CAJ | 학술논문

电力大数据中日益增多的非结构化数据为以人工诊断为主的传统处理方式提出了新的挑战。红外故障图像作为一种典型的非结构化数据，对于电力大数据的研究有着至关重要的作用。为了达到自动处理海量红外故障图像的目的，提出了一种基于卷积递归网络的电流互感器红外故障图像诊断方法。对红外故障图像首先进行超像素分割并利用其色度信息提取温度异常区域；然后采用两级联合卷积-递归神经网络，对大量样本信息进行训练学习来指导设备故障部位识别；最后依据部位信息对故障进行分类。实验结果表明，该算法鲁棒性较强，准确性较高，有效地提高了红外检测效率，为非结构化数据的特征提取分析提供了坚实的基础。
전력대수거중일익증다적비결구화수거위이인공진단위주적전통처리방식제출료신적도전。홍외고장도상작위일충전형적비결구화수거，대우전력대수거적연구유착지관중요적작용。위료체도자동처리해량홍외고장도상적목적，제출료일충기우권적체귀망락적전류호감기홍외고장도상진단방법。대홍외고장도상수선진행초상소분할병이용기색도신식제취온도이상구역；연후채용량급연합권적-체귀신경망락，대대량양본신식진행훈련학습래지도설비고장부위식별；최후의거부위신식대고장진행분류。실험결과표명，해산법로봉성교강，준학성교고，유효지제고료홍외검측효솔，위비결구화수거적특정제취분석제공료견실적기출。
Increasing unstructured data of big data in electric system puts forward a new challenge to traditional manual processing mode. As a typical kind of unstructured data, the infrared image is very important for the research of big data in electric system. In order to automatically processing massive infrared fault images, this paper presents a convolutional recursive network based current transformer infrared fault image diagnosis method. The infrared fault images are first segmented by super pixel segmentation method and then we take advantage of the hue information to extract the temperature anomaly area; secondly, a two-level joint convolution recursive neural network is adopted, the fault device position can be identified by training a large number of samples; finally, the fault information is confirmed according to the location information of fault classification. The experimental results show that, this algorithm has better robustness, higher accuracy, and can improve the efficiency of infrared diagnosis, which is also the foundation for the feature representation of unstructured data.