Temporal Graph Convolutional Autoencoder based Fault Detection for Renewable Energy Applications

Detecting faults in energy generation systems is a challenging task due to the complex nature of the system, measurement noise, and outliers. Recently, researchers have shown an increasing interest in using data-driven models that utilize sensor data for fault detection and diagnosis. However, the n...

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Veröffentlicht in:	IEEE International Conference on Industrial Cyber Physical Systems (Online) S. 1 - 6
Hauptverfasser:	Arifeen, Murshedul, Petrovski, Andrei
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 12.05.2024
Schlagworte:	Convolutional codes Fault detection Microgrids Noise Photovoltaic Microgrid Photovoltaic systems Predictive models Renewable energy sources Temporal Graph Convolutional Autoencoder Wind Turbine
ISSN:	2769-3899
Online-Zugang:	Volltext
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