@inproceedings{208cac508c6745219116e92023e67b6a,
title = "Accelerating parameter estimation for photovoltaic models via parallel particle swarm optimization",
abstract = "Bio-inspired metaheuristic algorithms have been widely proposed to estimate parameters of photovoltaic (PV) models in recent years due to its ability to handle nonlinear functions regardless of the derivatives information. However, these algorithms normally utilize multiple agents/particles in the search process, and it takes much time to search the possible solutions in the whole search domain by sequential computing devices. This paper proposes parallel particle swarm optimization (PPSO) method to extract and estimate the parameters of a PV model. The algorithm is implemented in OpenCL and is executed on Nvidia multi-core GPUs. From the simulation results, it is observed that the proposed method is capable of accelerating the computational speed with the same accuracy in comparison to sequential particle swarm optimization (PSO).",
keywords = "OpenCL, PV power generation, Photovoltaic (PV), heterogeneous computing, parallel computing, parameter estimation, particle swarm optimization (PSO)",
author = "Jieming Ma and Man, {Ka Lok} and Ting, {T. O.} and Nan Zhang and Guan, {Sheng Uei} and Wong, {Prudence W.H.}",
year = "2014",
doi = "10.1109/IS3C.2014.56",
language = "English",
isbn = "9781479952779",
series = "Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014",
publisher = "IEEE Computer Society",
pages = "175--178",
booktitle = "Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014",
note = "2nd International Symposium on Computer, Consumer and Control, IS3C 2014 ; Conference date: 10-06-2014 Through 12-06-2014",
}