Reliability Assessment of Grid Connected Solar PV System for Technical Institute

Abstract

Electrical energy performs an important role to fulfill the daily needs of modern society and also ensures the sustainable growth of humankind. The need for electricity has been growing continuously over the decades. Conventional energy sources contribute to a large extends with atmospheric pollution which results in the greenhouse effect. This problem can easily overcome by the rigorous use of other or renewable energy sources. These energy sources are freely available everywhere with almost free of cost, pollution-free, and non-exhaustible. Solar photovoltaic energy conversion technology will be the frontrunner in the present market. The grid-connected photovoltaic power system is always connected to the grid to meet the local load demand and excess power is supplied back to the grid. Reliability assessment and continuous availability of such systems are important considerations in terms of continuous power generations to satisfy customer needs and demand during its lifetime. This research is based on evaluating and defining the contribution of the photovoltaic system to improve the overall performance of 20 KW system installed at Nashik, Maharashtra. This can be achieved with the design of a system for minimum cost and size with better technical and economic aspects with appropriate component selections as per environmental conditions and geographical location. Also, the prediction model is developed to forecast the PV output power generation during its extracted lifetime. This will help the user to manage the energy demand by setting levels of appropriate import and export of energy into or to the grid. The system is modeled in HOMER software to achieve techno-economic aspects of the system. It is seen that the calculated value of annual energy produced by the designed PV system is 29660 KWh and that obtained from simulation results using HOMER software is 28526 KWh. The electrical output power generation Prediction model is developed using Adaptive Neuro-Fuzzy Inference System (ANFIS)