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

Abstract

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.