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Synchronized measurement based estimation of inter-area electromechanical modes using the Ibrahim time domain method

机译：易卜拉欣时域方法基于同步测量的区域间机电模式估计

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摘要

Based on measured ring-down signals from the power systems, this paper introduces the Ibrahim time domain (ITD) method for estimating the inter-area electromechanical modes, including the frequencies, damping ratios and the mode shapes. A zero-phase low-pass filter is utilized to improve the performance of the ITD method in the presence of measurement noise, and the statistical characteristics of the estimation results are obtained through Monte Carlo simulations. The performance of the proposed method is compared with that of the stochastic subspace identification (SSI) technique. Simulation results show that in the presence of measurement noise, the ITD method and the SSI method have similar good performance, but the ITD method is much faster than the SSI method; and when there exit both ring-down signals and ambient noise signals (ring-downs contaminated by ambient noise), the ITD method performs better than the SSI method does. Finally, the performance of the ITD method is evaluated by considering three factors that may be encountered in real measured data, which are measurement noise, ambient noise and limited availability of PMU measurements, and we find that as long as the total participation of the available inputs in a particular mode is overwhelming, the ITD method can still give quite acceptable estimation results for this mode. All the simulations are conducted using a nonlinear power system model, and the effect of the nonlinear factors of the system is incorporated in the simulation data.
机译：基于从电力系统测得的振铃信号，本文介绍了易卜拉欣时域（ITD）方法，用于估计区域间机电模式，包括频率，阻尼比和模式形状。利用零相位低通滤波器在存在测量噪声的情况下提高ITD方法的性能，并通过蒙特卡洛模拟获得估计结果的统计特征。将该方法的性能与随机子空间识别（SSI）技术的性能进行了比较。仿真结果表明，在存在测量噪声的情况下，ITD方法和SSI方法具有相似的良好性能，但ITD方法比SSI方法要快得多。当同时存在振铃信号和环境噪声信号（振铃信号被环境噪声污染）时，ITD方法的性能要好于SSI方法。最后，通过考虑实际测量数据中可能遇到的三个因素来评估ITD方法的性能，这三个因素是测量噪声，环境噪声和PMU测量的有限可用性，并且我们发现只要可用在特定模式下的输入是压倒性的，ITD方法仍然可以为该模式提供完全可以接受的估计结果。所有仿真都是使用非线性电力系统模型进行的，并且系统的非线性因素的影响已纳入仿真数据中。

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