Automatic Damage Detection on Rooftop Solar Photovoltaic Arrays

Homeowners may spend up to ~$375 to diagnose their damaged rooftop solar PV systems. Thus, recently, there is a rising interest to inspect potential damage on solar PV arrays automatically and passively. Unfortunately, current approaches may not reliably distinguish solar PV array damage from other degradation (e.g., shading, dust, snow). To address this issue, 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. We evaluate SolarDiagnostics by building a lower cost (~$35) prototype and using 60,000 damaged solar PV array images. We find that pre-trained SolarDiagnostics is able to detect damaged solar PV arrays with a Matthews Correlation Coefficient of 0.95.

[1]  Soteris A. Kalogirou,et al.  Fault detection and diagnosis methods for photovoltaic systems: A review , 2018, Renewable and Sustainable Energy Reviews.

[2]  George Makrides,et al.  Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems , 2019, Renewable Energy.

[3]  Andreas Spanias,et al.  A cyber-physical system approach for photovoltaic array monitoring and control , 2017, 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA).

[4]  Prashant J. Shenoy,et al.  SolarClique: Detecting Anomalies in Residential Solar Arrays , 2018, COMPASS.

[5]  Qi Li,et al.  SolarFinder: Automatic Detection of Solar Photovoltaic Arrays , 2020, 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[6]  Ahmed Benlarabi,et al.  Photovoltaic system fault identification methodology based on I-V characteristics analysis , 2019, XIAMEN-CUSTIPEN WORKSHOP ON THE EQUATION OF STATE OF DENSE NEUTRON-RICH MATTER IN THE ERA OF GRAVITATIONAL WAVE ASTRONOMY.

[7]  M. Dhimish,et al.  The impact of cracks on photovoltaic power performance , 2017 .

[8]  Sabri Boughorbel,et al.  Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric , 2017, PloS one.