Indian Journal of Science and Technology

Abstract

Objectives: To improve the power produced by a photovoltaic system under varying climatic circumstances and thus improving the convergence speed. Methods: To improve power in this work a Fuzzy PID regulator is implemented which is tuned by hybrid Artificial Neural Network - Particle Swarm Optimization – Simulated Annealing (ANN-PSO-SA) and FCN (Fuzzy Cognitive Network) optimization algorithm. Additionally a DC/DC help converter is employed to regulate the yield intensity of the photovoltaic system. Findings: The proposed technique works on maximum power point and improves the performances of solar energy conversion capability and maintains system stability in case of quickly unstable atmospheric rules. To the best of knowledge no PID controller or regulator has implemented this hybrid optimization algorithm along with fuzzy concepts and works with differing climatic conditions. This method achieves the advantages of the fuzzy techniques along with optimization techniques. Along with achieving maximum power the proposed controller achieves constant voltage control. The DC-DC boost converter makes use of the output of PV panel and is responsible for regulating the output power. The FCN utilized is responsible for maintaining an equilibrium condition at varying climatic conditions. Improvements: The tuned controller is compared to the conventional MPPT algorithms with specification rise time, overshoot and delay time. This is demonstrated in the comparison results shown in the results section.

Keywords: DC/DC Help Converter, Fuzzy Cognitive Network (FCN), Maximum Power Point Tracking (MPPT) and Photovoltaic (PV) System