Real‐time monitoring and diagnosis of photovoltaic system degradation only using maximum power point—the Suns‐Vmp method

• Published in: Progress in photovoltaics 2019-01, Vol.27 (1), p.55-66
• Main Authors: Sun, Xingshu, Chavali, Raghu Vamsi Krishna, Alam, Muhammad Ashraful
• Format: Article
• Language: English
• Subjects: Algorithms; Alternative energy sources; Bonding strength; characterization; Constraint modelling; Current voltage characteristics; Degradation; Discoloration; field data; Geography; Identification methods; Machine learning; Maximum power; maximum power point; Modules; Monitoring; Photovoltaic cells; reliability; Solar energy; system level; Temperature dependence; Time dependence; Viability
• ISSN: 1062-7995; 1099-159X
• DOI: 10.1002/pip.3043

The uncertainties associated with technology‐specific and geography‐specific degradation rates make it difficult to calculate the levelized cost of energy, and thus the economic viability of solar energy. In this regard, millions of fielded photovoltaic modules may serve as a global testbed, where we can interpret the routinely collected time series maximum power point (MPP) data to assess the time‐dependent “health” of solar modules. The existing characterization methods, however, cannot effectively mine/decode these datasets to identify various degradation pathways. In this paper, we propose a new methodology called the Suns‐Vmp method, which offers a simple yet powerful approach to monitoring and diagnosing time‐dependent degradation of solar modules by using the MPP data. The algorithm reconstructs “IV” curves by using the natural illumination‐dependent and temperature‐dependent daily MPP characteristics as constraints to fit physics‐based circuit models. These synthetic IV characteristics are then used to determine the time‐dependent evolution of circuit parameters (eg, series resistance), which in turn allows one to deduce the dominant degradation modes (eg, solder bond failure) of solar modules. The proposed method has been applied to a test facility at the National Renewable Energy Laboratory. Our analysis indicates that the solar modules degraded at a rate of ~0.7%/year because of discoloration and weakened solder bonds. These conclusions are validated by independent outdoor IV measurements 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 photovoltaic systems. Inspired by the well‐known Suns‐Voc method, we have developed a novel technique called the Suns‐Vmp method that can interpret the routinely collected maximum power point (MPP) data of PV systems to produce insightful information regarding the underlying degradation mechanisms. The method can be applied to analyze solar modules installed across the globe to establish a comprehensive database of PV degradation. The resulting database will eventually facilitate geographic‐ and technology‐specific reliability‐aware design to improve module lifetime.