摘要
水电站一次设备是电力系统的核心组成部分,其运行状态直接影响电网的稳定性和安全性。传统故障诊断方法在面对复杂设备故障时效果有限,难以满足电力系统需求,因此提出了一种基于改进卷积神经网络的水电站一次设备故障诊断方法。通过引入Retinex算法增强设备红外图像,结合交叉熵函数构建深度卷积去噪自编码器进行数据降维,并利用卷积神经网络确定故障特征与类型的映射关系。实验结果表明,所提方法对不同故障类型的诊断准确率始终保持在95%以上,训练时间控制在4 min以内,显著优于传统方法。
Primary equipment of hydropower station is the core component of the power system,and its operation status directly affects the stability and security of the power grid.The traditional fault diagnosis method has limited effect in the face of complex equipment faults,and it is difficult to meet the demand of the power system.In this paper,a fault diagnosis method for primary equipment of hydropower station based on improved convolutional neural network is proposed.By introducing the Retinex algorithm to enhance the infrared image of the equipment,combining the cross-entropy function to construct a deep convolutional denoising self-encoder for data dimensionality reduction,and utilizing the convolutional neural network to determine the mapping relationship between the fault features and types.The experimental results show that the diagnostic accuracy of this paper′s method for different fault types is always above 95%,and the training time is controlled within 4 minutes,which is significantly better than the traditional method.
作者
仵博
WU Bo(Chongqing Datang International Pengshui Hydropower Development Co.,Ltd.,Chongqing 409600,China)
出处
《电工技术》
2026年第1期172-174,181,共4页
Electric Engineering
关键词
水电站
一次设备
故障诊断
RETINEX算法
卷积神经网络
数据降维
hydropower station
primary equipment
fault diagnosis
Retinex algorithm
convolutional neural network
data dimensionality reduction
