• Poonam Pandey
• Dr. Vijayalaxmi Biradar

Solar Photovoltaic-Thermal (PV-T) electric power systems are notable options in the field of renewable energy systems, offering a potential solution for sustainable electricity generation. Nevertheless, the sporadic nature of solar irradiation presents difficulties in maintaining a consistent power output and synchronizing with the grid. This work investigates the utilization of Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers to tackle these difficulties by normalizing the frequency of Solar PV-T electric power systems. ANFIS controllers provide a resilient method for controlling and optimizing systems in real-time, utilizing the collective advantages of fuzzy logic and neural networks. This paper provides a thorough analysis that shows how ANFIS controllers effectively improve the performance of Solar PV-T electric power systems. ANFIS controllers allow for accurate adjustment of system parameters by utilizing real-time data inputs, hence ensuring precise regulation of power output to align with grid frequency requirements. The frequency normalization capabilities of Solar PV-T systems allows for smooth integration into current power grids, reducing variations and enhancing overall system stability.

Solar PV-Thermal, Electric Power Systems, ANFIS Controllers, Frequency Normalization, Renewable Energy, Grid Integration, Real-Time Control, Sustainability, Fuzzy Logic, Neural Networks.

"", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.i47-i56, December-2023, Available :http://www.jetir.org/papers/JETIR2312807.pdf

"", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppi47-i56, December-2023, Available at : http://www.jetir.org/papers/JETIR2312807.pdf