Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon-inspired optimization with Taguchi method

Jeng-Shyang Pan, Ai-Qing Tian, Václav Snášel, Lingping Kong and Shu-Chuan Chu

Energy, 2022, vol. 251, issue C

Abstract: The simulation, control and optimization of photovoltaic (PV) modules require the extraction of parameters from actual data and the construction of highly accurate PV cells. Multiple PV modules supplying power to a common load is the most common form of power distribution in PV systems. In these PV systems, providing separate maximum power point tracking (MPPT) technology for each PV module would increase the cost of the entire system. Determining how to accurately identify the internal parameter information of the PV modules and control the MPPT technology is the problem solved in this paper. we proposes an improved pigeon-inspired optimization (PIO) algorithm based on Taguchi method to solve the above problems. In this paper, we use the CEC2014 test library for testing and cross-sectional comparison. Experimental results show that the PIO algorithm based on Taguchi method is more competitive than other algorithms. The proposed algorithm uses measurement data to extract the unknown parameter in the PV modules and then uses this information to optimize the MPPT of all PV systems under partially shaded conditions (PSCs). Simulation results demonstrate the fitness value of the unknown parameters extracted by TPIO is 9.7525 × 10−4, which is better than the compared algorithms.

Keywords: Pigeon-inspired optimization; Maximum power point tracking; Partially shaded conditions; Parameters estimation; Photovoltaic (PV) system; Taguchi method (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222007666
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007666

DOI: 10.1016/j.energy.2022.123863

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().