Abstract:

Limited by the complexity and analysis efficiency of image processing， traditional photovoltaic fault diagnosis methods based on image recognition are difficult to achieve real-time monitoring and large-scale fault classification and location. To address this problem， a photovoltaic detection algorithm based on shape factor and improved watershed segmentation was proposed. Firstly， a photovoltaic module segmentation algorithm was designed on the basis of shape factor. And the shape factor was defined as the ratio of the area to the perimeter of ??the connected region. It has scale and rotation invariance， and is able to extract the contours of photovoltaic modules with different scales in complex backgrounds to avoid interference of background areas on fault diagnosis. Secondly， the watershed algorithm was improved by iterative H value， the over-segmentation phenomenon was suppressed by adjusting the local minimum value， and the fault classification and precise location of the segmented photovoltaic module image were performed. Finally， in order to achieve remote control， the human-computer interaction interface designed by Qt Designer software was embedded in Raspberry Pi， and the intranet penetration and Virtual Network Console （VNC） were configured. At the same time， the drone was equipped with a Raspberry Pi and a high-definition camera to achieve real-time monitoring and fault diagnosis of photovoltaic station during flight. Experimental results show that the comprehensive accuracy of the proposed algorithm for identifying photovoltaic faults is 85.19%， which is 9.38 percentage points higher than that of the traditional watershed algorithm， and the over-segmentation rate is reduced by 26.1 percentage points， indicating that the proposed algorithm can control the over-segmentation phenomenon more effectively and improve the accuracy of fault diagnosis.

Key words: photovoltaic module, visible fault, Raspberry Pi, intranet penetration, watershed algorithm

摘要：

受限于图像处理的复杂性和分析效率，传统的基于图像识别的光伏故障诊断方法难以实现实时监测和大范围故障分类与定位。针对此问题，提出一种基于形状因子与改进分水岭分割的光伏检测算法。首先，基于形状因子设计光伏组件分割算法，并定义形状因子为连通区域面积与周长的比值，该比值具备尺度和旋转不变性，能实现提取复杂背景中不同尺度的光伏组件轮廓，以避免背景区域对故障诊断造成干扰；其次，利用迭代H值改进分水岭算法，通过调整局部极小值抑制过分割现象，并对分割后的光伏组件图像进行故障分类和精确定位；最后，为了实现远程控制，在树莓派中嵌入由Qt Designer软件设计的人机交互界面并配置内网穿透和虚拟网络控制台（VNC），同时由无人机搭载树莓派和高清摄像头实现在飞行过程中对光伏场站的实时监控和故障诊断。实验结果表明，所提算法识别光伏故障的综合准确率为85.19%，相较于传统分水岭算法的准确率提高了9.38个百分点，过分割率降低了26.1个百分点。以上结果表明所提算法可以更加有效地控制过分割现象，提高故障诊断的准确率。

关键词: 光伏组件, 可见故障, 树莓派, 内网穿透, 分水岭算法