A Review of Graph Neural Networks and Their Applications in Power Systems (2101.10025v2)

Abstract: 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.

• The paper presents a comprehensive review of GNN models applied to power systems, demonstrating improved fault diagnosis and power flow efficiency.
• The study systematically compares various GNN architectures, including GCNs, GRNNs, GATs, and GGNs, for their roles in data-driven power system analysis.
• The review highlights challenges in adapting GNNs for dynamic graph structures and proposes future research to address scalability and deep network issues.

A Review of Graph Neural Networks in Power Systems

The paper "A Review of Graph Neural Networks and Their Applications in Power Systems" by Wenlong Liao et al. presents a comprehensive overview of the application of Graph Neural Networks (GNNs) in the domain of power systems. This work explores various GNN paradigms and their deployment in power system tasks, addressing the inherent complexity of the non-Euclidean data derived from power systems, which often manifests as graph-structured data.

Overview of Graph Neural Networks

Graph Neural Networks represent a novel approach that extends deep neural networks to process graph-structured data, which is common in power systems due to the interconnected nature of nodes and edges. The paper details several classical paradigms of GNN structures, such as Graph Convolutional Networks (GCNs), Graph Recurrent Neural Networks (GRNNs), Graph Attention Networks (GATs), and Graph Generative Networks (GGNs). Each paradigm was outlined with respect to its function and applicable domains, particularly within the framework of power systems.

Key Applications in Power Systems

The paper reviews a variety of applications where GNNs have proven beneficial:

1. Fault Scenario Applications: GNNs are leveraged for fault diagnosis, location, detection, and prediction in power systems. Spectral-based GCNs have been effectively used to improve accuracy in transformer fault diagnosis, while other GNN variants assist in fault localization and isolation. These GNN models represent a leap forward compared to traditional models due to their ability to handle complex interdependencies and leverage graph-structured data more effectively.
2. Power Flow and Optimal Load Shedding: Accurate modeling and prediction of power flows and load shedding are critical for power system operation. GNNs have demonstrated significant improvements in computational efficiency and accuracy for power flow approximation and optimization tasks compared to conventional methods.
3. Time Series Prediction of Renewable Sources: GNNs, particularly the hybrid models combining LSTMs and GCNs, provide enhanced accuracy in predicting power outputs from RES like solar and wind, by effectively capturing spatial-temporal dependencies.
4. Data Generation: GNNs, through generative models, are employed to generate synthetic feeder data and scenarios for training purposes, which are crucial in designing resilient power systems under stochastic conditions.

Implications and Future Directions

The application of GNNs in power systems demonstrates their potential to significantly enhance the analysis, prediction, and control within these systems by effectively utilizing graph data. The paper sheds light on the strengths of various GNN paradigms, pointing to their potential in resolving complex power system issues that classical models struggle with.

However, several challenges remain, particularly relating to the adaptation of GNN architectures for dynamic graph structures and achieving deep network layers without the overhead that typically accompanies traditional deep learning models. Future research could focus on these areas, along with the development of commercially viable applications that leverage the robust predictive power and computational efficiency of GNNs.

Conclusion

This paper underscores the transformative potential of Graph Neural Networks in power systems, offering insights into how they can be applied to complex, interconnected data scenarios prevalent in this domain. Through detailed analysis and comparison, the paper provides a significant contribution to ongoing research efforts seeking to enhance power system operations using advanced machine learning techniques.

This review positions GNNs as a promising area for further development and application, with the expectation that ongoing innovation will overcome current limitations and extend their applicability within the power system and beyond.