Causality and Interpretability for Electrical Distribution System faults

• URL: http://arxiv.org/abs/2508.02524v1
• Date: Mon, 04 Aug 2025 15:35:08 GMT
• Title: Causality and Interpretability for Electrical Distribution System faults
• Authors: Karthik Peddi, Sai Ram Aditya Parisineni, Hemanth Macharla, Mayukha Pal,
• Abstract summary: We present a new method that combines causal inference with machine learning to classify faults in electrical distribution systems.<n>Our experiments show high accuracy: 99.44% on the EDS fault dataset, which is better than state of art models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Causal analysis helps us understand variables that are responsible for system failures. This improves fault detection and makes system more reliable. In this work, we present a new method that combines causal inference with machine learning to classify faults in electrical distribution systems (EDS) using graph-based models. We first build causal graphs using transfer entropy (TE). Each fault case is represented as a graph, where the nodes are features such as voltage and current, and the edges demonstrate how these features influence each other. Then, the graphs are classified using machine learning and GraphSAGE where the model learns from both the node values and the structure of the graph to predict the type of fault. To make the predictions understandable, we further developed an integrated approach using GNNExplainer and Captums Integrated Gradients to highlight the nodes (features) that influences the most on the final prediction. This gives us clear insights into the possible causes of the fault. Our experiments show high accuracy: 99.44% on the EDS fault dataset, which is better than state of art models. By combining causal graphs with machine learning, our method not only predicts faults accurately but also helps understand their root causes. This makes it a strong and practical tool for improving system reliability.

Related papers

• Causal Discovery in Recommender Systems: Example and Discussion [3.013819656993265]
  Causality is receiving increasing attention by the artificial intelligence and machine learning communities. This paper gives an example of modelling a recommender system problem using causal graphs.
  arXiv  Detail & Related papers  (2024-09-16T13:31:04Z)
• Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency [48.769884734826974]
  We build a scalable and flexible method to evaluate if two variables are adjacent in a causal graph. The Differentiable Adjacency Test replaces an exponential number of tests with a provably equivalent relaxed problem. We also build a graph learning method based on DAT, DAT-Graph, that can also learn from data with interventions.
  arXiv  Detail & Related papers  (2024-06-13T14:39:40Z)
• GIF: A General Graph Unlearning Strategy via Influence Function [63.52038638220563]
  Graph Influence Function (GIF) is a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $epsilon$-mass perturbation in deleted data. We conduct extensive experiments on four representative GNN models and three benchmark datasets to justify GIF's superiority in terms of unlearning efficacy, model utility, and unlearning efficiency.
  arXiv  Detail & Related papers  (2023-04-06T03:02:54Z)
• CLEAR: Generative Counterfactual Explanations on Graphs [60.30009215290265]
  We study the problem of counterfactual explanation generation on graphs. A few studies have explored counterfactual explanations on graphs, but many challenges of this problem are still not well-addressed. We propose a novel framework CLEAR which aims to generate counterfactual explanations on graphs for graph-level prediction models.
  arXiv  Detail & Related papers  (2022-10-16T04:35:32Z)
• Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure [46.86463923605841]
  We present a graph classification investigation on the training graphs with severe bias. We discover that GNNs always tend to explore the spurious correlations to make decision. We propose a general disentangled GNN framework to learn the causal substructure and bias substructure.
  arXiv  Detail & Related papers  (2022-09-28T13:55:52Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• Graph Self-supervised Learning with Accurate Discrepancy Learning [64.69095775258164]
  We propose a framework that aims to learn the exact discrepancy between the original and the perturbed graphs, coined as Discrepancy-based Self-supervised LeArning (D-SLA) We validate our method on various graph-related downstream tasks, including molecular property prediction, protein function prediction, and link prediction tasks, on which our model largely outperforms relevant baselines.
  arXiv  Detail & Related papers  (2022-02-07T08:04:59Z)
• Distributionally Robust Semi-Supervised Learning Over Graphs [68.29280230284712]
  Semi-supervised learning (SSL) over graph-structured data emerges in many network science applications. To efficiently manage learning over graphs, variants of graph neural networks (GNNs) have been developed recently. Despite their success in practice, most of existing methods are unable to handle graphs with uncertain nodal attributes. Challenges also arise due to distributional uncertainties associated with data acquired by noisy measurements. A distributionally robust learning framework is developed, where the objective is to train models that exhibit quantifiable robustness against perturbations.
  arXiv  Detail & Related papers  (2021-10-20T14:23:54Z)
• Uncertainty Weighted Causal Graphs [0.0]
  Causality has traditionally been a scientific way to generate knowledge by relating causes to effects. We will attempt to go a step further modelling the uncertainty in the graph through probabilistic improving the management of the imprecision in the quoted graph.
  arXiv  Detail & Related papers  (2020-02-02T16:32:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.