【作者】 邓永生；

【导师】 熊小伏；

【作者基本信息】 重庆大学 ， 电力系统及其自动化， 2002， 硕士

【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。更多还原

【Abstract】 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.<WP=5> 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.更多还原