基于神经网络的变压器故障诊断研究

论文编号:DQ034  论文字数:20260,页数:35 附任务书，开题报告，文献综述，外文翻译

摘  要
 电力变压器是变电站的关键设备，其运行的安全、可靠性直接关系到电力系统的安全与稳定，因此，有效地监测变压器运行状态、诊断和预报变压器故障具有实际意义。
 本文在广泛查阅相关文献的基础上，系统综述了电力变压器故障过程特征气体产生的物、化机理，以及不同种类故障与不同种类特征气体含量之间的相应关系。
 在分析了油中溶解气体含量与不同故障之间联系的基础上，利用气相色谱技术采集油中特征气体含量数据样本作为人工神经网络学习训练的特征向量矩阵，充分利用人工神经网络具有的并行处理、学习和记忆、非线性映射、自适应能力和鲁棒性等特点，构造了基于人工神经网络的电力变压器故障特征气体诊断系统，选择和训练了BP神经网络。
 通过对样本的训练和对故障诊断样本的诊断，故障诊断正确率达90%以上，满足实际要求，说明本文建立的结构为5-12-5型的电力变压器故障诊断BP神经网络模型是合适的、可行的、正确的，能够很好的应用于电力变压器的故障诊断。

关键词：电力变压器 神经网络 故障诊断 收敛速度

Abstract
 Power transformer is the key apparatus at the transformer substation, its working safe and reliability relate to the safety and stabilization of power system directly. Therefore, it has practical significance to effective inspect the running condition of transformer, diagnose and predict the transformer fault.
 This paper is based on a lot of references, system overview the fault processing of the power transformer, the principle of gerneration characteristic gas, corresponding relationship between the different kinds of fault and different kinds of characteristic.
 Base on analysis the relationship between the dissolving gas content in oil and different kinds of faults, making use of the technology of gas phase color spectrum sample data of the characteristic gas content in oil. Then these data samples become to the learning and training characteristic vecter matrix of the artifial neural network .Making the most of the paralleling processing, learing,memorization,nonlinearity mapping,adaptation ability and robustness etc of the artifial neural network,constructing the fault characteristic gas diagnosis system of the power transformer based on the artifial neural network. Selecting and training the BP neural network .
 According to the transformer fault gases and the fault types, a type of 5-12-5 BP Neural Network model for transformer fault diagnosis is established. After training and fault diagnosing, the model's fault diagnosis accuracy is above 90%,which shows that the model is proper, feasible and correct for the transformer fault diagnosis.
 
 
   
 Key words: Power Transformer; Artificial Neural Network; Fault Diagnosis; Network Convergence Speed
 
 
 
目 录
摘  要 I
1 绪论 1
1.1引言 1
1.2国内外研究现状及发展趋势 1
1.3基于神经网络的变压器故障诊断研究的意义 3
1.3.1神经网络的应用领域 3
1.3.2神经网络的发展趋势 3
1.3.3基于神经网络的变压器故障诊断模型的优越性 4
1.4本文的主要研究内容 4
2 变压器故障及其诊断方法 6
2.1变压器油中气体产生机理 6
2.1.1油中溶解气体的来源 6
2.1.2特征气体产生的原因和特点 7
2.2变压器故障类型 8
2.3充油变压器的故障诊断方法 10
3 神经网络模型及BP网络学习算法分析 13
3.1神经网络模型 13
3.1.1人工神经网络理论 13
3.1.2人工神经元模型 13
3.1.3人工神经网络类型 14
3.2 BP网络模型及其学习算法 15
4 BP网络的设计与应用 17
4.1利用神经网络进行故障诊断的可行性 17
4.2 BP神经网络的创建 18
4.3训练样本的采集与预处理 19
4.4 BP神经网络编程仿真及在故障诊断的应用 24
5 结论 28
致 谢 29
参考文献 30

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