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Register a new userGuiding Cascading Failure Search with Interpretable Graph Convolutional Network
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Power system cascading failures become more time variant and complex because of the increasing network interconnection and higher renewable energy penetration. High computational cost is the main obstacle for a more frequent online cascading failure search, which is essential to improve system security. In this work, we show that the complex mechanism of cascading failures can be well captured by training a graph convolutional network (GCN) offline. Subsequently, the search of cascading failures can be significantly accelerated with the aid of the trained GCN model. We link the power network topology with the structure of the GCN, yielding a smaller parameter space to learn the complex mechanism. We further enable the interpretability of the GCN model by a layer-wise relevance propagation (LRP) algorithm. The proposed method is tested on both the IEEE RTS-79 test system and Chinas Henan Province power system. The results show that the GCN guided method can not only accelerate the search of cascading failures, but also reveal the reasons for predicting the potential cascading failures.
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The significant imbalance between power generation and load caused by severe disturbance may make the power system unable to maintain a steady frequency. If the post-disturbance dynamic frequency features can be predicted and emergency controls are appropriately taken, the risk of frequency instability will be greatly reduced. In this paper, a predictive algorithm for post-disturbance dynamic frequency features is proposed based on convolutional neural network (CNN) . The operation data before and immediately after disturbance is used to construct the input tensor data of CNN, with the dynamic frequency features of the power system after the disturbance as the output. The operation data of the power system such as generators unbalanced power has spatial distribution characteristics. The electrical distance is presented to describe the spatial correlation of power system nodes, and the t-SNE dimensionality reduction algorithm is used to map the high-dimensional distance information of nodes to the 2-D plane, thereby constructing the CNN input tensor to reflect spatial distribution of nodes operation data on 2-D plane. The CNN with deep network structure and local connectivity characteristics is adopted and the network parameters are trained by utilizing the backpropagation-gradient descent algorithm. The case study results on an improved IEEE 39-node system and an actual power grid in USA shows that the proposed method can predict the lowest frequency of power system after the disturbance accurately and quickly.
Transmission line failures in power systems propagate and cascade non-locally. This well-known yet counter-intuitive feature makes it even more challenging to optimally and reliably operate these complex networks. In this work we present a comprehensive framework based on spectral graph theory that fully and rigorously captures how multiple simultaneous line failures propagate, distinguishing between non-cut and cut set outages. Using this spectral representation of power systems, we identify the crucial graph sub-structure that ensures line failure localization -- the network bridge-block decomposition. Leveraging this theory, we propose an adaptive network topology reconfiguration paradigm that uses a two-stage algorithm where the first stage aims to identify optimal clusters using the notion of network modularity and the second stage refines the clusters by means of optimal line switching actions. Our proposed methodology is illustrated using extensive numerical examples on standard IEEE networks and we discussed several extensions and variants of the proposed algorithm.
This paper presents a novel mutual information (MI) matrix based method for fault detection. Given a $m$-dimensional fault process, the MI matrix is a $m times m$ matrix in which the $(i,j)$-th entry measures the MI values between the $i$-th dimension and the $j$-th dimension variables. We introduce the recently proposed matrix-based Renyis $alpha$-entropy functional to estimate MI values in each entry of the MI matrix. The new estimator avoids density estimation and it operates on the eigenspectrum of a (normalized) symmetric positive definite (SPD) matrix, which makes it well suited for industrial process. We combine different orders of statistics of the transformed components (TCs) extracted from the MI matrix to constitute the detection index, and derive a simple similarity index to monitor the changes of characteristics of the underlying process in consecutive windows. We term the overall methodology projections of mutual information matrix (PMIM). Experiments on both synthetic data and the benchmark Tennessee Eastman process demonstrate the interpretability of PMIM in identifying the root variables that cause the faults, and its superiority in detecting the occurrence of faults in terms of the improved fault detection rate (FDR) and the lowest false alarm rate (FAR). The advantages of PMIM is also less sensitive to hyper-parameters. The advantages of PMIM is also less sensitive to hyper-parameters. Code of PMIM is available at https://github.com/SJYuCNEL/Fault_detection_PMIM
In this paper, we study the self-healing problem of unmanned aerial vehicle (UAV) swarm network (USNET) that is required to quickly rebuild the communication connectivity under unpredictable external disruptions (UEDs). Firstly, to cope with the one-off UEDs, we propose a graph convolutional neural network (GCN) and find the recovery topology of the USNET in an on-line manner. Secondly, to cope with general UEDs, we develop a GCN based trajectory planning algorithm that can make UAVs rebuild the communication connectivity during the self-healing process. We also design a meta learning scheme to facilitate the on-line executions of the GCN. Numerical results show that the proposed algorithms can rebuild the communication connectivity of the USNET more quickly than the existing algorithms under both one-off UEDs and general UEDs. The simulation results also show that the meta learning scheme can not only enhance the performance of the GCN but also reduce the time complexity of the on-line executions.
This paper explores whether graph embedding methods can be used as a tool for analysing the robustness of power-grids within the framework of network science. The paper focuses on the strain elevation tension spring embedding (SETSe) algorithm and compares it to node2vec and Deep Graph Infomax, and the measures mean edge capacity and line load. These five methods are tested on how well they can predict the collapse point of the giant component of a network under random attack. The analysis uses seven power-grid networks, ranging from 14 to 2000 nodes. In total, 3456 load profiles are created for each network by loading the edges of the network to have a range of tolerances and concentrating network capacity into fewer edges. One hundred random attack sequences are generated for each load profile, and the mean number of attacks required for the giant component to collapse for each profile is recorded. The relationship between the embedding values for each load profile and the mean collapse point is then compared across all five methods. It is found that only SETSe and line load perform well as proxies for robustness with $R^2 = 0.89$ for both measures. When tested on a time series normal operating conditions line load performs exceptionally well ($R=0.99$). However, the SETSe algorithm provides valuable qualitative insight into the state of the power-grid by leveraging its method local smoothing and global weighting of node features to provide an interpretable geographical embedding. This paper shows that graph representation algorithms can be used to analyse network properties such as robustness to cascading failure attacks, even when the network is embedded at node level.
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