 A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV systemS. Sheik Mohammed, D. Devaraj and T.P. Imthias Ahamed Energy, 2016, vol. 112, issue C, 1096-1106 Abstract: This paper presents a novel hybrid algorithm to search the maximum power point (MPP) for the solar PV system. The proposed algorithm is a combination of two techniques i.e., the conventional Perturb & Observe (P&O) algorithm and Learning Automata (LA) optimization. To evaluate the proposed algorithm, a unique PV system model is designed for a number of different scenarios with various weather conditions. For each scenario, an exhaustive simulation is carried out and the results are compared with the conventional P&O MPPT algorithm. The results demonstrate that the proposed MPPT method has significantly improved the tracking performance, response to the fast changing weather conditions and also has less oscillation around MPP as compared to the conventional P&O MPPT and Modified P&O MPPT. The performance of proposed hybrid MPP algorithm is demonstrated experimentally. The results show that overall dynamic response of the proposed algorithm is remarkably better than conventional P&O MPPT and the Modified P&O MPPT algorithm. Keywords: Maximum Power Point Tracking; Perturb & Observe; Learning Automata; h-POLA; Interleaved boost converter; Simulation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:112:y:2016:i:c:p:1096-1106 DOI: 10.1016/j.energy.2016.07.024 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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