首页|基于PAT-Unet的泄露电流仪表示数识读算法设计

基于PAT-Unet的泄露电流仪表示数识读算法设计

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仪表能准确反映变电站电气设备的运行状态,为了克服人工巡检造成仪表漏读与错读等情况,提出一种基于PAT-Unet神经网络的仪表示数智能识读算法对泄露电流仪表的示数进行智能识读.首先设计PAT-Unet神经网络对仪表的指针与密集刻度进行分割,网络中在编码层构建特征聚合模块与残差特征分散模块,增强特征的提取能力;设计Transformer特征浓缩模块,进行深层语义信息融合,以增强对细小目标分割精度;引入金字塔切分注意力机制,加强网络编码层与译码层之间的信息交互能力.结合轮廓检测算法与最小外接矩形算法,计算刻度分割结果的关键点,利用透视变换完成倾斜仪表的校正;再使用K-means聚类算法定位泄露电流仪表圆心;最后,根据仪表圆心利用极坐标变换将扇形表盘展开为矩形,通过分别计算零刻度与指针以及最大刻度之间的距离关系得到泄露电流仪表示数.实验证明,所提算法能对倾斜仪表盘进行校正,在保证读数准确度的同时,能对泄露电流仪表示数进行智能识读.
Algorithm design for leakage current meter representation reading based on PAT-Unet
Meters can accurately reflect the operation status of substation electrical equipment,to overcome the manual inspection caused by meter leakage and misreading and so on,put forward a PAT-Unet neural network based on the number of intelligent reading algorithms for the leakage current meter display intelligent reading.Firstly,the PAT-Unet neural network is designed to segment the pointer and dense scale of the meter.The feature aggregation module and residual feature dispersion module are constructed in the coding layer of the network to enhance the feature extraction ability.Design the transformer feature concentration module for deep semantic information fusion to enhance the segmentation accuracy of acceptable targets;introduce the pyramid slicing attention mechanism to strengthen the information interaction between the network coding and decoding layers.The information interaction between the coding layer and the decoding layer of the network is enhanced.Ability.Combine the contour detection algorithm and the minimum outer rectangle algorithm to calculate the key point of the scale segmentation results,and use perspective transformation to complete the correction of tilted meters;then use the K-means clustering algorithm to locate the center of the leakage current meter;finally,according to the center of the metering circle,use the polar coordinate transformation to expand the sector dial into a rectangle,and get the number of the leakage current meter by calculating the distance between the zero scale,the pointer and the maximal scale respectively.The leakage current meter is obtained by calculating the distance relationship between the zero scale and pointer and the maximum scale respectively.Experiments have demonstrated that the proposed algorithm can correct the tilted dashboard and provide intelligent readings of the leakage current meter representation while ensuring the accuracy of the readings.

meter readingneural networkfeature fusionperspective transformationpolar coordinates conversion

漆梓渊、吴浩、陈伟哲、骆忠强、周媛

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四川轻化工大学人工智能四川省重点实验室 宜宾 644005

四川轻化工大学自动化信息工程学院 宜宾 644005

四川航天电液控制有限公司 成都 610017

仪表读数 神经网络 特征融合 透视变换 极坐标转换

国家自然科学基金四川省科技厅项目四川省科技厅项目四川省科技厅项目人工智能四川省重点实验室项目四川轻化工大学研究生创新基金

618013192021YFG03132022YFS05182022ZHCG00352019RYY01Y2022109

2024

10.19651/j.cnki.emt.2315252

电子测量技术
北京无线电技术研究所

电子测量技术

CSTPCD北大核心
影响因子：1.166
ISSN：1002-7300
年,卷(期)：2024.47(11)