We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Parameter estimation of photovoltaic model via parallel particle swarm optimization algorithm.
- Authors
Ma, Jieming; Man, Ka Lok; Guan, Sheng‐Uei; Ting, T. O.; Wong, Prudence W. H.
- Abstract
Recently, bio-inspired metaheuristic algorithms have been widely used as powerful optimization tools to estimate crucial parameters of photovoltaic (PV) models. However, the computational cost involved in terms of the time increases as data size or the complexity of the applied PV electrical model increases. Hence, to overcome these limitations, this paper presents the parallel particle swarm optimization (PPSO) algorithm implemented in Open Computing Language ( Open CL) to solve the parameter estimation problem for a wide range of PV models. Experimental and simulation results demonstrate that the PPSO algorithm not only has the capability of obtaining all the parameters with extremely high accuracy but also dramatically improves the computational speed. This is possible and is shown in this work via the inherent capabilities of the parallel processing framework. Copyright © 2015 John Wiley & Sons, Ltd.
- Subjects
PHOTOVOLTAIC power systems; PARTICLE swarm optimization; PARAMETER estimation; ALGORITHMS; METAHEURISTIC algorithms; PARALLEL algorithms; OPENCL (Computer program language)
- Publication
International Journal of Energy Research, 2016, Vol 40, Issue 3, p343
- ISSN
0363-907X
- Publication type
Article
- DOI
10.1002/er.3359