电气工程的毕业论文

题目：基于机器学习的电气设备故障诊断

摘要：电气设备故障对于电力系统的安全稳定运行具有极大的危害，因此对电气设备的故障诊断具有重要意义。传统的电气设备故障诊断方法需要大量的人力和时间，且准确率不高。本文提出了一种基于机器学习的电气设备故障诊断方法，通过分析电气设备的工作状态，提取相关特征，并使用支持向量机（SVM）算法进行分类诊断。实验结果表明，该方法具有较高的准确率和稳定性，可以有效地提高电气设备故障诊断效率。

关键词：电气设备故障诊断；机器学习；支持向量机；特征提取

Abstract: Electrical equipment failure has great harm to the safe and stable operation of the power system, so the diagnosis of electrical equipment failure is of great significance. Traditional electrical equipment fault diagnosis methods require a lot of manpower and time, and the accuracy is not high. This paper proposes a machine learning-based electrical equipment fault diagnosis method, which extracts relevant features by analyzing the working state of electrical equipment and uses the support vector machine (SVM) algorithm for classification diagnosis. Experimental results show that the method has high accuracy and stability, and can effectively improve the efficiency of electrical equipment fault diagnosis.

Keywords: electrical equipment fault diagnosis; machine learning; support vector machine; feature extraction