基于卷积递归网络的电流互感器红外故障图像诊断

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。

Convolutional-recursive network based current transformer infrared fault image diagnosis

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.