Unscented Trainable Kalman Filter Based on Deep Learning Method Considering Incomplete Information

Rapid changes of states and occurrence of data missing in power systems cause accurate state estimation very hard. In this paper, an unscented trainable Kalman filter (UTKF) with a deep learning prediction model is proposed to provide accurate state estimation under incomplete information. First, th...

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Veröffentlicht in:	IEEE access 2023-01, Vol.11, p.1-1
Hauptverfasser:	Yu, Yanjie, Li, Qiang, Zhang, Houyi
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Data loss Deep learning Dynamic state estimation Estimation Kalman filters Missing data missing data filling Power measurement Power system dynamics Power systems prediction model Prediction models Predictive models Root-mean-square errors State estimation unscented Kalman filter
Online-Zugang:	Volltext
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