Improved evolutionary programming with dynamic mutation and metropolis criteria for multi-objective reactive power optimisation
Improved evolutionary programming with dynamic mutation and metropolis criteria for multi-objective reactive power optimisation
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DOI：
10.1049/ip-gtd:20045007
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发表时间：
2005-03
期刊：
影响因子：
--
通讯作者：
C. Jiang;C. Wang
中科院分区：
文献类型：
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作者：
C. Jiang;C. Wang
无功优化是关系到电力系统安全、经济运行的一项重要技术。合理的无功分配可以提高额定电压，降低网损，保证电网在适当的条件下运行。提出了一种基于动态变异和城市选择的改进进化规划方法来解决放松管制环境下的多目标无功优化问题。该多目标函数包括最小化网损、电压偏差和补偿成本。针对传统进化规划方法存在的收敛和稳健性问题，考虑了影响算法收敛和稳健性的变异算子和选择准则，提出了一种动态变异和都市选择进化规划方法。该方法将混沌动力学引入到进化规划的变异算子中，采用类随机的确定性方法，突破了传统的用固定分布的随机数进行变异的思想。并在进化规划中引入大都市选择，构造新的选择算子。因此，该方法不仅加快了收敛速度，而且提高了精度，是优化电容器组和可调变比的有效途径。经IEEE-30节点系统测试，该方法是有效的。
Reactive power optimisation is an important technique, which is concerned with the security and economy of operation of the power system. The appropriate distribution of reactive power can elevate voltage rating, decrease network losses and maintain network running under proper conditions. An improved evolutionary programming method with dynamic mutation and metropolis selection to solve the multi-objective reactive power optimisation under the deregulation environment is presented. The multi-objective function includes the minimisation of network losses, voltage deviation and compensation cost. To solve the problems of convergence and robustness in the conventional evolutionary programming method, the mutation operators and the selection criteria that affect the convergence and robustness are considered and a dynamic mutation and metropolis selection evolutionary programming method is suggested. Introducing chaos dynamics into mutation operators of evolutionary programming, the new method adopts the certainty method of like-stochastic to obtain mutation operators which break through the conventional thought with mutation by stochastic numbers of fixed distribution. Also, it introduces the metropolis selection in evolutionary programming to construct new selection operators. Thus, the new method not only accelerates convergence but also increases precision, so is an efficient way to optimise the capacitor banks and the adjustable transformer ratio. Tested by the IEEE-30 bus system, the method is effective.