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Detail
Journal Article
Series‐arc‐fault diagnosis using feature fusion‐based deep learning model
- Authors
- Won‐Kyu Choi, Se‐Han Kim, Ji‐Hoon Bae
- Issue Date
- 2024-12
- Citation
- ETRI Journal, v.46, no.6, pp.1061-1074
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- This paper describes the detection of series arc faults, which constitute the major cause of electrical fires, in a power distribution system. Because the characteristics of series arc faults change considerably depending on the load type, their accurate detection and analysis are difficult. We propose a series‐arc‐fault detector that uses a transfer learning (TL)‐based feature fusion model. The model is trained stagewise for various features in the time and frequency domains using a one‐dimensional convolutional neural network combined with a long short‐term memory model that uses an attention mechanism to accurately detect arc‐fault features. To enhance the reliability of the proposed model, we implement an arc‐fault generator compliant with the UL1699 standard and acquire high‐quality data that suitably reflect the real environment. Experimental results show that the proposed model achieves an accuracy of 99.99% in classifying series arc faults for five different loads. Hence, a performance improvement of approximately 1.7% in classification accuracy is reached compared with a feature fusion model that does not incorporate TL‐based model transfer and the attention mechanism.
- KSP Keywords
- Attention mechanism, Convolution neural network(CNN), Detection and analysis, Different loads, Fault Features, Feature Fusion, Fusion model, Learning model, Memory Model, Model transfer, Proposed model
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
