Degradation Root-Cause Numerical Analysis of Around 100 PV Modules Installed in Hot and Arid Desert Environment

The voluminous amount of data produced by a modern solar farm suggests opportunities for smart farm management, just-in-time cleaning and maintenance, predictive logistics, etc. In this regard, a physics-based statistical approach called the Suns-Vmp method has been used to determine the pseudo I-V characteristics of a PV system based exclusively on measured time-series data for DC power output. These pseudo I-V characteristics have then been used to decode the degradation pathways within the photovoltaic (PV) system. In this work, we compare real-time field-measured I-V characteristics obtained through a high-speed data logger with pseudo I-V characteristics derived from the Suns-Vmp method. The objective is to evaluate the potential and limitations of both approaches in interpreting field data and to assess the frequency of calibration required for the Suns-Vmp method to yield robust insights into solar farm performance. The methodology was rigorously tested on 96 PV modules of different technologies and configurations installed in the outdoor test facility of Dubai Electricity & Water Authority (DEWA) R&D, Dubai, UAE. The power degradation results from the 96 PV modules indicated that the median degradations of the mono- and poly-crystalline technologies were estimated to be around 0.21%/year and 0.49%/year. This research sheds light on the feasibility and reliability of the Suns-Vmp method for assessing the performance and reliability of solar farms, offering valuable insights for effective solar farm management and maintenance.