This paper develops a novel approach to track power system state evolution based on the maximum correntropy criterion, due to its robustness against non-Gaussian errors. It includes the temporal aspects on the estimation process within a maximum-correntropy-based extended Kalman filter (MCEKF), which is able to deal with both nonlinear supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurement models. By representing the behavior of the state variables with a nonparametric model within the kernel density estimation, it is possible to include abrupt state transitions as part of the process noise with non-Gaussian characteristics. Also, a novel strategy to update the size of Parzen windows in the kernel estimation is proposed to suppress the effects of suspect samples. By properly adjusting the kernel bandwidth, the proposed MCEKF keeps its accuracy during sudden load changes and contingencies, or in the presence of bad data. Simulations with IEEE test systems and the Brazilian interconnected system are carried out. The results show that the method deals with non-Gaussian noises in both the process and measurement, and provides accurate estimates of the system state under normal and abnormal conditions.

中文翻译：

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使用基于最大熵的扩展卡尔曼滤波器跟踪电力系统状态演化

由于其针对非高斯误差的鲁棒性，本文提出了一种基于最大熵准则跟踪电力系统状态演化的新方法。它包括基于最大熵的扩展卡尔曼滤波器（MCEKF）中估计过程的时间方面，该滤波器能够处理非线性监督控制和数据采集（SCADA）和相量测量单元（PMU）测量模型。通过在内核密度估计中用非参数模型表示状态变量的行为，可以将突然的状态转换作为具有非高斯特性的过程噪声的一部分包括在内。此外，提出了一种在核估计中更新Parzen窗口大小的新颖策略，以抑制可疑样本的影响。通过适当地调整内核带宽，建议的MCEKF在突然的负载变化和意外情况下或存在不良数据时保持其准确性。使用IEEE测试系统和巴西互连系统进行了仿真。结果表明，该方法处理过程和测量中的非高斯噪声，并提供正常和异常条件下系统状态的准确估计。