Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917939947503616 |
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| author | Matos-Carvalho, João Pedro Stefenon, Stefano Frizzo Leithardt, Valderi Reis Quietinho Yow, Kin-Choong |
| author_facet | Matos-Carvalho, João Pedro Stefenon, Stefano Frizzo Leithardt, Valderi Reis Quietinho Yow, Kin-Choong |
| contents | Surface contamination on electrical grid insulators leads to an increase in leakage current until an electrical discharge occurs, which can result in a power system shutdown. To mitigate the possibility of disruptive faults resulting in a power outage, monitoring contamination and leakage current can help predict the progression of faults. Given this need, this paper proposes a hybrid deep learning (DL) model for predicting the increase in leakage current in high-voltage insulators. The hybrid structure considers a multi-criteria optimization using tree-structured Parzen estimation, an input stage filter for signal noise attenuation combined with a large language model (LLM) applied for time series forecasting. The proposed optimized LLM outperforms state-of-the-art DL models with a root-mean-square error equal to 2.24$\times10^{-4}$ for a short-term horizon and 1.21$\times10^{-3}$ for a medium-term horizon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17341 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators Matos-Carvalho, João Pedro Stefenon, Stefano Frizzo Leithardt, Valderi Reis Quietinho Yow, Kin-Choong Machine Learning Artificial Intelligence Signal Processing Surface contamination on electrical grid insulators leads to an increase in leakage current until an electrical discharge occurs, which can result in a power system shutdown. To mitigate the possibility of disruptive faults resulting in a power outage, monitoring contamination and leakage current can help predict the progression of faults. Given this need, this paper proposes a hybrid deep learning (DL) model for predicting the increase in leakage current in high-voltage insulators. The hybrid structure considers a multi-criteria optimization using tree-structured Parzen estimation, an input stage filter for signal noise attenuation combined with a large language model (LLM) applied for time series forecasting. The proposed optimized LLM outperforms state-of-the-art DL models with a root-mean-square error equal to 2.24$\times10^{-4}$ for a short-term horizon and 1.21$\times10^{-3}$ for a medium-term horizon. |
| title | Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators |
| topic | Machine Learning Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2502.17341 |