Monitoring of photovoltaic systems using different solar radiation and temperature databases
Keywords:Photovoltaic, Monitoring, Data base
With the demand for electricity growing, the migration to renewable sources is a reality. In distributed generation, photovoltaic systems are a renewable and sustainable alternative to the main energy sources to generate electricity. Monitoring a photovoltaic system over its operating time guarantees its good performance. This requires solar radiation and temperature data measured at the installation site or the use of solarimetric stations databases. However, the differences between the results simulated with databases and with data measured at the installation site are not widely known, which would be the ideal case from a technical point of view. The aim of this study was to verify the feasibility of monitoring the performance of a 2.5 kWp photovoltaic system located in the city of Porto Alegre - Brazil using the System Advisor Model (SAM) modeling tool and a public database. Simulation results were compared using data provided by a station of the National Institute of Meteorology (INMET) with the results obtained with data measured at the site of the photovoltaic system. Differences were verified between the solar radiation measured on site and that of the INMET database, and the difference in accumulated radiation was 9.2% for the entire period analyzed. When comparing the measured and simulated alternating current energy using the radiation and temperature data measured on site for the non-shading time, it was found that the difference between the results was 0.5%. Using the INMET climate file, the monthly differences ranged from -6% to 14% and the difference in accumulated energy for the entire measurement period was 2.5%. The results showed that the use of a database measured by a public solarimetric station close to the site, in this case approximately 6 km away from the installation, is feasible for monitoring photovoltaic systems, since the differences found were not significant. This monitoring can identify system failures and performance loss over time.
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