基于条件互信息与LSTNet的特高压变压器顶层油温预测方法

Forecasting Method for Top Oil Temperature in Ultra-high Voltage Transformers Based on Conditional Mutual Information and LSTNet

顶层油温预测可为特高压变压器绝缘老化评估及故障预警提供重要依据。该文提出一种基于条件互信息(conditional mutual information,CMI)及长期和短期时间序列网络(long-and short-term time-series network,LSTNet)的特高压变压器顶层油温预测方法。基于历史监测数据包括顶层油温、油中溶解气体含量、绕组温度、绕组电流、环境温度等9种参量,采用条件互信息方法,为顶层油温预测选取具有强信息增益的特征量,以降低预测模型输入特征维度;在此基础上,利用LSTNet提取特征量中蕴含的长期周期性规律和短期非线性变化特性,建立基于CMI-LSTNet预测模型,实现特高压变压器多个部位顶层油温预测。算例结果表明,相较于现有典型预测方法,该文方法不仅适应特高压变压器顶层油温变化趋势,且具有较高的预测精度。

The forecasting of top oil temperature can provide a significant basis for insulation aging assessment and fault warning of UHV transformers.This paper proposes a prediction method for top-level oil temperature of ultra-high voltage(UHV)transformer based on conditional mutual information(CMI)as well as long-term and short-term time-series network(LSTNet).Based on historical data,including top oil temperature,dissolved gases in oil,winding temperature,winding current,environmental temperature and other nine parameters,the CMI method is used to select the characteristics with greater information gain for the top oil temperature forecast to reduce the input characteristic dimension.On this basis,LSTNet is applied to extract the long-term periodic law and short-term nonlinear variation contained in the characteristic variables.CMI-LSTNet model is established to realize the prediction of top oil temperature at multiple parts of UHV transformer.The results show that the addressed method not only can describe the change tendency of top oil temperature of UHV transformer effectively,but also has higher prediction accuracy,compared with the existing typical forecasting methods.