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An Accurate, Shade Detection-Based Hybrid Maximum Power Point Tracking Approach for PV Systems

journal contribution
posted on 2020-06-01, 00:00 authored by D S Pillai, J P Ram, A M Y M Ghias, Apel MahmudApel Mahmud, N Rajasekar
© 1986-2012 IEEE. Optimizing the operational performance of maximum power point trackers during multiple peak partial shading conditions (PSCs) prevails as a major challenge in photovoltaic power generation. Though many of the newly evolved soft computing and heuristic methods are compatible during PSCs, the need for such techniques in uniform irradiation levels is uncertain because most of these algorithms produce extensive oscillations before converging to the global maximum power point (GMPP). On the other hand, adopting conventional perturb and observe (P&O) algorithm is meritorious to reduce the transient power and voltage oscillations. Therefore, this article presents a proficient hybrid tracking technology that provides an adequate tradeoff between conventional P&O and advanced soft computing techniques by accurately detecting shade occurrences. For which, the uniqueness in the operating point conductance of P&O at the leftmost power peak in the P-V curve is utilized. Subsequently, the proposed convention utilizes P&O for two major purposes: to track MPP in uniform irradiance, and to detect PSCs. More importantly, even during PSCs, the detection algorithm is well designed to operate at GMPP with the conventional P&O method itself, and, only for extreme shade cases, flower pollination algorithm is introduced to track GMPP. The pre-eminence of the proposed technology to track GMPP with reduced transient oscillations by discriminating shade occurrences is demonstrated via extensive experimental studies in this article.

History

Journal

IEEE Transactions on Power Electronics

Volume

35

Issue

6

Pagination

6594 - 6608

Publisher

IEEE

Location

Piscataway, NJ

ISSN

0885-8993

eISSN

1941-0107

Language

English

Publication classification

C1 Refereed article in a scholarly journal