论文标题
非高斯测量噪声下的传输线参数估计
Transmission Line Parameter Estimation Under Non-Gaussian Measurement Noise
论文作者
论文摘要
对传输线参数的准确知识对于各种电力系统监控，保护和控制应用是必不可少的。使用相量测量单元（PMU）数据进行传输线参数估计（TLPE）有充分记录。但是，有关基于PMU的TLPE的现有文献隐含地假设测量噪声为高斯。最近，已经表明，PMU测量中的噪声（尤其是在当前的相s子中）更好地由高斯混合模型（GMM）代表，即噪声是非高斯的。我们提出了一种新颖的TLPE方法，可以在PMU测量中处理非高斯噪声。测量噪声表示为GMM，其组件是使用预期最大化（EM）算法识别的。随后，噪声和参数估计是通过迭代地迭代解决最大似然估计问题直到收敛来进行的。通过使用IEEE 118-BUS系统以及从美国电力实用程序获得的IEEE 118-BUS系统以及从美国电力公司获得的专有PMU数据，证明了所提出的方法比传统方法的优越性能，例如最小二乘和最小二乘以及最近提出的最小总误差熵方法。
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.
