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Impact of measurement selection on load model parameter estimation

机译:测量选择对负荷模型参数估计的影响

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Measurement-based load model parameter estimation uses measurements from a disturbance on the grid. Those measurements can include voltage and/or real and reactive power. In this paper, we show that the type of measurements used directly impacts the accuracy of parameter estimation. We look at four scenarios. With wide-area deployment of voltage sensors, such as PMUs, the resulting parameter estimation is very accurate at high signal-to-noise ratios (SNR), but is very poor at low SNRs, because voltage has low sensitivity to the parameters. With only local deployment of complex power sensors, the estimate is worse than the first scenario at all SNRs. However, with wide-area deployment of complex power sensors, the estimate becomes very robust to low SNR, because complex power has much higher sensitivity to the parameters. Combining wide-area voltage and power measurements produces the best results.
机译:基于测量的负载模型参数估计使用来自电网干扰的测量。这些测量可以包括电压和/或有功功率和无功功率。在本文中,我们表明使用的测量类型直接影响参数估计的准确性。我们来看四种情况。随着电压传感器(例如PMU)的大范围部署,最终得到的参数估计在高信噪比(SNR)时非常准确,但是在低SNR时非常差,因为电压对参数的灵敏度很低。仅在本地部署复杂的功率传感器,在所有SNR情况下,估算结果都比第一种情况差。但是,在复杂功率传感器的大范围部署中,由于复杂功率对参数具有更高的灵敏度,因此估计对于低SNR变得非常稳健。结合广域电压和功率测量可产生最佳结果。

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