Optimisation of Solar Photovoltaic (PV) Parameters Using Meta-Heuristics

Valentine Obiora; Chitta Saha; Ammar Al Bazi; Koushik  Guha

doi:10.1007/s00542-020-05066-3

Optimisation of Solar Photovoltaic (PV) Parameters Using Meta-Heuristics

Valentine Obiora, Chitta Saha, Ammar Al Bazi, Koushik Guha

National Institute of Technology, Silchar

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

238 Downloads (Pure)

Abstract

This paper presents a critical analysis of the meta-heuristic techniques used in various researches on the optimisation of photovoltaic (PV) parameters, which involves the use of different algorithms in order to extract and improve these parameters from the Single Diode Model (SDM), Double Diode Model (DDM) and Three Diode Model (TDM) respectively. The modelling parameters such as the photon current, saturation current, the series and parallel resistances are investigated to understand the optimum value. It will also equate the results of datasheet values from PV manufactures with experiment values obtained from PV module measurements. The meta-heuristics techniques to be considered include Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Harmony Search (HS), Flower Pollination Algorithm (FPA), Simulated Annealing (SA), Teaching Learning Based Optimisation (TLBO), and other different hybrid solutions to optimize the convergence speed. Root Mean Square Error (RMSE) is used as a performance indicator of each meta-heuristic technique. These optimisation techniques are utilised in extracting the parameters of a 5W polycrystalline panel at Standard testing conditions. The results presented in this paper compared the performances of the mentioned meta-heuristics on the single, double and triple diode models respectively.

Original language	English
Pages (from-to)	3161-3169
Number of pages	9
Journal	Microsystem Technologies
Volume	27
Issue number	8
Early online date	28 Oct 2020
DOIs	https://doi.org/10.1007/s00542-020-05066-3
Publication status	Published - Aug 2021

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/s00542-020-05066-3

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Funder

Institute of Future Cities and Transport, Coventry University

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Hardware and Architecture
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s00542-020-05066-3

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Cite this

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title = "Optimisation of Solar Photovoltaic (PV) Parameters Using Meta-Heuristics",

abstract = "This paper presents a critical analysis of the meta-heuristic techniques used in various researches on the optimisation of photovoltaic (PV) parameters, which involves the use of different algorithms in order to extract and improve these parameters from the Single Diode Model (SDM), Double Diode Model (DDM) and Three Diode Model (TDM) respectively. The modelling parameters such as the photon current, saturation current, the series and parallel resistances are investigated to understand the optimum value. It will also equate the results of datasheet values from PV manufactures with experiment values obtained from PV module measurements. The meta-heuristics techniques to be considered include Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Harmony Search (HS), Flower Pollination Algorithm (FPA), Simulated Annealing (SA), Teaching Learning Based Optimisation (TLBO), and other different hybrid solutions to optimize the convergence speed. Root Mean Square Error (RMSE) is used as a performance indicator of each meta-heuristic technique. These optimisation techniques are utilised in extracting the parameters of a 5W polycrystalline panel at Standard testing conditions. The results presented in this paper compared the performances of the mentioned meta-heuristics on the single, double and triple diode models respectively.",

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Optimisation of Solar Photovoltaic (PV) Parameters Using Meta-Heuristics

Abstract

Bibliographical note

Funder

ASJC Scopus subject areas

UN SDGs

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