Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids

• URL: http://arxiv.org/abs/2602.10888v1
• Date: Wed, 11 Feb 2026 14:17:43 GMT
• Title: Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids
• Authors: Marc Gillioz, Guillaume Dubuis, Étienne Voutaz, Philippe Jacquod,
• Abstract summary: We apply several machine learning algorithms to the problem of anomaly detection in operational data for large-scale, high-voltage electric power grids.<n>We observe important differences in the performance of the algorithms.<n>We show that unsupervised learning algorithm work remarkably well and that their predictions are robust against simultaneous, concurring anomalies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We apply several machine learning algorithms to the problem of anomaly detection in operational data for large-scale, high-voltage electric power grids. We observe important differences in the performance of the algorithms. Neural networks typically outperform classical algorithms such as k-nearest neighbors and support vector machines, which we explain by the strong contextual nature of the anomalies. We show that unsupervised learning algorithm work remarkably well and that their predictions are robust against simultaneous, concurring anomalies.

Related papers

• Towards model-free RL algorithms that scale well with unstructured data [1.3799571823220778]
  We provide an algorithm that constructs reward-relevant general value function questions to find and exploit predictive structure directly from the experience stream. The proposed algorithm reliably outperforms a conventional deep RL algorithm on these scaling problems.
  arXiv  Detail & Related papers  (2023-11-03T20:03:54Z)
• Counting and Algorithmic Generalization with Transformers [0.0]
  We show that standard Transformers are based on architectural decisions that hinder out-of-distribution performance. We demonstrate that a modified transformer can exhibit a good algorithmic generalization performance on counting.
  arXiv  Detail & Related papers  (2023-10-12T18:39:24Z)
• Computing large deviation prefactors of stochastic dynamical systems based on machine learning [4.474127100870242]
  We present large deviation theory that characterizes the exponential estimate for rare events of dynamical systems in the limit of weak noise. We design a neural network framework to compute quasipotential, most probable paths and prefactors based on the decomposition of vector field. Numerical experiments demonstrate its powerful function in exploring internal mechanism of rare events triggered by weak random fluctuations.
  arXiv  Detail & Related papers  (2023-06-20T09:59:45Z)
• Dual Algorithmic Reasoning [9.701208207491879]
  We propose to learn algorithms by exploiting duality of the underlying algorithmic problem. We demonstrate that simultaneously learning the dual definition of these optimisation problems in algorithmic learning allows for better learning. We then validate the real-world utility of our dual algorithmic reasoner by deploying it on a challenging brain vessel classification task.
  arXiv  Detail & Related papers  (2023-02-09T08:46:23Z)
• On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network [55.56019538079826]
  Bilevel optimization has been applied to a wide variety of machine learning models. Most existing algorithms restrict their single-machine setting so that they are incapable of handling distributed data. We develop novel decentralized bilevel optimization algorithms based on a gradient tracking communication mechanism and two different gradients.
  arXiv  Detail & Related papers  (2022-06-30T05:29:52Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• Sampling Strategies for Static Powergrid Models [0.0]
  Power flow calculation is an iterative method to compute the voltage magnitudes of the power grid's buses from power values. Machine learning and, especially, artificial neural networks were successfully used as surrogates for the power flow calculation.
  arXiv  Detail & Related papers  (2022-04-19T11:38:07Z)
• Robustification of Online Graph Exploration Methods [59.50307752165016]
  We study a learning-augmented variant of the classical, notoriously hard online graph exploration problem. We propose an algorithm that naturally integrates predictions into the well-known Nearest Neighbor (NN) algorithm.
  arXiv  Detail & Related papers  (2021-12-10T10:02:31Z)
• Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms [71.62575565990502]
  We prove that the generalization error of an optimization algorithm can be bounded on the complexity' of the fractal structure that underlies its generalization measure. We further specialize our results to specific problems (e.g., linear/logistic regression, one hidden/layered neural networks) and algorithms.
  arXiv  Detail & Related papers  (2021-06-09T08:05:36Z)
• Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork [73.94896986868146]
  Phase retrieval is an important component in modern computational imaging systems. Recent advances in deep learning have opened up a new possibility for robust and fast PR. We develop a novel framework for deep unfolding to overcome the existing limitations.
  arXiv  Detail & Related papers  (2021-01-12T08:36:23Z)
• Spiking Neural Networks Hardware Implementations and Challenges: a Survey [53.429871539789445]
  Spiking Neural Networks are cognitive algorithms mimicking neuron and synapse operational principles. We present the state of the art of hardware implementations of spiking neural networks. We discuss the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level.
  arXiv  Detail & Related papers  (2020-05-04T13:24:00Z)

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.