and makes a global convergence analysis based on the Markov Chains.
Based on the single genetic algorithms and the features of the distribution network reconfiguration, this dissertation makes a further study on such aspects as selection operator, crossover operator, mutation operator, termination conditions and etc, thus, puts forward improved genetic algorithms.
The dissertation makes certain study on the optimization reconfiguration model of distribution network. It puts forward a multi-objective model and according to the theory of variable weight coefficients transforms the multi-objective problem into a single-objective one.
It applies the IGAs to the solving of the distribution network optimization reconfiguration, making a further probe into such areas as chromosome encoding, initialization, constraints handling, construction of fitness function and the gene operation. As the optimization results show, the improved genetic algorithms has fine ability of global searching and good solution speed.
The dissertation makes a basic exploration in the application of IGAs for the service restoration through network reconfiguration for distribution network. It explores its mathematical model, studys the chromosome encoding plan and constructs the fitness function. It also explores into its basic procedures in detail. As the example imitation results show, IGAs can be applied to the solving of the distribution network service restoration, and has a better convergence and a higher computation efficiency, therefore, extending prospects in the application.
Such an analysis indicates that IGAs has a successful application to the distribution network reconfiguration. IGAs bears a better convergence and a higher efficiency than SGA.
引文
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[2] 柳焯.最优化原理及其在电力系统中的应用.哈尔滨:哈尔滨工业大学出版社,1988
[3] 杨毅刚,杨期余,杨维汉.电力系统优化的理论基础.北京:水利电力出版社,1990
[4] 刘健等.配电自动化系统.北京:中国水利水电出版社,1999
[5] 刘健. 变结构耗散网络. 北京:中国水利水电出版社,2000
[6] 刘健等. 城乡电网建设与改造指南. 北京:中国水利水电出版社,2001
[7] 罗毅等. 配电网自动化实用技术. 北京:中国电力出版社,1999
[8] 方富淇.配电网自动化.北京:中国电力出版社,2000
[9] 刘宝碇,赵瑞清.随机规划与模糊规划.北京:清华大学出版社,1998
[10] 周明,孙树栋.遗传算法原理及应用.北京:国防工业出版社,1999
[11] E. Masud .An Interactive Procedure for Sizing and Timing Distribution Substation Using Optimization Techniques. IEEE on PAS,1974, 8(5),1281~1286
[12] Crawford, Holt. A Mathematical Optimization Technique for Locating and Sizing Distribution Substation and Deriving Their Optimal Service Areas. IEEE on PAS,1975,13(2), 230~235
[13] H. L. Will, T. O. Vismor. Automatic Assessment of Transfer Costs in Automated Substation Planning Models. Kansas city: IEEE T&D Conference,1984
[14] M. Kaplan, et al. Contribution to the Determination of Optimum Site for Substation. IEEE on PAS,1981,7(5), 2263~2270
[15] M. Ponnavaikko .An Approach to Optimal Distribution System Planning Through Conductor Gradation" IEEE on PAS,1982,23(6),1735~1742
[16] K. Aoki, et al. New Approximate Optimization Method for Distribution System Planning. IEEE on PS,1990,11(1),126~132
[17] 王天华,王平洋,范明天.馈线自动化规划中环网开关配置的优化算法研究.电力系统自动化,1999,23(15),23~26
[18] 盛四清等.基于遗传算法的地区电网停电恢复.电力系统自动化,2001, 25(8),53~55
[19] J H Holland. Adaptation in Natural and Artificial Systems. Michigan: The University of Michigan Press,1975
[20] D E Goldberg. Genetic Algorithms in Search，Optimization and Machine Learning.America:Addison-Wesley,1989
[21] L Davis. Handbook of Genetic Algorithms. New York: Van Nonstrand Reinhold,1991
[22] Michalewicz Z. Genetic Algorithm + Data Structures = Evolution Programs. New York: Spring-Verlag,1992
[23] David B Fogel. An Introduction to Simulated Evolutionary Optimization. IEEE Trans on Neural Networks,1994,5(1),3~11
[24] L J. Fogel, A J. Owers. Artificial Intelligence through Simulated Evolution. America: John Wiley&Sons,1966
[25] H P Schwefel. Evolution and Optimum Seeking. America: John Wiley&Sons,1994
[26] David B Fogel. An introduction to Simulated Evolutionary Optimizations. IEEE Trans on Neural Networks, 1994,5(1),3~11
[27] Sheble G B, Britting K. Refine Genetic Algorithm――Economic Dispatch Example. IEEE Trans on PS, 1995, 10(1),117~123
[28] 袁慧梅.具有自适应交换率和变异率的遗传算法.北京:首都师范大学学报（自然科学版）, 2000,21(3),14~20
[29] Scrinivas M, Patanaik L M. Adaptive Probabilities of Crossover and Mutation. IEEE Trans on SMC, 1994, 24(4),656~666
[30] 叶在福,单渊达.基于多种群遗传算法的输电系统扩展规划.电力系统自动化,2000,29(10),24~37
[31] Davis L. Adapting Operator Probabilities in Genetics Algorithms. In: Proceedings of the 3rd International Conference on Genetic Algorithms. Boc, 1989,61~69
[32] Fogarty T C. Varying the Probability of Mutation in Genetic Algorithms. In: Proceedings of the 3rd International Conference on Genetic Algorithms. Boc, 1989,104~109
[33] 恽为民,席裕庚.简单遗传算法的运行机理研究.珠海:第三届中国自动化学术讨论会,1993,36~43
[34] Whitley D. Genitor Ⅱ. A Distributed Genetic Algorithms. New York: Van Nonstrand Reinhold ,1990,189~214
[35] Koichi Nara et al, Implementation of Genetic Algorithm for Distribution System Loss Minimum Reconfiguration, IEEE Trans on PWRS, 1992.8,7(3).
[36] Nam K et al, Genetic Algorithm for Reconfiguration of Radial Load Balanced Distribution System. In ISAP97 1997
[37] Fudou H et al. Agenetic Algorithm for Network Reconfiguration Using Three
Phase Unbalanced Load Flow. In ISAP97 1997
[38] Jang J T et al. A Study on the Reconfiguration Method in Multi-Linked Distribution System. In ISAP97 1997
[39] Monclar F R et al. The Use of a Genetic Algorithm for Power Loss Reduction via Reconfiguration of MV networks. In ISAP97 1997:443~447
[40] 梁勇,张焰,候志俭.遗传算法在配电网重构中的应用.电力系统及其自动化学报,1998,12(4),29~34
[41] Jong-Bae Park, Young-Moon Park, Jong-Ryul Won, Kwang Y. Lee .An Improved Genetic Algorithm for Generation Expansion Planning. IEEE Trans on PW, 2000,15(3),916~922
[42] 韩祯祥,文福全.模拟进化优化方法及其应用.计算机科学,1995, 22( 2),20~25
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