电力变压器故障诊断与预测的机器学习方法

Machine Learning Methods for Fault Diagnosis and Prediction of Power Transformers

针对电力变压器故障诊断与预测问题,基于机器学习方法进行研究。在研究过程中,采用支持向量机(SupportVectorMachine,SVM)和深度学习2种机器学习方法进行故障诊断与预测,并对2种方法的性能进行了对比分析。根据实验结果,提出的基于机器学习方法的电力变压器故障诊断与预测研究,在提高故障诊断准确率和预测能力方面取得了显著成效。SVM、深度学习以及综合方法分别在不同程度上优化了故障诊断与预测的性能,为电力系统运行维护提供了有力支持。通过研究可以有效提高变压器故障的准确诊断率和预测准确度,降低电力系统的事故风险,确保电网安全稳定运行。

This article focuses on the diagnosis and prediction of power transformer faults,and conducts research based on machine learning methods.In the research process,this article adopts two machine learning methods,Support Vector Machine(SVM)and deep learning,for fault diagnosis and prediction,and compares and analyzes the performance of the two methods.According to the experimental results,it can be found that the research on power transformer fault diagnosis and prediction based on machine learning methods proposed in this article has achieved significant results in improving fault diagnosis accuracy and prediction ability.SVM,deep learning,and synthesis methods have optimized the performance of fault diagnosis and prediction to varying degrees,providing strong support for the operation and maintenance of power systems.This study can effectively improve the accuracy of transformer fault diagnosis and prediction,reduce the risk of power system accidents,and ensure the safe and stable operation of the power grid.