Transmission Line Parameter Estimation Under Non-Gaussian Measurement Noise
Transmission Line Parameter Estimation Under Non-Gaussian Measurement Noise
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DOI：
10.1109/tpwrs.2022.3204232
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发表时间：
2022-08
影响因子：
6.6
通讯作者：
A. Varghese;A. Pal;Gautam Dasarathy
中科院分区：
文献类型：
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作者：
A. Varghese;A. Pal;Gautam Dasarathy
准确了解输电线路参数对于各种电力系统监测、保护和控制应用至关重要。在传输线参数估计（TLPE）中使用相量测量单元（PMU）数据是有充分记录的。然而，现有的基于pmu的TLPE文献隐含地假设测量噪声为高斯噪声。最近的研究表明，PMU测量中的噪声（特别是电流相量中的噪声）可以用高斯混合模型（GMMs）更好地表示，即噪声是非高斯的。我们提出了一种新的TLPE方法，可以处理PMU测量中的非高斯噪声。测量噪声用GMM表示，该GMM的分量使用期望最大化算法进行识别。然后，通过迭代求解极大似然估计问题进行噪声和参数估计，直到收敛。该方法优于传统方法，如最小二乘和总最小二乘，以及最近提出的最小总误差熵方法，通过使用IEEE 118总线系统和从美国电力公司获得的专有PMU数据进行模拟，证明了该方法的优越性能。
Accurate knowledge of transmission line parameters is essential for a variety of power system monitoring, protection, and control applications. The use of phasor measurement unit (PMU) data for transmission line parameter estimation (TLPE) is well-documented. However, existing literature on PMU-based TLPE implicitly assumes the measurement noise to be Gaussian. Recently, it has been shown that the noise in PMU measurements (especially in the current phasors) is better represented by Gaussian mixture models (GMMs), i.e., the noises are non-Gaussian. We present a novel approach for TLPE that can handle non-Gaussian noise in the PMU measurements. The measurement noise is expressed as a GMM, whose components are identified using the expectation-maximization (EM) algorithm. Subsequently, noise and parameter estimation is carried out by solving a maximum likelihood estimation problem iteratively until convergence. The superior performance of the proposed approach over traditional approaches such as least squares and total least squares as well as the more recently proposed minimum total error entropy approach is demonstrated by performing simulations using the IEEE 118-bus system as well as proprietary PMU data obtained from a U.S. power utility.