用优化大模型预测电网绝缘子泄漏电流，提前预警故障。
Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators
- 结合树形帕兹估计与降噪滤波，优化大模型进行时序预测。
- 短期预测误差仅2.24×10⁻⁴，中期为1.21×10⁻³。
- 适合电力系统故障预警，尤其关注高压绝缘子状态监控。
电网绝缘子表面污染会导致泄漏电流增加，最终引发电弧放电，可能造成停电。为降低此类故障风险，监测污染程度和泄漏电流有助于预测故障发展。本文提出一种混合深度学习模型，用于预测高压绝缘子泄漏电流的增长。该模型采用树状帕兹估计进行多准则优化，结合信号降噪输入过滤器，并引入大语言模型（LLM）进行时间序列预测。所提优化后的LLM在短中期预测中表现优于现有先进模型：短期预测的均方根误差为2.24×10⁻⁴，中期为1.21×10⁻³。
原文摘要 · Abstract (English)
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.
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