A goal of the Tribal Colleges and Universities Program (TCUP) is to increase the science, technology, engineering and mathematics (STEM) instructional and research capacities of specific institutions of higher education that serve the Nation's indigenous students. Expanding the research efforts of faculty at these institutions expands the opportunities of their students to pursue challenging, rewarding careers in STEM fields, provides for research studies in areas that may be culturally significant, and encourages a community and generational appreciation for science and mathematics education. This project aligns directly with that goal, using a design that will facilitate progression and advancement of American Indian students by providing training in experimental field research, data collection and analysis, general STEM-based concepts, technical report writing, and presentation skills. It can prepare students for careers in the competitive and growing field of sustainable energy production. Moreover, the results from this research may advance knowledge and understanding within the field of solar energy performance and reliability. It can have practical applications for both manufacturers and consumers.
The overarching goal of the potentially transformative research is to explore climatic factors that affect solar module performance and reliability, and develop a framework for the performance, failure and reliability assessment of solar energy systems. This project will be implemented in collaboration with Argonne National Laboratory (ANL), which has recently installed a state-of-the-art solar energy comparative research facility, the first of its kind in the Midwest. The objectives of this research include (1) Develop a framework to model the efficiency and reliability of solar energy systems, to estimate the system performance at any given point in time, for new and in particular, used solar energy systems. (2) Develop a method to denoise and convert actual raw continuous PV system and weather data (e.g. incoming solar irradiation, temperature, generated DC electricity, inverter converted AC electricity) into useful performance metrics. (3) Verify the accuracy of the solar energy system performance model through comparison to real-time solar energy system performance data.
