An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids
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 so...
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| Veröffentlicht in: | Energy and AI Jg. 17; S. 100377 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.09.2024
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| ISSN: | 2666-5468, 2666-5468 |
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| Abstract | 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.
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•Employed Yule–Walker for grid data spectral analysis to unveil distinct time-frequency characteristics•Merged self-supervised learning with dynamic thresholds (SAT-SSL) for high efficiency and accuracy•Validated SAT-SSL has better fault diagnosis performance on both benchmark and real-world datasets•Enhanced power grid fault detection and classification accuracy for intelligent monitoring and maintenance |
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| AbstractList | 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.
[Display omitted]
•Employed Yule–Walker for grid data spectral analysis to unveil distinct time-frequency characteristics•Merged self-supervised learning with dynamic thresholds (SAT-SSL) for high efficiency and accuracy•Validated SAT-SSL has better fault diagnosis performance on both benchmark and real-world datasets•Enhanced power grid fault detection and classification accuracy for intelligent monitoring and maintenance 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. |
| ArticleNumber | 100377 |
| Author | Xiao, Qinge Guo, Yuanjun Cheng, Lan Zhang, Jiahao Yang, Zhile Wu, Xinyu Liang, Rui Khan, Sohail |
| Author_xml | – sequence: 1 givenname: Jiahao surname: Zhang fullname: Zhang, Jiahao organization: Taiyuan University of Technology, Taiyuan, Shanxi, China – sequence: 2 givenname: Lan surname: Cheng fullname: Cheng, Lan organization: Taiyuan University of Technology, Taiyuan, Shanxi, China – sequence: 3 givenname: Zhile surname: Yang fullname: Yang, Zhile organization: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China – sequence: 4 givenname: Qinge surname: Xiao fullname: Xiao, Qinge organization: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China – sequence: 5 givenname: Sohail surname: Khan fullname: Khan, Sohail organization: Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule – Institute of Applied Sciences and Technology, Haripur, Khyber Pakhtunkhwa, Pakistan – sequence: 6 givenname: Rui surname: Liang fullname: Liang, Rui organization: Chengxi Company, State Grid Tianjin Electric Power Company, Tianjin, China – sequence: 7 givenname: Xinyu surname: Wu fullname: Wu, Xinyu organization: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China – sequence: 8 givenname: Yuanjun orcidid: 0000-0002-2213-5489 surname: Guo fullname: Guo, Yuanjun email: yj.guo@siat.ac.cn organization: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China |
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| Keywords | Deep learning Data driven Power grid fault detection Semi-supervised learning Smart grid |
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