Performance Analysis of a Rooftop Grid-Connected Photovoltaic System in North-Eastern India, Manipur - Thokchom Suka Deba Singh; Benjamin A. Shimray; Sorokhaibam Nilakanta Meitei (Energies)
The performance analysis of a 10 kWp rooftop grid connected solar photovoltaic (PV) system located in Sagolband, Imphal, India has been studied for 5 years. The key technical parameters such as array yield (<semantics>YA</semantics>), reference yield (<semantics>YR</semantics>), final yield (<semantics>YF</semantics>), capacity utilization factor (CUF), PV system efficiency (<semantics>ηSys</semantics>), and performance ratio (PR) were used to investigate its performance. In this study, the experimentally measured results of the system’s performance for the five years (i.e., July 2018 to June 2023) were compared with the predicted results, which were obtained using PVsyst V7.3.0 software. The measured energy generation in 5 years (including 40 days OFF due to inverter failure on 17 June 2019 because of a surge, which was resolved on 27 July 2019) was 58,911.3 kWh as compared to the predicted 77,769 kWh. The measured daily average energy yield was 3.2 kWh/kWp as compared to the predicted 4.2 kWh/kWp. It can be seen that there was a large difference between the real and predicted values, which may be due to inverter downtime, local environmental variables (e.g., lower-than-expected solar irradiation and temperature impacts), and the possible degradation of photovoltaic modules over time. The measured daily average PR of the system was 70.71%, and the maximum occurred in the months of October, November, December, and January, which was almost similar to the predicted result. The measured daily average CUF of the system was 13.36%, and the maximum occurred in the months of March, April, and May. The measured daily average system efficiency was 11.31%. Moreover, the actual payback was 4 years and 10 months, indicating strong financial viability despite the system’s estimated lifespan of 25 years. This study highlights the importance of regular maintenance, fault detection, and better predictive modelling for more accurate energy projections, and also offers an understanding of real-world performance fluctuations.