Data-Driven Approach for Electromagnetic Transient Recognition Using Transfer Learning

Han Zhang; Wenxia Sima; Ming Yang

doi:10.17775/CSEEJPES.2021.05660

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Data-Driven Approach for Electromagnetic Transient Recognition Using Transfer Learning

Regular Papers | 更新时间：2026-10-16

- Data-Driven Approach for Electromagnetic Transient Recognition Using Transfer Learning
- CSEE Journal of Power and Energy Systems Vol. 11, Issue 5, Pages: 2178-2188(2025)
- 作者机构：
  
  State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University,Chongqing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.05660
  CLC：
- Published：01 September 2025
- 稿件说明：
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Han Zhang, Wenxia Sima, Ming Yang. Data-Driven Approach for Electromagnetic Transient Recognition Using Transfer Learning[J]. CSEE Journal of Power and Energy Systems, 2025, 11(5): 2178-2188.
DOI：

Han Zhang, Wenxia Sima, Ming Yang. Data-Driven Approach for Electromagnetic Transient Recognition Using Transfer Learning[J]. CSEE Journal of Power and Energy Systems, 2025, 11(5): 2178-2188. DOI： 10.17775/CSEEJPES.2021.05660.

摘要

Abstract

Recognition methods of electromagnetic transients (EMT) have been widely used in power systems with the assumption that training and testing data are drawn from the same probability distribution. However

that assumption is hard to satisfy in industrial applications because the distribution of measured EMT testing data generally changes over time. The performance of these methods gradually deteriorates with the distribution shift. The phenomenon limits application of EMT recognition methods. Therefore

this paper proposes a transfer learning-based recognition network (TLRN) for EMT to break the limitation. It consists of a feature extractor

EMT recognizer

domain recognizer

and maximum mean discrepancy (MMD). The feature extractor is constructed to learn features of EMT automatically. The domain recognizer and MMD make features learned by the feature extractor domain invariant. Based on domain invariant features

the EMT recognizer achieves accurate EMT recognition

despite the distribution discrepancy between EMT training and testing data. TLRN maintains satisfactory EMT recognition performance by updating periodically with an unsupervised learning strategy. Using EMT datasets measured from different substations

scenario experiments

and experiment comparisons are conducted

and the recognition performance of the proposed TLRN is demonstrated.

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Related Institution

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