基于深度学习算法的电力系统故障检测与诊断研究
摘 要
电力系统的故障检测与诊断一直是电力行业面临的一个难题。本文以深度学习算法为基础，结合数据采集、预处理方法及主成分分析、神经网络算法等技术，研究了电力系统故障检测与诊断的方法和实现。在实验中，通过采集真实的电力系统数据，应用主成分分析和神经网络算法，对电力系统中的故障进行了检测与诊断，得到了较为准确的结果。同时，本文比较了不同算法的优缺点，分析了其在电力系统故障检测与诊断中的适用性。研究结果表明，基于深度学习算法的电力系统故障检测与诊断具有一定的可行性和准确性，对于保障电力系统的安全运行具有重要意义。
关键词：深度学习算法 、电力系统 、故障检测 、主成分分析、神经网络算法
Abstract
The fault detection and diagnosis of the power system has always been a challenge faced by the power industry. This article is based on deep learning algorithms and combines data collection, preprocessing methods, principal component analysis, neural network algorithms, and other technologies to study the methods and implementation of power system fault detection and diagnosis. In the experiment, by collecting real power system data, principal component analysis and neural network algorithms were applied to detect and diagnose faults in the power system, and more accurate results were obtained. Meanwhile, this article compares the advantages and disadvantages of different algorithms and analyzes their applicability in power system fault detection and diagnosis. The research results indicate that the deep learning algorithm based fault detection and diagnosis of power systems has certain feasibility and accuracy, which is of great significance for ensuring the safe operation of power systems.
Keyword:Deep learning algorithms、Power systems、Fault detection、Principal component analysis、Neural network algorithms
目 录
1引言 1
2电力系统故障检测与诊断技术的基础理论 1
2.1电力系统故障检测与诊断技术的发展历程 1
2.2变电站设备的故障模式及特征提取方法 2
2.3主成分分析法原理及其在故障诊断中的应用 2
2.4神经网络算法原理及其在故障诊断中的应用 3
3电力系统故障检测与诊断技术的研究方法 3
3.1数据采集和预处理方法研究 3
3.2基于主成分分析法的故障诊断方法研究 4
4实验结果和分析 4
4.1系统实现方法和实验平台介绍 4
4.2基于主成分分析法的故障诊断实验结果和分析 5
4.3基于神经网络算法的故障诊断实验结果和分析 5
4.4不同方法的比较与评估 6
5结论 6
参考文献 8
致谢 9
摘 要
电力系统的故障检测与诊断一直是电力行业面临的一个难题。本文以深度学习算法为基础，结合数据采集、预处理方法及主成分分析、神经网络算法等技术，研究了电力系统故障检测与诊断的方法和实现。在实验中，通过采集真实的电力系统数据，应用主成分分析和神经网络算法，对电力系统中的故障进行了检测与诊断，得到了较为准确的结果。同时，本文比较了不同算法的优缺点，分析了其在电力系统故障检测与诊断中的适用性。研究结果表明，基于深度学习算法的电力系统故障检测与诊断具有一定的可行性和准确性，对于保障电力系统的安全运行具有重要意义。
关键词：深度学习算法 、电力系统 、故障检测 、主成分分析、神经网络算法
Abstract
The fault detection and diagnosis of the power system has always been a challenge faced by the power industry. This article is based on deep learning algorithms and combines data collection, preprocessing methods, principal component analysis, neural network algorithms, and other technologies to study the methods and implementation of power system fault detection and diagnosis. In the experiment, by collecting real power system data, principal component analysis and neural network algorithms were applied to detect and diagnose faults in the power system, and more accurate results were obtained. Meanwhile, this article compares the advantages and disadvantages of different algorithms and analyzes their applicability in power system fault detection and diagnosis. The research results indicate that the deep learning algorithm based fault detection and diagnosis of power systems has certain feasibility and accuracy, which is of great significance for ensuring the safe operation of power systems.
Keyword:Deep learning algorithms、Power systems、Fault detection、Principal component analysis、Neural network algorithms
目 录
1引言 1
2电力系统故障检测与诊断技术的基础理论 1
2.1电力系统故障检测与诊断技术的发展历程 1
2.2变电站设备的故障模式及特征提取方法 2
2.3主成分分析法原理及其在故障诊断中的应用 2
2.4神经网络算法原理及其在故障诊断中的应用 3
3电力系统故障检测与诊断技术的研究方法 3
3.1数据采集和预处理方法研究 3
3.2基于主成分分析法的故障诊断方法研究 4
4实验结果和分析 4
4.1系统实现方法和实验平台介绍 4
4.2基于主成分分析法的故障诊断实验结果和分析 5
4.3基于神经网络算法的故障诊断实验结果和分析 5
4.4不同方法的比较与评估 6
5结论 6
参考文献 8
致谢 9
