UTHM Institutional Repository

Particle swarm optimization based maximum power point tracking for partially shaded photovoltaic arrays

Teo, Kenneth Tze Kin and Lim, Pei Yi and Chua, Bih Lii and Goh , Hui Hwang and Tan, Min Keng (2016) Particle swarm optimization based maximum power point tracking for partially shaded photovoltaic arrays. International Journal of Simulation: Systems, Science & Technology, 17 (34). 20.1-20.7. ISSN 1473804x

Full text not available from this repository.


This paper presents particle swarm optimization based perturb and observe (PSO-P&O) algorithm for maximizing output power of photovoltaic (PV) array under partially shaded conditions (PSC). During PSC, the P-V characteristic of PV will become more complex with multiple maximum power points (MPP). Most of the conventional maximum power point tracking (MPPT) algorithms, such as P&O, will be trapped at the local MPP and hence limiting the maximum power generation. As such, investigation on PSO-P&O algorithm is carried out to maximize the PV generated power principally under PSC operation. The performances of conventional P&O and the proposed PSO-P&O algorithms are investigated particularly on the transient and steady state responses under various shaded conditions. The simulation results show the developed PSO- P&O algorithm is able to facilitate the PV array to reach the global MPP and assist the PV array to produce more stable output power compared to the conventional P&O algorithm.

Item Type: Article
Uncontrolled Keywords: Photovoltaic array; partially shaded conditions; particle swarm optimization; perturb and observe; MPPT
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001-1841 Production of electric energy or power. Powerplants. Central stations
Divisions: Faculty of Electrical and Electronic Engineering > Department of Electrical Power Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 Aug 2018 03:22
Last Modified: 13 Aug 2018 03:22
URI: http://eprints.uthm.edu.my/id/eprint/9981
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item