摘 要
随着电力系统规模的不断扩大和复杂性的增加，变压器作为电力传输与分配中的关键设备，其运行状态直接影响到整个电网的安全与稳定。传统的变压器故障诊断方法多依赖于专家经验和定期检测，存在效率低、成本高且难以实时捕捉潜在故障等问题。因此，基于机器学习的变压器故障诊断系统应运而生，成为电力行业研究的热点之一。本文围绕基于机器学习的变压器故障诊断系统展开研究，旨在通过运用先进的机器学习算法，实现对变压器故障的高效、准确诊断。首先，本文深入分析了变压器故障的类型、成因及表现形式，明确了故障诊断的目标和需求。随后，本文详细介绍了机器学习在变压器故障诊断中的应用现状和发展趋势，包括常用的机器学习算法（如支持向量机、神经网络、随机森林等）及其在处理变压器故障数据中的优势和挑战。在系统设计方面，本文提出了一个基于机器学习的变压器故障诊断系统框架。该系统通过采集变压器的运行数据（如油温、油位、声音、振动等），结合历史故障案例和专家知识，构建故障特征库。然后，利用机器学习算法对故障特征进行训练和学习，建立故障诊断模型。当系统接收到新的变压器运行数据时，模型能够迅速识别并诊断出潜在的故障类型及其严重程度，为维修人员提供及时、准确的故障信息。
关键词：机器学习 变压器 故障诊断系统
Abstract
With the expansion and complexity of power system, transformer is the key equipment in power transmission and distribution, and its operation status directly affects the safety and stability of the whole power grid. The traditional transformer fault diagnosis methods mainly rely on expert experience and regular inspection, and have problems such as low efficiency, high cost and difficult to catch potential faults in real time. Therefore, the transformer fault diagnosis system based on machine learning came into being and has become one of the hot spots in the power industry. This paper focuses on the research of transformer fault diagnosis system based on machine learning, aiming to realize the efficient and accurate diagnosis of transformer fault by using advanced machine learning algorithm. Firstly, this paper deeply analyzes the types, causes and manifestations of transformer faults, and clarifies the target and demand of fault diagnosis. Subsequently, this paper introduces the application status and development trend of machine learning in transformer fault diagnosis in detail, including common machine learning algorithms (such as support vector machines, neural networks, random forests, etc.) and their advantages and challenges in processing transformer fault data. In the aspect of system design, this paper proposes a fr amework of transformer fault diagnosis system based on machine learning. By collecting transformer operation data (such as oil temperature, oil level, sound, vibration, etc.), the system combines historical fault cases and expert knowledge to build fault signature database. Then, the machine learning algorithm is used to train and learn the fault features, and the fault diagnosis model is established. When the system receives the new transformer operation data, the model can quickly identify and diagnose the potential fault type and its severity, and provide timely and accurate fault information for maintenance personnel.
Keyword:Machine learning Transformer Fault diagnosis system
目 录
1引言 1
2机器学习基础与算法选择 1
2.1机器学习理论基础 1
2.2算法性能评估指标 2
2.3适用于故障诊断的算法比较 2
3变压器故障诊断系统设计 3
3.1系统架构设计 3
3.2数据采集与预处理 4
3.3特征提取与选择 4
3.4设计的创新性与实用性分析 5
4系统实现与测试分析 5
4.1系统实现过程 5
4.2测试方案与结果分析 6
4.3性能评估与优化策略 7
4.4测试的科学性与准确性分析 7
5结论 8
参考文献 9
致谢 10
