A Review of Graph Neural Networks and Their Applications in Power Systems
By Wenlong Liao, B. Bak‐Jensen, Jayakrishnan Radhakrishna Pillai, Yuelong Wang, Yusen Wang. Journal of Modern Power Systems and Clean Energy, 2021
373 citations recorded by the source corpus.
Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power systems, such as fault scenario application, time series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/2fb16966229a3097598ccdfcc9797efba0b93bb5
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In Artificial Intelligence
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In Electrical and Electronic Engineering
Citations in the corpus
- A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, methodology): “This modeling approach has also been widely adopted in many realworld application sectors with different time series data, including transportation [36], on-demand services [37], [38], energy [39], healthcare [40], [41], economy [42], and other fields [43], [44], [45].”