Hybrid Physical-Data Driven Model for Denoising of Generator State Measurements

The measurement data of power systems are often mixed with a lot of noise due to the interference of the external environment. In order to eliminate the effect of noise, it is significant to denoise the noisy data to obtain the real state measurements. In order to deal with the problem of insufficie...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 74; S. 1 - 12
Hauptverfasser:	Wang, Huaiyuan, Zhang, Shiping, Liu, Baojin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Constraints Data recovery deep learning (DL) Differential equations Generators Machine learning Neural networks Noise Noise measurement Noise reduction Phasor measurement units physics-informed neural networks (PINNs) Pollution measurement Power measurement power system Power system stability stacked denoising autoencoder (SDAE) Training
ISSN:	0018-9456, 1557-9662
Online-Zugang:	Volltext
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