Measurement of thermal and electrical parameters in photovoltaic systems for predictive and cross-correlated monitorization

Photovoltaic electricity generation is growing at an almost exponential rate worldwide, reaching 400 GW(p) of installed capacity in 2018. Different types of installations, ranging from small building integrated systems to large plants, require different maintenance strategies, including strategies for monitorization and data processing. In this article, we present three case studies at different scales (from hundreds of W-p to a 2.1 MWp plant), where automated parameter monitorization and data analysis has been carried out, aiming to detect failures and provide recommendations for optimum maintenance procedures. For larger systems, the data collected by the inverters provides the best source of information, and the cross-correlated analysis which uses these data is the best strategy to detect failures in module strings and failures in the inverters themselves (an average of 32.2% of inverters with failures was found after ten years of operation). In regards to determining which module is failing, the analysis of thermographic images is reliable and allows the detection of the failed module within the string (up to 1.5% for grave failures and 9.1% of medium failures for the solar plant after eleven years of activity). Photovoltaic (PV) systems at different scales require different methods for monitorization: Medium and large systems depend on inverter automated data acquisition, which can be complemented with thermographic images. Nevertheless, if the purpose of the monitorization is to obtain detailed information about the degradation processes of the solar cells, it becomes necessary to measure the environmental (irradiance and ambient temperature), thermal and electrical parameters (I-V characterization) of the modules and compare the experimental data with the modelling results. This is only achievable in small systems.C.T. is grateful to Fundación Séneca-CARM for grant (Exp. 19768/FPI/15). The authors want to acknowledge Pedro García Trenza (from company Drónica) and Emilio Ballester (from company CRES) for sharing data and for their helpful comments. This research was funded by MINECO-Spain (including FEDER funds): Project ENE2016-79282-C5-5-R and Fundación Séneca (Región de Murcia, Spain): Project 19882-GERM-15