Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network
Author:
Funder
National Natural Science Foundation of China
Publisher
Elsevier BV
Subject
Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering
Reference41 articles.
1. An artificial neural network-based condition monitoring method for wind turbines with application to the monitoring of the gearbox;Bangalore;Wind Energy,2017
2. Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management;Black;Int. J. Sustain. Energy,2021
3. Control chart monitoring of wind turbine generators using the statistical inertia of a wind farm average;Cambron;Renew. Energy,2018
4. Damage detection in operational wind turbine blades using a new approach based on machine learning;Chandrasekhar;Renew. Energy,2021
5. Graph signal processing and deep learning: Convolution pooling, and topology;Cheung;IEEE Signal Process. Mag.,2020
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