Soiling is a major source of power degradation in photovoltaic (PV) modules, with variable impact as a function of environmental conditions. For concentrated photovoltaic (CPV) modules the impact is greater due to requirements in preserving the optical path within the module. In an effort to reduce soiling a number of self-cleaning coatings have been developed and implemented in solar modules. Self-cleaning films are generally based on high (hydrophilic) or low (hydrophobic) surface energy materials that leverage extreme wetting behavior to reduce contamination, and/or photocatalytic materials that decompose contaminants with ultraviolet (UV) activation. In this study we explore a number of third-party coating materials to reduce soiling losses in Morgan Solar's Sun Simba CPV modules. The primary objective is to identify coating solutions that are mechanically robust and effective in anti-soiling, as well as compatible with our materials and optical concentration architecture. In preliminary testing two products were compared with extreme wetting characteristics. The material 1 coating reduces soiling rates to 0.8-1.4%/month, whereas the material 2 coating failed mechanically and the module soiled at a rate of 6.6%/month as a consequence - the control module soiled at a rate of 4.1%/month for comparison. We followed-up with the deployment of a long-term study that will compare 4 self-cleaning/easy-to-clean products. The materials have been selected for their extreme wetting characteristics and suitable optical properties. Deposition parameters were optimized and the coated modules have been mounted for evaluation in the Mojave Desert, California.

We present a laser-based module tester for performance rating of CPV modules. Our test methodology uses a system of collimated lasers to characterize each individual concentrator unit, then combines the results to model a full module IV curve. Power ratings and IV curves of modules evaluated by the laser tester are compared with outdoor measurements.

An electroluminescence test for a Concentrated PV system is presented with the objective of capturing high resolution pseudo-efficiency maps that highlight optical defects in the concentrator system. Key parameters of the experimental setup and imaging system are presented. Image processing is discussed, including comparison of experimental to nominal results and the quantitative estimation of optical efficiency. Efficiency estimates are validated using measurements under a collimated solar simulator and ray-tracing software. Further validation is performed by comparison of the electroluminescence technique to direct mapping of the optical efficiency. Initial results indicate the mean estimation error for Isc is -2.4% with a standard deviation is 6.9% and a combined measurement and analysis time of less than 5 seconds per optic. An extension of this approach to in-line quality control is discussed.

Sensitivity analysis is the use of computational and statistical methods to investigate and analyze the sensitivity of a model. Within the context of product development and manufacturing it is a tool used to quantify the relative importance of known process variation within the space of design tolerances and observed manufacturing defects. In this work sensitivity analysis is shown to provide numerous benefits to the development of a CPV system, including; ranking the relative importance of manufacturing tolerances, understanding how defects interact to influence device efficiency, predicting distributions of product efficiency and manufacturing yield, comparing product designs, and providing feedback for product tolerance requirements.