PV Module Performance Under Real-world Test Conditions - A Data Analytics Approach

In pursuit of a higher fidelity understanding of the long-term degradation of long-lived technologies, such as photovoltaic (PV) systems, the framework of Lifetime and Degradation Science (L&DS) goes beyond initial qualification tests and investigates the underpinning mechanisms of degradation. L&DS concerns itself with the complex and multivariate signatures of the degradation process and uncovering the fundamental physical mechanisms contributing to that degradation. In the case of PV modules, this effort requires extensive continuous monitoring of PV modules' power production and climatic conditions. The responses of PV module to the stressors of the real world is cross-correlated to the simulated and accelerated stressors placed on devices in a laboratory setting. A unique, highly instrumented, outdoor test facility for PV materials, components, and systems, the Solar Durability and Lifetime Extension (SDLE) center's SunFarm, was built for the purpose of better understanding the power degradation mechanisms of PV modules and materials. The SDLE SunFarm provides an apparatus for the collection of real-world time series data consisting of output power, weather and insolation metrology. The SunFarm is comprised of 122 individual PV power plants, including 120 module-level plants and 2*8 modules, string-level plants. Output power is monitored through appropriate grid-tied inverters. The metrology package developed at CWRU for the collection of time series data provides a model to be implemented at external sites around the globe. In order to expand the ability of monitoring PV systems' performance under different climatic conditions, a global SunFarm Network was implemented among nine outdoor test facilities around the world in collaboration with academic institutions and industrial partners including commercial power plants. This thesis provides the initial data analytics on the first six months of data from 60 PV modules on the SDLE SunFarm, and serves as a model for the analytics of full dataset from the global SunFarm Network. The data was first validated by characterization of the measurement apparatus, redundancy of measurement, and time-slewing according to minimization of the time cross-correlation function using a free and open-source statistical software language and packages known as "R". Using R (v3.0.1) for clustering data analysis base upon unfiltered AC power time series showed that the data fell into six clusters, which represented the six different electrical sites of SDLE SunFarm. The data were intelligently assembled and subsampled around solar noon time. PV performance ratio (PR), which is a measure of PV modules' output at given incident power from sunlight, was used as a indicator of modules' working effectiveness. Correlations among the filtered sub-set of solar noon time PR data were discerned with hierarchical clustering analysis. K-means clustering was used to confirm the optimum number of clusters for the analytics. The clustering results differentiate modules on different physical sites, pointed out malfunctions of the PV mounting system, and incapacity of certain module brands. These results are useful for correlating different modules' response to stressors and those stressors' effects on overall performance.