Optimal Parameter Estimation for Solar PV Panel Based on ANN and Adaptive Particle Swarm Optimization

Wai Lun Lo; Henry Shu Hung Chung; Richard Tai Chiu Hsung; Hong Fu; Tony Yulin Zhu; Tak Wai Shen; Harris Sik Ho Tsang

doi:10.3390/a18100598

Optimal Parameter Estimation for Solar PV Panel Based on ANN and Adaptive Particle Swarm Optimization

Wai Lun Lo^*
, Henry Shu Hung Chung
, Richard Tai Chiu Hsung
, Hong Fu
, Tony Yulin Zhu
, Tak Wai Shen
, Harris Sik Ho Tsang

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

7 Downloads (CityUHK Scholars)

Abstract

Parameter estimation for solar photovoltaic panels is a popular research topic in green energy. Model parameters can be used for fault diagnosis in solar panels. Artificial neural network (ANN) approaches have been developed to estimate the model parameters of solar panels. In this study, an ANN and Adaptive Particle Swarm Optimization (APSO) approach for model parameter estimation of solar panel is proposed. Load perturbation is injected at the output of the solar PV panel, and the load voltage and current time series are measured. The current and voltage vectors are used as inputs for an ANN, which is used as a classifier for the ranges of the model parameters. The population of the APSO is initialized according to the results of the ANN classifier, and the APSO algorithm is then used to estimate the model parameters of the PV panel. Simulations and experimental studies show that the proposed method has better performance than conventional PSO, and it requires a smaller number of generations to achieve an average parameter estimation error of less than 5%. © 2025 by the authors.

Original language	English
Article number	598
Journal	Algorithms
Volume	18
Issue number	10
Online published	24 Sept 2025
DOIs	https://doi.org/10.3390/a18100598
Publication status	Published - Oct 2025

Funding

The work described in this study was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Reference No.: UGC/FDS13/E01/21).

UN SDGs

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

SDG 7 Affordable and Clean Energy

Research Keywords

Adaptive Particle Swarm Optimization
artificial neural network
PV panel parameter estimation

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

RGC Funding Information

RGC-funded

Access Output

10.3390/a18100598

362370624.pdfFinal Published version, 766 KBLicence: CC BY

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@article{fa489e2ea1ae4c94b9ba0f2b555c661d,

title = "Optimal Parameter Estimation for Solar PV Panel Based on ANN and Adaptive Particle Swarm Optimization",

abstract = "Parameter estimation for solar photovoltaic panels is a popular research topic in green energy. Model parameters can be used for fault diagnosis in solar panels. Artificial neural network (ANN) approaches have been developed to estimate the model parameters of solar panels. In this study, an ANN and Adaptive Particle Swarm Optimization (APSO) approach for model parameter estimation of solar panel is proposed. Load perturbation is injected at the output of the solar PV panel, and the load voltage and current time series are measured. The current and voltage vectors are used as inputs for an ANN, which is used as a classifier for the ranges of the model parameters. The population of the APSO is initialized according to the results of the ANN classifier, and the APSO algorithm is then used to estimate the model parameters of the PV panel. Simulations and experimental studies show that the proposed method has better performance than conventional PSO, and it requires a smaller number of generations to achieve an average parameter estimation error of less than 5\%. {\textcopyright} 2025 by the authors.",

keywords = "Adaptive Particle Swarm Optimization, artificial neural network, PV panel parameter estimation",

author = "Lo, \{Wai Lun\} and Chung, \{Henry Shu Hung\} and Hsung, \{Richard Tai Chiu\} and Hong Fu and Zhu, \{Tony Yulin\} and Shen, \{Tak Wai\} and Tsang, \{Harris Sik Ho\}",

year = "2025",

month = oct,

doi = "10.3390/a18100598",

language = "English",

volume = "18",

journal = "Algorithms",

issn = "1999-4893",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "10",

}

TY - JOUR

T1 - Optimal Parameter Estimation for Solar PV Panel Based on ANN and Adaptive Particle Swarm Optimization

AU - Lo, Wai Lun

AU - Chung, Henry Shu Hung

AU - Hsung, Richard Tai Chiu

AU - Fu, Hong

AU - Zhu, Tony Yulin

AU - Shen, Tak Wai

AU - Tsang, Harris Sik Ho

PY - 2025/10

Y1 - 2025/10

N2 - Parameter estimation for solar photovoltaic panels is a popular research topic in green energy. Model parameters can be used for fault diagnosis in solar panels. Artificial neural network (ANN) approaches have been developed to estimate the model parameters of solar panels. In this study, an ANN and Adaptive Particle Swarm Optimization (APSO) approach for model parameter estimation of solar panel is proposed. Load perturbation is injected at the output of the solar PV panel, and the load voltage and current time series are measured. The current and voltage vectors are used as inputs for an ANN, which is used as a classifier for the ranges of the model parameters. The population of the APSO is initialized according to the results of the ANN classifier, and the APSO algorithm is then used to estimate the model parameters of the PV panel. Simulations and experimental studies show that the proposed method has better performance than conventional PSO, and it requires a smaller number of generations to achieve an average parameter estimation error of less than 5%. © 2025 by the authors.

AB - Parameter estimation for solar photovoltaic panels is a popular research topic in green energy. Model parameters can be used for fault diagnosis in solar panels. Artificial neural network (ANN) approaches have been developed to estimate the model parameters of solar panels. In this study, an ANN and Adaptive Particle Swarm Optimization (APSO) approach for model parameter estimation of solar panel is proposed. Load perturbation is injected at the output of the solar PV panel, and the load voltage and current time series are measured. The current and voltage vectors are used as inputs for an ANN, which is used as a classifier for the ranges of the model parameters. The population of the APSO is initialized according to the results of the ANN classifier, and the APSO algorithm is then used to estimate the model parameters of the PV panel. Simulations and experimental studies show that the proposed method has better performance than conventional PSO, and it requires a smaller number of generations to achieve an average parameter estimation error of less than 5%. © 2025 by the authors.

KW - Adaptive Particle Swarm Optimization

KW - artificial neural network

KW - PV panel parameter estimation

UR - https://www.scopus.com/pages/publications/105020173161

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-105020173161&origin=recordpage

U2 - 10.3390/a18100598

DO - 10.3390/a18100598

M3 - RGC 21 - Publication in refereed journal

SN - 1999-4893

VL - 18

JO - Algorithms

JF - Algorithms

IS - 10

M1 - 598

ER -

Optimal Parameter Estimation for Solar PV Panel Based on ANN and Adaptive Particle Swarm Optimization

Abstract

Funding

UN SDGs

Research Keywords

Publisher's Copyright Statement

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this