Big Data Analysis Based Transformer Temperature Prediction Method in Distribution Station Area

Xianming Cheng, Haipeng Sun, Zhibin Yin, Xiao Ding
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Abstract

The normal operation of power transformer is related to the safety and stability of the power grid. Abnormal temperature may cause damage to transformer equipment, seriously affect its service life, and even lead to major accidents. In this paper, a transformer temperature prediction method based on big data is proposed. The ambient temperature is included in the prediction conditions. A feature extraction method based on adaptive weighting is designed to mine the time series features in the column head temperature and ambient temperature, and an interactive feature fusion strategy is used to form a comprehensive and reliable transformer temperature prediction. The experimental simulation shows that the transformer temperature prediction method proposed in this paper has high prediction accuracy, effectively provides more quantitative auxiliary information for the operation monitoring of power transformer equipment, ensures the safe and stable operation of transformer, and has high practicability.
基于大数据分析的配电站区域变压器温度预测方法
电力变压器的正常运行关系到电网的安全稳定。温度异常会对变压器设备造成损坏,严重影响其使用寿命,甚至导致重大事故。本文提出了一种基于大数据的变压器温度预测方法。环境温度也包括在预测条件中。设计了一种基于自适应加权的特征提取方法,挖掘柱头温度和环境温度中的时间序列特征,并采用交互式特征融合策略,形成全面可靠的变压器温度预测。实验仿真表明,本文提出的变压器温度预测方法预测精度高,有效地为电力变压器设备的运行监测提供了更定量的辅助信息,保证了变压器的安全稳定运行,具有较高的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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