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The uncertainties associated with technology- and geography-specificdegradation rates make it difficult to calculate the levelized cost of energy(LCOE), and thus the economic viability of solar energy. In this regard,millions of fielded photovoltaic (PV) modules may serve as a global testbed,where we can interpret the routinely collected maximum power point (MPP)time-series data to assess the time-dependent "health" thereof. The existingcharacterization methods, however, cannot effectively mine/decode thesedatasets to identify various degradation pathways of the corresponding solarmodules. In this paper, we propose a new methodology, i.e., the Suns-Vmpmethod, which offers a simple and powerful approach to monitoring anddiagnosing time-dependent degradation of solar modules by physically mining theMPP data. The algorithm reconstructs "IV" curves by using the naturalillumination- and temperature-dependent daily MPP characteristics asconstraints to fit the physics-based compact model. These synthetic IVcharacteristics are then used to determine the time-dependent evolution ofcircuit parameters (e.g., series resistance) which in-turn allows one to deducethe dominant degradation mode (e.g., corrosion) for the modules. The proposedmethod has been applied to analyze the MPP data from a test facility at theNational Renewable Energy Laboratory (NREL). Our analysis indicates that thesolar modules degraded at a rate of 0.7 %/year due to discoloration andweakened solder bonds. These conclusions are independently validated by outdoorIV measurement and on-site imaging characterization. Integrated withphysics-based degradation models or machine learning algorithms, the method canalso serve to predict the lifetime of PV systems.

In-Situ Self-Monitoring of Real-Time Photovoltaic Degradation Only Using Maximum Power Point: the Suns-Vmp Method