Evaluating the Impact of Phasor Measurement Units on the Accuracy of State Estimation

H. Mosbah, M. El-Hawary
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引用次数: 2

Abstract

PMUs are viewed as one of the most vital measurement devices in future of electric grid. PMUs devices can provide synchronized phasor measurement of voltages and currents from broadly scattered areas in an electric power grid. A hybrid Multilayer Perceptron NN-Stochastic Fractal Search (MLP-SFS) algorithm is being proposed in rectangular coordinates to solve hybrid state estimator problem. Hybrid SE is defined based on its measurement set which consists of traditional as well as synchronized measurements. The approach classifies the process into two steps. The first step, Multilayer Perceptron NN is used to compute the initial estimated states. The second step, SFS is implemented to acquire the final estimated states. This hybrid technique is used to improve the accuracy of state estimation. The size of PMUs is gradually increased by adding them to the conventional measurement set. Six cases are tested to show the impact of PMUs on the accuracy. The application of the hybrid technique is illustrated on IEEE 14, 30, and 57-bus systems. The Performance of MLPN-SFS is compared to MLP and SFS individually.
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相量测量单元对状态估计精度的影响评估
pmu被认为是未来电网中最重要的测量设备之一。pmu设备可以提供来自电网中广泛分散区域的电压和电流的同步相量测量。提出了一种在直角坐标系下求解混合状态估计器问题的混合多层感知器nn -随机分形搜索(MLP-SFS)算法。混合SE是根据其测量集来定义的,该测量集由传统测量集和同步测量集组成。该方法将该过程分为两个步骤。第一步,使用多层感知器神经网络计算初始估计状态。第二步,实现SFS获取最终估计状态。采用这种混合技术提高了状态估计的精度。通过将pmu添加到常规测量集中,逐渐增大pmu的尺寸。通过对六个案例的测试,显示了PMUs对精度的影响。说明了混合技术在IEEE 14、30和57总线系统上的应用。将MLPN-SFS的性能分别与MLP和SFS进行了比较。
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