摘 要
变压器作为电力系统中的关键设备，其运行状态直接关系到整个电力系统的安全稳定。传统故障诊断方法存在效率低、准确性不足等问题，难以满足现代电力系统对变压器故障诊断的高要求。为此，本文提出基于机器学习的变压器故障诊断方法，旨在提高故障诊断的准确性和时效性。该方法首先收集变压器在不同工况下的运行数据，包括电气量和非电气量，并对数据进行预处理以消除噪声干扰。然后选取支持向量机、随机森林等典型机器学习算法构建故障诊断模型，通过对比分析确定最优模型。实验结果表明，所提方法能够有效识别变压器内部故障类型，诊断准确率高达95%以上，且具有较强的泛化能力。与传统方法相比，该方法无需复杂的人工特征提取过程，可直接利用原始数据进行训练，大大简化了故障诊断流程，提高了诊断效率。此外，该方法还引入了迁移学习机制，使得模型能够在少量样本条件下快速适应新类型的变压器故障诊断任务，进一步提升了其实用价值。综上所述，基于机器学习的变压器故障诊断方法为变压器故障诊断提供了一种新的思路和技术手段，在实际应用中展现出良好的性能和广阔的应用前景。
关键词：变压器故障诊断 机器学习 支持向量机
Abstract
Transformers, as critical components in power systems, play a pivotal role in ensuring the safety and stability of the entire electrical infrastructure. Traditional fault diagnosis methods suffer from inefficiency and insufficient accuracy, failing to meet the stringent requirements of modern power systems for transformer fault diagnosis. To address these challenges, this paper proposes a machine learning-based approach for transformer fault diagnosis, aiming to enhance both the accuracy and timeliness of fault detection. The proposed method first collects operational data under various operating conditions, encompassing both electrical and non-electrical parameters, followed by preprocessing to eliminate noise interference. Subsequently, typical machine learning algorithms such as Support Vector Machines (SVM) and Random Forests are employed to construct fault diagnosis models, with the optimal model determined through comparative analysis. Experimental results demonstrate that the proposed method can effectively identify internal fault types in transformers, achieving a diagnostic accuracy exceeding 95%, along with strong generalization capabilities. Compared to traditional methods, this approach eliminates the need for complex manual feature extraction, allowing direct utilization of raw data for training, thereby significantly simplifying the diagnosis process and improving efficiency. Moreover, the introduction of transfer learning mechanisms enables the model to rapidly adapt to new types of transformer fault diagnosis tasks even with limited sample sizes, further enhancing its practical value. In summary, the machine learning-based transformer fault diagnosis method provides a novel perspective and technical means for transformer fault diagnosis, showcasing excellent performance and broad application prospects in practical scenarios.
Keyword:Transformer Fault Diagnosis Machine Learning Support Vector Machine
目 录
引言 1
1变压器故障诊断需求分析 1
1.1变压器故障类型概述 1
1.2传统诊断方法局限性 2
1.3机器学习的应用优势 2
2机器学习算法选择与优化 3
2.1常用机器学习算法比较 3
2.2特征提取与选择方法 3
2.3算法性能评估指标 4
3数据采集与预处理技术 4
3.1故障数据获取途径 5
3.3数据增强与标注方法 6
4故障诊断模型构建与验证 6
4.1模型架构设计原则 6
4.2训练与测试集划分 7
4.3实验结果与分析讨论 7
结论 8
参考文献 10
致谢 11
