PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning

• URL: http://arxiv.org/abs/2308.03953v2
• Date: Sun, 27 Aug 2023 11:27:13 GMT
• Title: PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning
• Authors: Yang Li, Shitu Zhang, Yuanzheng Li, Jiting Cao, Shuyue Jia
• Abstract summary: This paper proposes a novel phasor measurement unit (PMU) measurements-based STVSA method by using deep transfer learning. It employs temporal ensembling for sample labeling and utilizes least squares generative adversarial networks (LSGAN) for data augmentation, enabling effective deep learning on small-scale datasets. Experimental results on the IEEE 39-bus test system demonstrate that the proposed method improves model evaluation accuracy by approximately 20% through transfer learning.
• Score: 2.1303885995425635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has emerged as an effective solution for addressing the challenges of short-term voltage stability assessment (STVSA) in power systems. However, existing deep learning-based STVSA approaches face limitations in adapting to topological changes, sample labeling, and handling small datasets. To overcome these challenges, this paper proposes a novel phasor measurement unit (PMU) measurements-based STVSA method by using deep transfer learning. The method leverages the real-time dynamic information captured by PMUs to create an initial dataset. It employs temporal ensembling for sample labeling and utilizes least squares generative adversarial networks (LSGAN) for data augmentation, enabling effective deep learning on small-scale datasets. Additionally, the method enhances adaptability to topological changes by exploring connections between different faults. Experimental results on the IEEE 39-bus test system demonstrate that the proposed method improves model evaluation accuracy by approximately 20% through transfer learning, exhibiting strong adaptability to topological changes. Leveraging the self-attention mechanism of the Transformer model, this approach offers significant advantages over shallow learning methods and other deep learning-based approaches.

Related papers

• iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System [24.898026682692688]
  This study explores advanced classification techniques leveraging interpretable fuzzy transfer learning (iFuzzyTL) iFuzzyTL refines input signal processing and classification in a human-interpretable format by integrating fuzzy inference systems and attention mechanisms. The model's efficacy is demonstrated across three datasets.
  arXiv  Detail & Related papers  (2024-10-16T06:07:23Z)
• Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration [74.09687562334682]
  We introduce a novel training data attribution method called Debias and Denoise Attribution (DDA) Our method significantly outperforms existing approaches, achieving an averaged AUC of 91.64%. DDA exhibits strong generality and scalability across various sources and different-scale models like LLaMA2, QWEN2, and Mistral.
  arXiv  Detail & Related papers  (2024-10-02T07:14:26Z)
• 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)
• An Adaptive Task-Related Component Analysis Method for SSVEP recognition [0.913755431537592]
  Steady-state visual evoked potential (SSVEP) recognition methods are equipped with learning from the subject's calibration data. This study develops a new method to learn from limited calibration data.
  arXiv  Detail & Related papers  (2022-04-17T15:12:40Z)
• A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems [9.299576471941753]
  This paper proposes a novel deep-learning intelligent system incorporating data augmentation for STVSA of power systems. Due to the unavailability of reliable quantitative criteria to judge the stability status for a specific power system, semi-supervised cluster learning is leveraged to obtain labeled samples. conditional least squares generative adversarial networks (LSGAN)-based data augmentation is introduced to expand the original dataset.
  arXiv  Detail & Related papers  (2021-12-05T11:40:54Z)
• Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery [14.44961822756759]
  We propose a novel data-driven, real-time power system voltage control method based on the physics-informed guided meta evolutionary strategy (ES) The main objective is to quickly provide an adaptive control strategy to mitigate the fault-induced delayed voltage recovery (FIDVR) problem.
  arXiv  Detail & Related papers  (2021-11-29T07:12:40Z)
• Benchmarking Detection Transfer Learning with Vision Transformers [60.97703494764904]
  complexity of object detection methods can make benchmarking non-trivial when new architectures, such as Vision Transformer (ViT) models, arrive. We present training techniques that overcome these challenges, enabling the use of standard ViT models as the backbone of Mask R-CNN. Our results show that recent masking-based unsupervised learning methods may, for the first time, provide convincing transfer learning improvements on COCO.
  arXiv  Detail & Related papers  (2021-11-22T18:59:15Z)
• Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression [1.5039745292757671]
  We propose an approach to detect the dataset shifts effectively for regression problems. Our approach is based on the inductive conformal anomaly detection and utilizes a variational autoencoder for regression model. We demonstrate our approach by using an advanced emergency braking system implemented in an open-source simulator for self-driving cars.
  arXiv  Detail & Related papers  (2021-04-14T03:46:37Z)
• Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data [77.88594632644347]
  Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge. We propose a novel momentum-based method to mitigate this decentralized training difficulty.
  arXiv  Detail & Related papers  (2021-02-09T11:27:14Z)
• Adaptive Gradient Method with Resilience and Momentum [120.83046824742455]
  We propose an Adaptive Gradient Method with Resilience and Momentum (AdaRem) AdaRem adjusts the parameter-wise learning rate according to whether the direction of one parameter changes in the past is aligned with the direction of the current gradient. Our method outperforms previous adaptive learning rate-based algorithms in terms of the training speed and the test error.
  arXiv  Detail & Related papers  (2020-10-21T14:49:00Z)
• On Robustness and Transferability of Convolutional Neural Networks [147.71743081671508]
  Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. We study the interplay between out-of-distribution and transfer performance of modern image classification CNNs for the first time. We find that increasing both the training set and model sizes significantly improve the distributional shift robustness.
  arXiv  Detail & Related papers  (2020-07-16T18:39:04Z)

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.