Searching for Critical Power System Cascading Failures With Graph Convolutional Network

Liu, Yuxiao; Zhang, Ning; Wu, Dan; Botterud, Audun; Yao, Rui; Chen, Kang

doi:10.1109/tcns.2021.3063333

Cited by 31 publications

(12 citation statements)

References 34 publications

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“…Ref. [127] used graph convolutional network (GCN), which can efficiently exploit power system topology, to construct a search model for critical cascading faults. They explain the diagnostic results by LRP and give the contribution of different fault features.…”

Section: Fault Diagnosismentioning

confidence: 99%

Review on Interpretable Machine Learning in Smart Grid

Liao

et al. 2022

Energies

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In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the increase of model complexity, the interpretability of machine learning algorithms is worse. The smart grid is a critical infrastructure area, so machine learning models involving it must be interpretable in order to increase user trust and improve system reliability. Unfortunately, the black-box nature of most machine learning models remains unresolved, and many decisions of intelligent systems still lack explanation. In this paper, we elaborate on the definition, motivations, properties, and classification of interpretability. In addition, we review the relevant literature addressing interpretability for smart grid applications. Finally, we discuss the future research directions of interpretable machine learning in the smart grid.

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Section: Fault Diagnosismentioning

confidence: 99%

Review on Interpretable Machine Learning in Smart Grid

Liao

et al. 2022

Energies

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“…Related work on power grid property prediction Since power grids have an underlying graph structure, the recent development of graph representation learning [Bronstein et al, 2021, Hamilton, 2020 makes it possible to use machine learning for analyzing power grids. There are a number of applications using Graph Neural Networks (GNNs) for different power flow-related tasks [Donon et al, 2019, Kim et al, 2019, Bolz et al, 2019, Retiére et al, 2020, Wang et al, 2020, Owerko et al, 2020, Misyris et al, 2020, Liu et al, 2021 and to predict transient dynamics in microgrids Yu et al [2022]. In [Nauck et al, 2022] small GNNs are used to predict the dynamic stability on small datasets.…”

Section: Introductionmentioning

confidence: 99%

Predicting basin stability of power grids using graph neural networks

et al. 2022

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The prediction of dynamical stability of power grids becomes more important and challenging with increasing shares of renewable energy sources due to their decentralized structure, reduced inertia and volatility. We investigate the feasibility of applying graph neural networks (GNN) to predict dynamic stability of synchronisation in complex power grids using the single-node basin stability (SNBS) as a measure. To do so, we generate two synthetic datasets for grids with 20 and 100 nodes respectively and estimate SNBS using Monte-Carlo sampling. Those datasets are used to train and evaluate the performance of eight different GNN-models. All models use the full graph without simplifications as input and predict SNBS in a nodal-regression-setup. We show that SNBS can be predicted in general and the performance significantly changes using different GNN-models. Furthermore, we observe interesting transfer capabilities of our approach: GNN-models trained on smaller grids can directly be applied on larger grids without the need of retraining.

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“…GCN demonstrates good classification and prediction capability with the graphstructured data in power systems [19], [20]. For example, Yuxiao Liu et al [21] developed an interpretable GCN to guide cascading failure search efficiently. Nevertheless, the GCN is not adept at capturing the sequential characteristics, i.e., the temporal information of time series of power system dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…ACC increases sharply to 98% within 20 epochs.TableIshows the performance of the transient prediction under the single-node perturbations for the IEEE 39-bus and IEEE 118-bus power systems. The test performance under the single-node test dataset shows that the TTEDNN model outperforms all the existing deep learning methods including CNN[44], Attention-CNN[45], GCN[21], Graph Attention Network (GAT)[46] and Node-Level GCN (NGCN)[47]. The TTEDNN model has the best performance in terms of the ACC of 99.38% and the AUC of 0.9994 for the IEEE 39bus power system, and the ACC of 99.88% and the AUC of 0.9999 for IEEE 118-bus power system.…”

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confidence: 97%

Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network

Sun¹,

Huo²,

Liang³

et al. 2022

Preprint

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Transient stability prediction is critically essential to the fast online assessment and maintaining the stable operation in power systems. The wide deployment of phasor measurement units (PMUs) promotes the development of datadriven approaches for transient stability assessment. This paper proposes the temporal and topological embedding deep neural network (TTEDNN) model to forecast transient stability with the early transient dynamics. The TTEDNN model can accurately and efficiently predict the transient stability by extracting the temporal and topological features from the time-series data of the early transient dynamics. The grid-informed adjacency matrix is used to incorporate the power grid structural and electrical parameter information. The transient dynamics simulation environments under the single-node and multiple-node perturbations are used to test the performance of the TTEDNN model for the IEEE 39-bus and IEEE 118-bus power systems. The results show that the TTEDNN model has the best and most robust prediction performance. Furthermore, the TTEDNN model also demonstrates the transfer capability to predict the transient stability in the more complicated transient dynamics simulation environments.

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Searching for Critical Power System Cascading Failures With Graph Convolutional Network

Cited by 31 publications

References 34 publications

Review on Interpretable Machine Learning in Smart Grid

Review on Interpretable Machine Learning in Smart Grid

Predicting basin stability of power grids using graph neural networks

Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network

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