Condition monitoring and fault diagnosis of photovoltaic modules are essential to ensure the efficient and reliable operation of large-scale photovoltaic plants. This article presents an algorithmic solution for the rapid and sensitive detection of photovoltaic modules with multiple visible defects by an image analyzing apparatus mounted onto an unmanned aerial vehicle. The proposed solution is composed of three stages to efficiently and accurately analyze various forms of module defects. First, the Kirsch operator is employed to identify the anomalous regions, which can significantly reduce the computational complexity, and error rate. Afterward, a deep convolutional neural network is adopted to extract defect features. Finally, a multiple classification support vector machine is developed to facilitate the defects detection decision-making. The proposed solution is extensively evaluated by the comprehensive dataset collected from real-world solar photovoltaic plants. The experimental results clearly demonstrate the effectiveness of our solution for photovoltaic modules diagnosis with multiple visible defects.

中文翻译：

---

光伏电站多缺陷无人值守检测系统

光伏组件的状态监测和故障诊断对于确保大型光伏电站高效可靠运行至关重要。本文提出了一种算法解决方案，用于通过安装在无人机上的图像分析设备快速灵敏地检测具有多个可见缺陷的光伏模块。所提出的解决方案由三个阶段组成，以高效准确地分析各种形式的模块缺陷。首先，采用Kirsch算子识别异常区域，可以显着降低计算复杂度和错误率。之后，采用深度卷积神经网络来提取缺陷特征。最后，开发了多分类支持向量机以促进缺陷检测决策。提议的解决方案通过从现实世界太阳能光伏电站收集的综合数据集进行了广泛评估。实验结果清楚地证明了我们的解决方案对具有多个可见缺陷的光伏组件诊断的有效性。