Homeowners are increasingly deploying rooftop solar photovoltaic (PV) arrays due to the rapid decline in solar module prices. However, homeowners may have to spend up to ∼$375 to diagnose their damaged rooftop solar PV system. Thus, recently, there is a rising interest to inspect potential damage on solar PV arrays automatically and passively. Unfortunately, recent approaches that leverage machine learning techniques have the limitation of distinguishing solar PV array damages from other solar degradation (e.g., shading, dust, snow). To address this problem, we design a new system—SolarDiagnostics that can automatically detect and profile damages on rooftop solar PV arrays using their rooftop images with a lower cost. In essence, SolarDiagnostics first leverages an K-Means algorithm to isolate rooftop objects to extract solar panel residing contours. Then, SolarDiagnostics employs a convolutional neural networks to accurately identify and characterize the damage on each solar panel residing contour. We evaluate SolarDiagnostics by building a lower cost prototype and using 60,000 damaged solar PV array images generated by deep convolutional generative adversarial networks. We find that SolarDiagnostics is able to detect damaged solar PV arrays with a Matthews correlation coefficient (MCC) of 1.0. In addition, pre-trained SolarDiagnostics yields an MCC of 0.95, which is significantly better than other re-trained machine learning-based approaches and yields as the similar MCC as of re-trained SolarDiagnostics. We make the source code and datasets that we use to build and evaluate SolarDiagnostics publicly-available.

由于太阳能组件价格的快速下降，房主越来越多地部署屋顶太阳能光伏 (PV) 阵列。然而，房主可能需要花费高达 375 美元来诊断他们损坏的屋顶太阳能光伏系统。因此，最近，人们对自动和被动地检查太阳能光伏阵列的潜在损坏越来越感兴趣。不幸的是，最近利用机器学习技术的方法在区分太阳能光伏阵列损坏与其他太阳能退化（例如，阴影、灰尘、雪）方面存在局限性。为了解决这个问题，我们设计了一个新系统——SolarDiagnostics，它可以使用屋顶图像以较低的成本自动检测和分析屋顶太阳能光伏阵列的损坏情况。在本质上，SolarDiagnostics 首先利用 K-Means 算法来隔离屋顶对象以提取太阳能电池板驻留轮廓。然后，SolarDiagnostics 使用卷积神经网络来准确识别和表征每个太阳能电池板驻留轮廓上的损坏。我们通过构建低成本原型并使用由深度卷积生成对抗网络生成的 60,000 个损坏的太阳能光伏阵列图像来评估 SolarDiagnostics。我们发现 SolarDiagnostics 能够检测损坏的太阳能光伏阵列，马修斯相关系数 (MCC) 为 1.0。此外，预训练的 SolarDiagnostics 产生 0.95 的 MCC，这明显优于其他重新训练的基于机器学习的方法，并且产生与重新训练的 SolarDiagnostics 类似的 MCC。