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Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm

机译:用于电力损耗的分配网络重新配置,使用混沌随机分数算法进行功率损耗和电压曲线改进

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This paper proposes a chaotic stochastic fractal search algorithm (CSFSA) method to solve the reconfiguration problem for minimizing the power loss and improving the voltage profile in distribution systems. The proposed method is a metaheuristic method developed for overcoming the weaknesses of the conventional SFSA with two processes of diffuse and update. In the first process, new points will be created from the initial points by the Gaussian walk. For the second one, SFSA will update better positions for the particles obtained in the diffusion process. In addition, this study has also integrated the chaos theory to improve the SFSA diffusion process as well as increase the rate of convergence and the ability to find the optimal solution. The effectiveness of the proposed CSFSA has been verified on the 33-bus, 84-bus, 119-bus, and 136-bus distribution systems. The obtained results from the test cases by CSFSA have been verified to those from other natural methods in the literature. The result comparison has indicated that the proposed method is more effective than many other methods for the test systems in terms of power loss reduction and voltage profile improvement. Therefore, the proposed CSFSA can be a very promising potential method for solving the reconfiguration problem in distribution systems.
机译:本文提出了一种混沌随机分数态搜索算法(CSFSA)方法来解决重新配置问题,以使功率损耗最小化,改善分配系统中的电压曲线。该方法是一种用于克服传统SFSA的弱点的成式方法,具有两个漫射和更新的过程。在第一个过程中,将从高斯步行中的初始点创建新点。对于第二个,SFSA将更新在扩散过程中获得的粒子的更好位置。此外,本研究还集成了混沌理论,以改善SFSA扩散过程,增加收敛速度和找到最佳解决方案的能力。拟议的CSFSA的有效性已经在33公交车,84公交车,119公共汽车和136柱式分配系统上验证。通过CSFSA测试病例的获得结果已经验证了来自文献中的其他自然方法的结果。结果比较表明,在功率损耗减少和电压型材改进方面,所提出的方法比测试系统的许多其他方法更有效。因此,所提出的CSFSA可以是用于解决分配系统中的重新配置问题的非常有希望的潜在方法。

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