SolarDiagnostics: Automatic damage detection on rooftop solar photovoltaic arrays

Abstract

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.