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

Dhanup S. Pillai, J. Prasanth Ram, A. M.Y.M. Ghias, Md Apel Mahmud, N. Rajasekar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8897615
Pages (from-to)6594-6608
Number of pages15
JournalIEEE Transactions on Power Electronics
Volume35
Issue number6
Early online date13 Nov 2019
DOIs
Publication statusPublished - Jun 2020
Externally publishedYes

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