Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment

• URL: http://arxiv.org/abs/2505.06207v1
• Date: Fri, 09 May 2025 17:36:59 GMT
• Title: Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment
• Authors: Muhy Eddin Za'ter, Amir Sajad, Bri-Mathias Hodge,
• Abstract summary: This paper introduces a novel approach to the power system security assessment using Multi-Task Learning (MTL)<n>The proposed MTL framework simultaneously assesses static, voltage, transient, and small-signal stability.<n>It consists of a shared encoder and multiple decoders, enabling knowledge transfer between stability tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel approach to the power system security assessment using Multi-Task Learning (MTL), and reformulating the problem as a multi-label classification task. The proposed MTL framework simultaneously assesses static, voltage, transient, and small-signal stability, improving both accuracy and interpretability with respect to the most state of the art machine learning methods. It consists of a shared encoder and multiple decoders, enabling knowledge transfer between stability tasks. Experiments on the IEEE 68-bus system demonstrate a measurable superior performance of the proposed method compared to the extant state-of-the-art approaches.

Related papers

• Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation [61.64052577026623]
  Real-world multi-view datasets are often heterogeneous and imperfect.<n>We propose a novel robust MVL method (namely RML) with simultaneous representation fusion and alignment.<n>In experiments, we employ it in unsupervised multi-view clustering, noise-label classification, and as a plug-and-play module for cross-modal hashing retrieval.
  arXiv  Detail & Related papers  (2025-03-06T07:01:08Z)
• Multi-QuAD: Multi-Level Quality-Adaptive Dynamic Network for Reliable Multimodal Classification [57.08108545219043]
  Existing reliable multimodal classification methods fail to provide robust estimation of data quality.<n>New framework for reliable classification termed textitMulti-level Quality-Adaptive Dynamic multimodal network (Multi-QuAD) is proposed.<n>Experiments conducted on four datasets demonstrate that Multi-QuAD significantly outperforms state-of-the-art methods in classification performance and reliability.
  arXiv  Detail & Related papers  (2024-12-19T03:26:51Z)
• Semi-Supervised Multi-Task Learning Based Framework for Power System Security Assessment [0.0]
  This paper develops a novel machine learning-based framework using Semi-Supervised Multi-Task Learning (SS-MTL) for power system dynamic security assessment. The learning algorithm underlying the proposed framework integrates conditional masked encoders and employs multi-task learning for classification-aware feature representation. Various experiments on the IEEE 68-bus system were conducted to validate the proposed method.
  arXiv  Detail & Related papers  (2024-07-11T22:42:53Z)
• Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map [33.929818014940054]
  We introduce a novel data-driven deep reinforcement learning (DRL) approach to handle multiple power flow adjustment tasks jointly. At the heart of the proposed method is a multi-task attribution map (MAM), which enables the DRL agent to explicitly attribute each transmission interface task to different power system nodes. Based on this MAM, the agent can further provide effective strategies to solve the multi-task adjustment problem with a near-optimal operation cost.
  arXiv  Detail & Related papers  (2024-05-24T08:20:53Z)
• Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study [17.555311732095483]
  The FMTL approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL) This paper introduces a novel framework, FMTL-Bench, for systematic evaluation of the FMTL paradigm.
  arXiv  Detail & Related papers  (2024-02-20T10:13:44Z)
• Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement [69.51496713076253]
  In this paper, we focus on the aforementioned efficiency aspects of existing MTL methods. We first carry out large-scale experiments of the methods with smaller backbones and on a the MetaGraspNet dataset as a new test ground. We also propose Feature Disentanglement measure as a novel and efficient identifier of the challenges in MTL.
  arXiv  Detail & Related papers  (2024-02-05T22:15:55Z)
• Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs [65.42104819071444]
  Multitask learning (MTL) leverages task-relatedness to enhance performance. We employ high-order tensors, with each mode corresponding to a task index, to naturally represent tasks referenced by multiple indices. We propose a general framework of low-rank MTL methods with tensorized support vector machines (SVMs) and least square support vector machines (LSSVMs)
  arXiv  Detail & Related papers  (2023-08-30T14:28:26Z)
• Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach [38.76462300149459]
  We develop a Multi-objective Correction (MoCo) method for multi-objective gradient optimization. The unique feature of our method is that it can guarantee convergence without increasing the non fairness gradient.
  arXiv  Detail & Related papers  (2022-10-23T05:54:26Z)
• Joint Differentiable Optimization and Verification for Certified Reinforcement Learning [91.93635157885055]
  In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties. We propose a framework that jointly conducts reinforcement learning and formal verification.
  arXiv  Detail & Related papers  (2022-01-28T16:53:56Z)
• Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning [72.28520951105207]
  Overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning. We propose a novel regularization-based update scheme that penalizes large joint action-values deviating from a baseline. We show that our method provides a consistent performance improvement on a set of challenging StarCraft II micromanagement tasks.
  arXiv  Detail & Related papers  (2021-03-22T14:18:39Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)

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.