遗传算法改进及其在电力系统无功优化中的应用剖析.docx

华北电力大学本科毕业设计（论文）  PAGE \* MERGEFORMAT II 华北电力大学本科毕业设计（论文） 遗传算法改进及其在电力系统无功优化中的应用 摘要 安全、经济、可靠运行是对电力系统最基本也是最重要的要求。无功功率分布合理的话，不仅可以使电压质量得到提高，还能最大化电力设备的经济效益，减少网络损耗和线损率等。为了保证电力系统安全、经济运行，无功优化技术的重要性不言而喻。传统无功优化方法有很多不足之处，为此科学家们把人工智能算法引入无功优化计算中来。本文介绍了智能算法中的经典代表——遗传算法在无功优化中的应用，对遗传算法的解题过程做了较深入的了解，并提出了应用于无功优化的改进遗传算法。 本文的无功优化数学模型采用的是以最小网损为主，结合节点电压越限和发电机无功出力越限罚函数的目标函数。简单遗传算法计算过程中存在收敛速度较慢和容易陷入局部最优解的问题，为此本文采用了实数编码，即十进制编码方式，运算过程中采用自适应的交叉算子和变异算子，对遗传种群采取精英策略，结合目标函数中的惩罚函数，力求又快又准地寻得全局最优解。 本文应用MATLAB遗传算法工具箱编程，先用两个简单算例证明了遗传算法的优越性和简单遗传算法的缺点，然后对IEEE-14、30节点系统进行了无功优化，对比简单遗传算法，证明了改进后的遗传算法在无功优化中的有效性和正确性。 关键词：无功优化；遗传算法；自适应；罚函数；MATLAB  = 1 \* ROMAN I  IMPROVED GENETIC ALGORITHM APPROACH FOR REACTIVE POWER PLANNING Abstract Safety and economy are two important indexes of power system. To improve the system voltage quality, give full play to the economic benefits of power equipment, reduce the rate of power grid loss and line loss, reasonable reactive power distribution is necessary. Hence, reactive power optimization technology is an effective and important method to guarantee the safety and economic operation of power system. There are many defects in the traditional reactive power optimization method, to solve this problem, scientists have introduced artificial intelligence algorithm to the reactive power optimization calculation. This paper introduces the classical representation of the intelligent algorithm——the application of Genetic Algorithm in reactive power optimization, makes a better understanding of the GA’s process of solving problems, and proposes the improved Genetic Algorithm for reactive power optimization. Combining with the penalty function of the node voltage and the reactive power of generator, the objective function of the reactive power optimization is based on the minimum system loss. Existing slow convergence and local optimal solution in the process of simple genetic algorithm calculation, this paper uses real coding method, adaptive crossover and mutation operator