Maximum Power Point Tracking using Grey Wolf Technique Under Fast-Changing Irradiance
Rubi Debbarma1, Champa Nandi2
1Rubi Debbarma, Department of Electrical Engineering, Tripura University, Suryamaninagar, Tripura, India.
2Dr. Champa Nandi*, Department of Electrical Engineering, Tripura University, Suryamaninagar, Tripura, India.
Manuscript received on August 16, 2020. | Revised Manuscript received on August 27, 2020. | Manuscript published on September 10, 2020. | PP: 365-371 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.K78380991120 | DOI: 10.35940/ijitee.K7838.0991120
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, maximum power point tracking (MPPT) using Grey wolf optimization (GWO) algorithm is presented using MATLAB/Simulink. As we know that meta-heuristic or nature-inspired algorithm has proven to be superior in performance compared to the conventional MPPT methods. Grey Wolf optimization algorithm is a meta-heuristic algorithm based on the hunting behaviour of grey wolves. The proposed system includes modelling of PV system under changing irradiance and the MPPT control is driven by GWO algorithm. Most of the conventional MPPTs are unable to track multiple peaks and also shows oscillations on the output side, for this reason proposed MPPT algorithm is used in this paper. For eliminating oscillations, this algorithm has proven to be better compared to perturb and observe (P&O) and particle swarm optimization (PSO). The results are compared in terms of output power.
Keywords: Grey Wolf Optimization, Maximum Power Point Tracking, Particle Swarm Optimization, Perturb and Observe, Photovoltaic PV System.
Scope of the Article: Swarm Intelligence