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author:

Lin, J. (Lin, J..) [1] | Tu, M. (Tu, M..) [2] | Hong, H. (Hong, H..) [3] | Lu, C. (Lu, C..) [4] | Song, W. (Song, W..) [5]

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Scopus

Abstract:

Power system state estimation is a primary and major method for monitoring power grids in real time. Massive synchrophasor data contains temporal correlations and spatial characteristics based on the physical constraints of the power system. The spectral-domain convolution method based on the graph Fourier transform is used to construct a multilayer graph convolution neural network model to predict the short-term states of a power system, including the latest state when the power system is in the quasi-steady state. Combining the advantages of linear state estimation, a forecasting-aided state estimation method that can take advantage of predicted values and phase measurement units is designed to obtain the real-time state. Furthermore, predicted innovations analysis method are proposed to identify system mutations and bad data. Enough simulation tests validate that the proposed method can accurately estimate the real-time state of a power system. IEEE

Keyword:

Convolution graph convolution neural network innovations Kalman filters phase measurement units Phasor measurement units Power measurement Power system dynamics power system forecasting-aided state estimation Power systems State estimation synchrophasors

Community:

  • [ 1 ] [Lin J.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China
  • [ 2 ] [Tu M.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China
  • [ 3 ] [Hong H.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China
  • [ 4 ] [Lu C.]Department of Electrical Engineering, Tsinghua University, Beijing, China
  • [ 5 ] [Song W.]Department of Electrical Engineering, Tsinghua University, Beijing, China

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Source :

IEEE Internet of Things Journal

ISSN: 2327-4662

Year: 2024

Issue: 9

Volume: 11

Page: 1-1

8 . 2 0 0

JCR@2023

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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