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首页> 外文期刊>International review of electrical engineering >A Reliable Wide-Area Measurement System Using Hybrid Genetic Particle Swarm Optimization (HGPSO)
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A Reliable Wide-Area Measurement System Using Hybrid Genetic Particle Swarm Optimization (HGPSO)

机译:使用混合遗传粒子群算法(HGPSO)的可靠的广域测量系统

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The state of the power system has to be estimated continuously and accurately by means of operating parameters i.e. voltage and current phasors. Phasor measurement unit (PMU) is becoming a most prominent tool for monitoring, control and protection of electric networks, and hence it is required to employ them for the present and future power system networks. Hence an Optimal PMU Placement (OPP) is quite important during the planning studies for both existing and future power networks. So, when a new state estimator is commissioned, or an existing estimator is up-graded, the problems of minimizing the number of PMUs and their optimal location for system with complete observability will come into scenario. This paper presents a novel Hybrid Genetic Particle Swarm Optimization (HGPSO) method based approach for a power system to have complete observability based optimal PMU placement problem subjected to all possible contingencies and PMU communication channel limitations. The proposed method is tested with some of standard IEEE test systems and also practiced on some Inter Regional Power Grids (IRPGs) of the Indian power system using MATLAB. The results obtained were also compared with existing techniques that have been already applied on the test systems said above, were proved to be the best and effective.
机译:电力系统的状态必须借助于操作参数即电压和电流相量来连续和准确地估计。相量测量单元(PMU)成为监视,控制和保护电网的最主要工具,因此需要将其用于当前和将来的电力系统网络。因此,在对现有和未来电力网络进行规划研究期间,最佳PMU布置(OPP)至关重要。因此,当委托新的状态估计器或升级现有的估计器时,将出现使PMU的数量最小化以及它们对于具有完全可观察性的系统的最佳位置的问题。本文提出了一种新颖的基于混合遗传粒子群算法(HGPSO)的电力系统方法,该方法具有完全可观察性的最优PMU放置问题,该问题受到所有可能的突发事件和PMU通信通道限制的影响。所提出的方法已通过一些标准IEEE测试系统进行了测试,并使用MATLAB在印度电力系统的某些区域间电网(IRPG)上得到了实践。还将获得的结果与已经在上述测试系统上应用的现有技术进行了比较,事实证明它们是最佳和有效的。

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