Graph Neural Network-Based Distribution System State Estimators

Madbhavi, Rahul; Natarajan, Balasubramaniam; Srinivasan, Babji

doi:10.1109/tii.2023.3248082

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…PGNN [27,28] [28]: Network topology as physics knowledge DRL [34,35] [34]: Adapt to topology changes and DER uncertainty State Estimation /Load Monitoring GL [36][37][38][39] [36]: Two-layer framework of GCN and GRU [37]: Simultaneously captures spatiotemporal correlation of data [38]: Pseudo-measurements are generated via GNN [39]: Low pass property of voltage phasors are utilized to reconstruct network TL [40][41][42] [40]: A temporal convolutional network is developed to learn the dynamic features of individual appliance load [42]: Privacy-preserving TL for non-intrusive load monitoring PGNN [43][44][45] [43]: PMU allocation are optimized and embedded into NN design [44]: Jacobian matrix as physics knowledge to construct the loss function [45] [62]: The backward/forward sweep distribution flow formulation is used to construct the graph learning [63]: Kron reduction is implemented to reduce computational complexity…”

Section: Research Topic Algorithm Comments and Keynotes For Selected ...mentioning

confidence: 99%

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Artificial Intelligence Applications in Electric Distribution Systems: Post-Pandemic Progress and Prospect

Chung

Zhang

2023

Applied Sciences

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Advances in machine learning and artificial intelligence (AI) techniques bring new opportunities to numerous intractable tasks for operation and control in modern electric distribution systems. Nevertheless, AI applications for such grids as cyber-physical systems encounter multifaceted challenges, e.g., high requirements for the quality and quantity of training data, data efficiency, physical inconsistency, interpretability, and privacy concerns. This paper provides a systematic overview of the state-of-the-art AI methodologies in the post-pandemic era, represented by transfer learning, deep attention mechanism, graph learning, and their combination with reinforcement learning and physics-guided neural networks. Dedicated research efforts on harnessing such recent advances, including power flow, state estimation, voltage control, topology identification, and line parameter calibration, are categorized and investigated in detail. Revolving around the characteristics of distribution system operation and integration of distributed energy resources, this paper also illuminates prospects and challenges typified by the privacy, explainability, and interpretability of such AI applications in smart grids. Finally, this paper attempts to shed light on the deeper and broader prospects in the realm of smart distribution grids by interoperating them with smart building and transportation electrification

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Section: Research Topic Algorithm Comments and Keynotes For Selected ...mentioning

confidence: 99%

“…To address this issue, ref. [38] leverages the inter-dependencies of nodes learned by the GNN architecture to generate pseudo-measurements to achieve accurate estimations with lower computational times. Similarly, ref.…”

Section: State Estimation/load Monitoringmentioning

confidence: 99%

Artificial Intelligence Applications in Electric Distribution Systems: Post-Pandemic Progress and Prospect

Chung

Zhang

2023

Applied Sciences

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“…Despite the increasing development of GNNs applications in power systems and growing research on deep learning for DSSE, the literature on GNN for DSSE is limited to parallel works [36], [37] In [36], an electrical-model-guided GNN is used to perform DSSE and compared to conventional methods and other machine learning techniques. This approach demonstrated higher accuracy and robustness, indicating the potential of GNN-based approaches.…”

Section: Introductionmentioning

confidence: 99%

“…This approach demonstrated higher accuracy and robustness, indicating the potential of GNN-based approaches. In [37], a GNN model is combined with matrix completion techniques to perform DSSE without the need for a detailed system model, highlighting the robustness of GNN approaches to model inaccuracies. While these approaches show promising results, they rely on labeled data for training, which is impractical due to the limited observability of the system's state.…”

Section: Introductionmentioning

confidence: 99%

Deep Statistical Solver for Distribution System State Estimation

Habib

Isufi

Breda³

et al. 2024

IEEE Trans. Power Syst.

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Implementing accurate Distribution System State Estimation (DSSE) faces several challenges, among which the lack of observability and the high density of the distribution system. While data-driven alternatives based on Machine Learning models could be a choice, they suffer in DSSE because of the lack of labeled data. In fact, measurements in the distribution system are often noisy, corrupted, and unavailable. To address these issues, we propose the Deep Statistical Solver for Distribution System State Estimation (DSS 2 ), a deep learning model based on graph neural networks (GNNs) that accounts for the network structure of the distribution system and the governing power flow equations of the problem. DSS 2 is based on GNN and leverages hypergraphs to model the network as a graph into the deep-learning algorithm and to represent the heterogeneous components of the distribution systems. A weakly supervised learning approach is put forth to train the DSS 2 : by enforcing the GNN output into the power flow equations, we force the DSS 2 to respect the physics of the distribution system. This strategy enables learning from noisy measurements and alleviates the need for ideal labeled data. Extensive experiments with case studies on the IEEE 14-bus, 70-bus, and 179-bus networks showed the DSS 2 outperforms the conventional Weighted Least Squares algorithm in accuracy, convergence, and computational time while being more robust to noisy, erroneous, and missing measurements. The DSS 2 achieves a competing, yet lower, performance compared with the supervised models that rely on the unrealistic assumption of having all the true labels.

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“…1). These include social network analysis [40], molecular and drug discovery [41], bioinformatics [42], computer vision [43], language processing [44], materials science, and chemistry [45], as well as more recent advances in internet of things [46,47], energy systems [48,49], intelligent transportation systems [50,51], power systems [52], wireless networks [53], and communication systems [54].…”

Section: Introductionmentioning

confidence: 99%

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

Li,

Xue,

Zargari

et al. 2023

IEEE Access

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Graph theory within power electronics, developed over a 50-year span, is continually evolving, necessitating ongoing research endeavors. Facing with the never-been-seen explosion of graphstructured data, the state-of-the-art deep learning technique-Graph Neural Networks (GNNs), becomes the leading trend in machine learning within just recent five years and demonstrated surprisingly broad and prominent benefits covering from new drug discovery to better IC design. However, its promising applications in Power Electronics are still rarely discussed and its full potential remains unexplored. Addressing this gap, this review paper is the first to outline GNNs' general workflow in power electronics, laying the groundwork and examining current GNN methodologies within the field. To bridge the gap in the sparse GNN literature within this domain, we also provide extended discussions on leveraging insights from GNN-aided circuit design to enrich power electronics research. Our work includes in-depth GNNbased case studies that demonstrate promising applications from converters to system-level power electronics, showcasing GNNs' unique benefits and untapped possibilities (e.g., accurate component design, voltage predictions on IEEE-13 bus and 118 bus systems). Additionally, we provide a comprehensive survey of GNNs' latest and successful applications, emphasizing their impact on energy-centric sectors, such as transportation electrification, smart grids. Considering the interdisciplinary nature of power electronics in modern energy systems, our review highlights the potential of GNNs emerge as a promising tool to decode the intricate behavior and dynamics of power electronics systems, and we hope such synergies between advanced AI methodologies like GNNs with the ever-evolving graph theory can lead to more powerful tools, novel methodologies, and advancements in the power electronics community.

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Graph Neural Network-Based Distribution System State Estimators

Cited by 10 publications

References 22 publications

Artificial Intelligence Applications in Electric Distribution Systems: Post-Pandemic Progress and Prospect

Artificial Intelligence Applications in Electric Distribution Systems: Post-Pandemic Progress and Prospect

Deep Statistical Solver for Distribution System State Estimation

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

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