A Novel MPPT Method Based on Cuckoo Search Algorithm and Golden Section Search Algorithm for Partially Shaded PV System

• Published in: Canadian journal of electrical and computer engineering Vol. 42; no. 3; pp. 173 - 182
• Main Authors: Nugraha, Dimas Aji, Lian, K. L., Suwarno
• Format: Journal Article
• Language: English
• Published: Montreal IEEE Canada 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer simulation; Cuckoo search (CS); Experiments; golden section search (GSS); Heuristic methods; Maximum power point trackers; maximum power point tracking (MPPT); Maximum power tracking; Optimization; partial shading; photovoltaic (PV) system; Photovoltaic cells; Photovoltaic systems; Search algorithms; Search problems; Shading; Simulation; Solar cells; Studies
• ISSN: 0840-8688
• DOI: 10.1109/CJECE.2019.2914723

Partial shading is a common and difficult problem to be solved in a photovoltaic (PV) system. Numerous efforts have been introduced to mitigate this problem. Some commonly used approaches are deploying some metaheuristic (MH) algorithm to track the multiple-peak P-V curve of partially shaded PV system. Cuckoo search (CS) is a new optimization algorithm based on the MH approach. It has been used to solve an optimization problem in many applications, including the maximum power point tracking (MPPT) problem. The CS algorithm performs well in tracking the global maximum power point (GMPP). However, just like any other MH algorithm, there is still a dilemmatic trading between their accuracy and the tracking time needed to find GMPP. This paper proposes a new MPPT algorithm by combining the CS algorithm with golden section search (GSS) to take beneficial features from both the algorithms. To validate the proposed algorithm, it is evaluated with various cases of partial shading. The simulation and experimental results show a noticeable performance improvement compared with the original CS algorithm and other MH algorithms.