一种脆弱线路缺陷的图像智能检测算法设计

Design of an Intelligent Image Detection Method for Vulnerable Line Defects

摘要:

配电线路稳定运行可以有效提升电力系统有序性，脆弱线路缺陷是引起配电网连锁故障停电的主要原因。以人工为主的识别方法存在明显缺陷，在无人机的辅助下，设计了一种脆弱线路缺陷图像自动检测方法。通过构建脆弱线路数据集，以输电线路的脆弱性综合指标为依据，辨识配电网脆弱线路。建立配电网脆弱线路缺陷特征分类标准，利用图像增强技术提升脆弱线路缺陷图像成像效果。采用对比度受限自适应直方图均衡方法均衡脆弱线路缺陷图像的色彩和反差，结合小波变换对均衡后的脆弱线路缺陷图像进行降噪处理。运用卷积神经网络将降噪后的脆弱线路缺陷图像输入至卷积层完成脆弱线路缺陷自动检测。通过实验测试发现：提出方法的召回率最高为89.32%，精确率最高为98.20%，错检率最低为0.98%，能够最小范围识别脆弱线路缺陷，充分证实了提出算法检测效率较高。

Abstract:

The stable operation of distribution lines can effectively improve the order of the power system. Vulnerable line defects are the main cause of cascading failures and blackouts in distribution networks. The artificial recognition method has obvious defects. With the help of UAV, an automatic detection method of fragile line defect image is designed. Based on the comprehensive vulnerability index of transmission lines, the vulnerable lines of distribution network are identified by constructing the vulnerable line data set. Establish the classification standard of vulnerable line defect characteristics in distribution network, and use image enhancement technology to improve the imaging effect of vulnerable line defect images. The contrast limited adaptive histogram equalization method is used to balance the color and contrast of the fragile line defect image, and the wavelet transform is used to denoise the balanced fragile line defect image. The convolution neural network is used to input the de-noised fragile line defect image into the convolution layer to complete the automatic detection of fragile line defects. Through experimental tests, it is found that the highest recall rate of the proposed method is 89.32%, the highest accuracy rate is 98.20%, and the lowest error detection rate is 0.98%. It can identify the vulnerable line defects in the minimum range, which fully proves that the proposed algorithm has high detection efficiency.