The robust Kalman filter with correntropy loss has received much attention in recent years for forecasting-aided state estimation in power systems, since it efficiently reduces the negative influence of various abnormal situations, such as non-Gaussian communication, changing environment, and instrument failures, and obviously improves the stability of power systems. However, the existing correntropy-based robust Kalman filters usually use the Gaussian function with a fixed center as the kernel function in correntropy, which may not be a suitable choice in practical applications of power system forecasting-aided state estimation (PSSE). To address this issue, a new and robust unscented Kalman filter, called the maximum correntropy with variable center unscented Kalman filter (MCVUKF), is proposed in this paper for PSSE. Specifically, MCVUKF adopts an extended version of correntropy, whose center can be located at any position, to replace the original correntropy in an unscented Kalman filter to improve the performance in PSSE. Moreover, by using an exponential function of the innovation vector to adjust a covariance matrix, an enhanced MCVUKF (En-MCVUKF) method is also developed for suppressing the influence of bad data to the innovation vector and further improving the accuracy of PSSE. Finally, extensive simulations have been conducted on IEEE 14-bus, 30-bus, and 57-bus test power systems, and the simulation results have shown the superiority of the proposed MCVUKF and En-MCVUKF methods compared with several related state-of-the-art Kalman filter methods.
Measurement data cleaning is a key step of edge computing in a distribution network; it is beneficial to improve the state perception and regional autonomy level of a distribution network. According to the temporal and spatial correlation of measurement data in the distribution network, a joint cleaning method of measurement data in a distribution network is proposed based on the correntropy criterion with variable center unscented Kalman filter (CC-VC-UKF). Initially, the mean square error (MSE) in the original unscented Kalman filter (UKF) is replaced by the correntropy criterion with variable center (CC-VC) to improve the accuracy of filtering the measurement data in the distribution network with a non-Gaussian non-zero mean measurement deviation. Then, the measured data of different measuring devices located on the same section of the line are filtered based on the CC-VC-UKF algorithm according to their respective reference time series to improve the signal-to-noise ratio of the measured data. Then, the filtered measured data are filtered and cleaned based on the CC-VC-UKF algorithm according to the space–time joint filtering and cleaning technology. Finally, the method is used to test the measurement data of the distribution network obtained by a power supply company in a city in north China to solve the problem of measurement deviation caused by the existence of space distance. Results show that this method can obtain FTU measurement data with higher precision from network topology based on the filtered TTU measurement data through the media of filtered spatial measurement deviation.
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