摘要
为了提高振动监测诊断算法在变压器振动监测点发生偏移以及低信噪比环境下的识别精度,提出了一种基于多点振动检测和改进MobileNet的变压器工况识别方法。分析了变压器箱体侧壁不同测点的振动特性,研究常规人工智能算法在测量点发生偏移和低信噪比环境下的分类效果。提出了适配多点检测的改进MobileNet算法:对多测点振动信号进行预处理生成三维数据结构;一级算法使用逐点卷积-深度卷积-逐点卷积架构搭配空洞卷积核,对数据流进行多尺度特征提取;二级算法使用全连接网络,对样本进行识别。研究结果表明:与常规算法相比,多点检测方案下改进的MobileNet算法性能表现优异,当测点偏移小于0.3m时,识别准确率下降小于2%;当信噪比为10dB时,识别准确率稳定在97%。此外,该算法具有更小的模型占用内存和更短的识别预测时间,部署到树莓派等移动端设备能够大幅提升运算速度,降低运行功耗。
To improve the recognition accuracy of vibration monitoring and diagnosis algorithms in transformer vibration monitoring points with displacement and low signal-noise ratio environment,a transformer condition recognition method based on multi-point vibration detection and improved MobileNet is proposed.Analyzed the vibration characteristics of different measuring points on the side wall of the transformer box and studied the classification performance of conventional artificial intelligence algorithms in the environment with measuring point offset and low signal-noise ratio.Proposed an improved MobileNet algorithm that adapts to multi-point detection:preprocessing vibration signals from multiple measuring points to generate a three-dimensional data structure.The first level algorithm uses a pointwise-depthwise-pointwise convolution architecture combined with a dilated convolution kernel to extract multi-scale features from the data stream.The second-level algorithm uses a fully connected network to identify samples.The research results show that the improved MobileNet under the multi-point detection scheme performs well compared with conventional algorithms.When the measuring point offset is less than 0.3m,the recognition accuracy decreases by less than 2%.When the signal-noise ratio is 10dB,the recognition accuracy remains at 97%.In addition,this algorithm has a smaller model footprint and shorter recognition and prediction time.Deploying it on mobile devices such as Raspberry Pi can significantly improve computing speed and reduce running power consumption.
作者
王泓江
李喆
WANG Hongjiang;LI Zhe(School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Minhang District,Shanghai 200240,China)
出处
《电网技术》
北大核心
2025年第3期1266-1275,I0096,共11页
Power System Technology
关键词
变压器工况识别
多点振动检测
测点偏移
低信噪比
改进MoblieNet
transformer condition recognition
multi-point vibration detection
measuring point offset
low signal-noise ratio
improved MoblieNet
