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Optimal power flow using Moth Swarm Algorithm with Gravitational Search Algorithm considering wind power

机译:考虑风能的飞虫算法和引力搜索算法的最优潮流

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摘要

One of the major problems to be solved in power systems is Optimum Power Flow. Presently, in order to fulfill the power demand and make cost effective power supply, it is forced to use the renewable energy resources as an alternative energy sources. Various existing research studies utilized different artificial intelligence and metaheuristic approaches for solving OPF problems in power system. But, in this paper it is aimed to provide a hybrid approach MSA-GSA by integrating Moth Swarm Algorithm (MSA) and Gravitational Search Algorithm (GSA) for power systems with Wind energy sources. To do this, the WDF (Weibull Distribution Function) is used for demonstrating the alternating nature of wind farm. The test cases, with and without wind power are considered for solving the objective functions of deteriorating the Fuel cost for reduced power loss. Finally, the simulation results are tested with IEEE 30-bus, IEEE 57 bus and IEEE 118 bus with and without wind power. The proposed MSA-GSA algorithm offers better results when associated with the existing procedures.
机译:电力系统要解决的主要问题之一是最佳功率流。目前,为了满足电力需求并提供具有成本效益的电力供应,不得不将可再生能源用作替代能源。现有的各种研究利用不同的人工智能和元启发式方法来解决电力系统中的OPF问题。但是,本文旨在通过将蛾群算法(MSA)和引力搜索算法(GSA)集成在一起,为具有风力能源的电力系统提供一种混合方法MSA-GSA。为此,使用WDF(Weibull分布函数)来演示风电场的交替性质。考虑使用具有和不具有风力的测试用例,以解决降低燃料成本,降低功率损耗的客观功能。最后,在有和没有风力的情况下,使用IEEE 30总线,IEEE 57总线和IEEE 118总线对仿真结果进行了测试。当与现有程序关联时,提出的MSA-GSA算法可提供更好的结果。

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