Academic Journal
An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids
| Title: | An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids |
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| Authors: | Jiahao Zhang, Lan Cheng, Zhile Yang, Qinge Xiao, Sohail Khan, Rui Liang, Xinyu Wu, Yuanjun Guo |
| Source: | Energy and AI, Vol 17, Iss , Pp 100377- (2024) |
| Publisher Information: | Elsevier, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Electrical engineering. Electronics. Nuclear engineering LCC:Computer software |
| Subject Terms: | Power grid fault detection, Semi-supervised learning, Data driven, Deep learning, Smart grid, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765 |
| More Details: | With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2666-5468 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2666546824000430; https://doaj.org/toc/2666-5468 |
| DOI: | 10.1016/j.egyai.2024.100377 |
| Access URL: | https://doaj.org/article/0682971025b245739b83d86db77260a9 |
| Accession Number: | edsdoj.0682971025b245739b83d86db77260a9 |
| Database: | Directory of Open Access Journals |
| ISSN: | 26665468 |
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| DOI: | 10.1016/j.egyai.2024.100377 |
| Published in: | Energy and AI |
| Language: | English |