  The uncertainties associated with technology- and geography-specific
degradation 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 existing
characterization methods, however, cannot effectively mine/decode these
datasets to identify various degradation pathways of the corresponding solar
modules. In this paper, we propose a new methodology, i.e., the Suns-Vmp
method, which offers a simple and powerful approach to monitoring and
diagnosing time-dependent degradation of solar modules by physically mining the
MPP data. The algorithm reconstructs "IV" curves by using the natural
illumination- and temperature-dependent daily MPP characteristics as
constraints to fit the physics-based compact model. These synthetic IV
characteristics are then used to determine the time-dependent evolution of
circuit parameters (e.g., series resistance) which in-turn allows one to deduce
the dominant degradation mode (e.g., corrosion) for the modules. The proposed
method has been applied to analyze the MPP data from a test facility at the
National Renewable Energy Laboratory (NREL). Our analysis indicates that the
solar modules degraded at a rate of 0.7 %/year due to discoloration and
weakened solder bonds. These conclusions are independently validated by outdoor
IV measurement and on-site imaging characterization. Integrated with
physics-based degradation models or machine learning algorithms, the method can
also serve to predict the lifetime of PV systems.
