Generalized load modeling considering random proportion of wind power connected to distribution networks

Qin, Zhen; Luan, Zhaowen; Yang, Shaobin

doi:10.1109/icsgce.2016.7876064

Cited by 2 publications

(1 citation statement)

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“…Estimation models demonstrate the spatial correlation of measurement data. Literature [12] used the correlation between fault remote signaling data to group remote signaling displacement data, and then transform the fault diagnosis problem of remote signaling displacement data into the classification problem of sample data in multidimensional space. Literature [13] proposed a load data repair method based on a collaborative filtering recommendation algorithm, which calculates the load range recommendation degree according to the horizontal correlation of load changes within the distribution network area and realizes rapid data correction for abnormal loads.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Joint Cleaning of Distribution Network Measurement Data Based on Correntropy Criterion with Variable Center Unscented Kalman Filter

Sun

Liu

et al. 2022

Applied Sciences

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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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Section: Introductionmentioning

confidence: 99%