利用遗传算法、GWO和粒子群算法的混合优化提高电力系统可靠性

Rachapalli Sireesha, Srinivasa Rao Coppisetty, Mallapu Vijaya Kumar
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引用次数: 0

摘要

摘要:本文提出了一种优化方法,用于研究在一定的降损和可靠性条件下构建的重构网络问题。此外,重新配置的网络系统根据影响限制的标准寻求优化。本研究针对重构过程中无供电资源的特定网络故障进行优化,以避免有功损耗的影响和占有。利用优化过程的数学方法实现了这些目标。在系统开发过程中首先生成数学公式。在此基础上,提出了一种基于遗传算法、灰狼优化和粒子群优化的优化方法。最后,对预期的方法进行了估计。基于结果,很明显,所提出的混合GWO-PSO方法在节点电压、可靠性、线路电流和计算时间方面优于所有其他方法。此外,当最佳尺寸的分布式电源被放置在最佳位置时,总损耗减少了63%,电压分布也得到了改善。
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Hybrid optimization to enhance power system reliability using GA, GWO, and PSO
Abstract An optimization approach is described in the research study that deals with the issue of reconfiguration networks built with certain conditions of power loss reduction and reliability. Furthermore, the reconfigured networking system seeks optimization based on criteria affecting the limitations. This study optimises specific network faults subjecting resources with no supply during reconfiguration to avoid the effect and possess through active power losses. These goals were met using the mathematical method of the optimisation process. The mathematical formulation is generated first in the system development process. As a result, a comprehensive methodology using genetic algorithm, Grey Wolf optimization (GWO), and particle swarm optimization (PSO) was developed. Finally, intended methodologies were estimated. Based on the results, it is clear that the proposed hybrid GWO-PSO approach outperforms all other methods in terms of node voltage, reliability, line currents, and computational duration. Furthermore, when optimally sized distributed generations are placed in optimal locations, total loss is reduced by up to 63% and voltage profiles improve.
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