In practical applications like power systems, the distribution of the measurement noise is usually unknown and frequently deviates from the assumed Gaussian model, yielding outliers. Under these conditions, the performances of the existing state estimators that rely on Gaussian assumption can deteriorate significantly. In addition, the sampling rates of measurements from supervisory control and data acquisition (SCADA) system and phasor measurement unit (PMU) are quite different, causing time skewness problem. In this paper, we propose a robust state estimation framework to address the unknown non-Gaussian noise and the measurement time skewness issue. In the framework, robust Mahalanbis distances are proposed to detect system abnormalities and assign appropriate weights to each chosen buffered PMU measurements. Those weights are further utilized by the Schweppe-type Huber generalized maximum-likelihood (SHGM) estimator to filter out non-Gaussian PMU measurement noise and help suppress outliers. In the meantime, the SHGM estimator is used to handle unknown noise of the received SCADA measurements, yielding another set of state estimates. We show that the state estimates provided by the SHGM estimator follow an asymptotical Gaussian distribution. This nice property allows us to obtain the optimal state estimates by resorting to the data fusion theory for the fusion of the estimation results from two independent SHGM estimators. Extensive simulation results carried out on the IEEE 14, 30 and 118-bus test systems demonstrate the effectiveness and robustness of the proposed method.

中文翻译：

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具有未知测量噪声统计的鲁棒混合状态估计框架


在电力系统等实际应用中，测量噪声的分布通常是未知的，并且经常偏离假设的高斯模型，从而产生异常值。在这些条件下，依赖高斯假设的现有状态估计器的性能可能会显着恶化。此外，监控与数据采集（SCADA）系统和相量测量单元（PMU）的测量采样率差异较大，导致时间偏斜问题。在本文中，我们提出了一种鲁棒的状态估计框架来解决未知的非高斯噪声和测量时间偏斜问题。在该框架中，提出了稳健的马哈兰比斯距离来检测系统异常并为每个选定的缓冲 PMU 测量分配适当的权重。 Schweppe 型 Huber 广义最大似然 (SHGM) 估计器进一步利用这些权重来滤除非高斯 PMU 测量噪声并帮助抑制异常值。同时，SHGM 估计器用于处理接收到的 SCADA 测量值的未知噪声，从而产生另一组状态估计。我们证明 SHGM 估计器提供的状态估计遵循渐近高斯分布。这个良好的特性使我们能够利用数据融合理论来融合两个独立的 SHGM 估计器的估计结果，从而获得最佳状态估计。在 IEEE 14、30 和 118 总线测试系统上进行的大量仿真结果证明了该方法的有效性和鲁棒性。