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Solving Optimal Power Flow Problems Using Chaotic Self-adaptive Differential Harmony Search Algorithm

机译：用混沌自适应微分和声搜索算法求解最优潮流问题

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摘要

Optimal power flow is the basic tool that allows an electric utility to determine the economic and secure operating conditions of an electric power system. This article presents a chaotic self-adaptive differential harmony search algorithm to solve optimal power flow problems with non-smooth and non-convex cost functions. The searching capacity of the proposed chaotic self-adaptive differential harmony search algorithm has been improved by introducing a chaotic self-adaptive differential mutation operator instead of a pitch adjustment operator in the harmony search algorithm. The effectiveness of the proposed chaotic self-adaptive differential harmony search algorithm has been tested with IEEE 30-bus, IEEE 300-bus, and 66-bus Indian utility systems. The simulation results obtained using the proposed algorithm are compared with other variants of the improved harmony search algorithm, such as the differential harmony search algorithm, the chaotic differential harmony search algorithm, the interior point method, and other techniques reported in the literature, to show its effectiveness. The results obtained by the proposed algorithm are found to be better than the results obtained by the differential harmony search, chaotic differential harmony search, interior point method, and other algorithms reported in the literature in terms of solution quality and standard deviation of generation cost. In terms of speed of convergence and computational time, the proposed algorithm is better than the differential harmony search and chaotic differential harmony search algorithms.
机译：最佳潮流是允许电力公司确定电力系统的经济和安全运行条件的基本工具。本文提出一种混沌自适应微分和声搜索算法，以解决具有非光滑和非凸成本函数的最优潮流问题。通过在和声搜索算法中引入混沌自适应微分变异算子而不是基音调整算子，提高了所提出的混沌自适应微分和声搜索算法的搜索能力。所提出的混沌自适应微分和声搜索算法的有效性已经在IEEE 30总线，IEEE 300总线和66总线印度公用事业系统中进行了测试。将使用该算法获得的仿真结果与改进的和声搜索算法的其他变体进行比较，例如差分和声搜索算法，混沌差分和声搜索算法，内点法和文献中报道的其他技术，以显示其有效性。从解决方案质量和发电成本的标准偏差方面，发现该算法获得的结果要优于差分和声搜索，混沌差分和声搜索，内点法和其他文献报道的算法。在收敛速度和计算时间上，该算法优于差分和声搜索和混沌差分和声搜索算法。

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