Comparison between particle swarm optimization and Cuckoo Search method for optimization in unbalanced active distribution system

Tianjian Wang, Matin Meskin, I. Grinberg
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引用次数: 11

Abstract

Recently, the integration of distributed generation (DG) units to distribution networks has grown significantly. This integration provides an opportunity to control the power flow, resulting in the optimal power flow (OPF) at the distribution level. OPF can reduce system losses and decrease the DG generation costs. Additionally, it can improve the voltage profile. Applying OPF to distribution networks is a challenging task since the nature of distribution networks makes the OPF a nonlinear problem. In this paper, a multi-objective function is used to define the nonlinear power flow problem. To solve the OPF problem, the Particle Swarm Optimization (PSO) and Cuckoo Search (CS) algorithms are applied. These approaches are investigated utilizing IEEE 37 nodes test case. Comparing the results of the two methods shows that the CS algorithm performs better than PSO. The advantages of the CS algorithm, including fewer initial solutions, strong optimization searching ability, and fast convergence speed, make it an effective tool for solving the nonlinear optimization problem.
粒子群算法与布谷鸟搜索算法在不平衡有源配电网优化中的比较
近年来,分布式发电机组与配电网的集成发展迅速。这种集成提供了控制潮流的机会,从而在配电层面实现最佳潮流(OPF)。OPF可以减少系统损耗,降低DG发电成本。此外,它可以改善电压分布。由于配电网的非线性特性,将OPF应用于配电网是一项具有挑战性的任务。本文采用多目标函数来定义非线性潮流问题。为了解决OPF问题,采用了粒子群优化(PSO)和布谷鸟搜索(CS)算法。利用IEEE 37节点测试用例对这些方法进行了研究。比较两种方法的结果表明,CS算法的性能优于粒子群算法。CS算法具有初始解少、寻优能力强、收敛速度快等优点,是求解非线性优化问题的有效工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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