Soft Computing Driven Hybrid MPPT Strategy for Photovoltaic Systems in Variable Environments

Md Samiul Alam; Muhammad Majid Gulzar; Abdulaziz M. Qwbaiban; Mohamed Soliman

doi:10.1109/ICRERA66237.2025.11283845

Soft Computing Driven Hybrid MPPT Strategy for Photovoltaic Systems in Variable Environments

Md Samiul Alam^*
, Muhammad Majid Gulzar^*
, Abdulaziz M. Qwbaiban
, Mohamed Soliman

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This research presents a comparative analysis of Maximum Power Point Tracking (MPPT) algorithms for photovoltaic (PV) systems that employ soft computing approaches. Fluctuations in irradiance and temperature create nonlinearity and partial shading issues, resulting in several local maxima on the photovoltaic curve. Traditional techniques such as Perturb and Observe (P&O) encounter difficulties in these circumstances. This issue was addressed by evaluating intelligent and hybrid methodologies, including Particle Swarm Optimization (PSO), Adaptive PSO, Teaching-Learning-Based Optimization (TLBO), Multi-Objective TLBO (MO-TLBO), and a proposed Hybrid PSO-Flying Squirrel Search Optimization (PSO-FSSO) using MATLAB/Simulink simulations. The comparative results indicate that the Hybrid PSO-FSSO attained the maximum tracking effectiveness of 99.3% with the quickest convergence time of 0.5 seconds, surpassing other methodologies. The results highlight that hybrid soft computing techniques markedly improve MPPT efficacy in fluctuating environmental conditions, providing a resilient and effective solution for future photovoltaic systems.

Original language	English
Title of host publication	14th International Conference on Renewable Energy Research and Applications, ICRERA 2025
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1303-1310
Number of pages	8
ISBN (Electronic)	9798331599898
DOIs	https://doi.org/10.1109/ICRERA66237.2025.11283845
State	Published - 2025
Event	14th International Conference on Renewable Energy Research and Applications, ICRERA 2025 - Vienna, Austria Duration: 27 Oct 2025 → 30 Oct 2025

Publication series

Name	14th International Conference on Renewable Energy Research and Applications, ICRERA 2025

Conference

Conference	14th International Conference on Renewable Energy Research and Applications, ICRERA 2025
Country/Territory	Austria
City	Vienna
Period	27/10/25 → 30/10/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Flying squirrel search optimization
Maximum power point tracking
Particle swarm optimization
Photovoltaic
Teaching learning-based optimization

ASJC Scopus subject areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Control and Optimization

Access to Document

10.1109/ICRERA66237.2025.11283845

Cite this

Alam, M. S., Gulzar, M. M., Qwbaiban, A. M., & Soliman, M. (2025). Soft Computing Driven Hybrid MPPT Strategy for Photovoltaic Systems in Variable Environments. In 14th International Conference on Renewable Energy Research and Applications, ICRERA 2025 (pp. 1303-1310). (14th International Conference on Renewable Energy Research and Applications, ICRERA 2025). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRERA66237.2025.11283845

Alam, Md Samiul ; Gulzar, Muhammad Majid ; Qwbaiban, Abdulaziz M. et al. / Soft Computing Driven Hybrid MPPT Strategy for Photovoltaic Systems in Variable Environments. 14th International Conference on Renewable Energy Research and Applications, ICRERA 2025. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 1303-1310 (14th International Conference on Renewable Energy Research and Applications, ICRERA 2025).

@inproceedings{9ed53a63c8d44046a0a8749f3d44a198,

title = "Soft Computing Driven Hybrid MPPT Strategy for Photovoltaic Systems in Variable Environments",

abstract = "This research presents a comparative analysis of Maximum Power Point Tracking (MPPT) algorithms for photovoltaic (PV) systems that employ soft computing approaches. Fluctuations in irradiance and temperature create nonlinearity and partial shading issues, resulting in several local maxima on the photovoltaic curve. Traditional techniques such as Perturb and Observe (P\&O) encounter difficulties in these circumstances. This issue was addressed by evaluating intelligent and hybrid methodologies, including Particle Swarm Optimization (PSO), Adaptive PSO, Teaching-Learning-Based Optimization (TLBO), Multi-Objective TLBO (MO-TLBO), and a proposed Hybrid PSO-Flying Squirrel Search Optimization (PSO-FSSO) using MATLAB/Simulink simulations. The comparative results indicate that the Hybrid PSO-FSSO attained the maximum tracking effectiveness of 99.3\% with the quickest convergence time of 0.5 seconds, surpassing other methodologies. The results highlight that hybrid soft computing techniques markedly improve MPPT efficacy in fluctuating environmental conditions, providing a resilient and effective solution for future photovoltaic systems.",

keywords = "Flying squirrel search optimization, Maximum power point tracking, Particle swarm optimization, Photovoltaic, Teaching learning-based optimization",

author = "Alam, \{Md Samiul\} and Gulzar, \{Muhammad Majid\} and Qwbaiban, \{Abdulaziz M.\} and Mohamed Soliman",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 14th International Conference on Renewable Energy Research and Applications, ICRERA 2025 ; Conference date: 27-10-2025 Through 30-10-2025",

year = "2025",

doi = "10.1109/ICRERA66237.2025.11283845",

language = "English",

series = "14th International Conference on Renewable Energy Research and Applications, ICRERA 2025",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1303--1310",

booktitle = "14th International Conference on Renewable Energy Research and Applications, ICRERA 2025",

address = "United States",

}

Alam, MS, Gulzar, MM , Qwbaiban, AM & Soliman, M 2025, Soft Computing Driven Hybrid MPPT Strategy for Photovoltaic Systems in Variable Environments. in 14th International Conference on Renewable Energy Research and Applications, ICRERA 2025. 14th International Conference on Renewable Energy Research and Applications, ICRERA 2025, Institute of Electrical and Electronics Engineers Inc., pp. 1303-1310, 14th International Conference on Renewable Energy Research and Applications, ICRERA 2025, Vienna, Austria, 27/10/25. https://doi.org/10.1109/ICRERA66237.2025.11283845

Soft Computing Driven Hybrid MPPT Strategy for Photovoltaic Systems in Variable Environments. / Alam, Md Samiul; Gulzar, Muhammad Majid ; Qwbaiban, Abdulaziz M. et al.
14th International Conference on Renewable Energy Research and Applications, ICRERA 2025. Institute of Electrical and Electronics Engineers Inc., 2025. p. 1303-1310 (14th International Conference on Renewable Energy Research and Applications, ICRERA 2025).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Soft Computing Driven Hybrid MPPT Strategy for Photovoltaic Systems in Variable Environments

AU - Alam, Md Samiul

AU - Gulzar, Muhammad Majid

AU - Qwbaiban, Abdulaziz M.

AU - Soliman, Mohamed

PY - 2025

Y1 - 2025

N2 - This research presents a comparative analysis of Maximum Power Point Tracking (MPPT) algorithms for photovoltaic (PV) systems that employ soft computing approaches. Fluctuations in irradiance and temperature create nonlinearity and partial shading issues, resulting in several local maxima on the photovoltaic curve. Traditional techniques such as Perturb and Observe (P&O) encounter difficulties in these circumstances. This issue was addressed by evaluating intelligent and hybrid methodologies, including Particle Swarm Optimization (PSO), Adaptive PSO, Teaching-Learning-Based Optimization (TLBO), Multi-Objective TLBO (MO-TLBO), and a proposed Hybrid PSO-Flying Squirrel Search Optimization (PSO-FSSO) using MATLAB/Simulink simulations. The comparative results indicate that the Hybrid PSO-FSSO attained the maximum tracking effectiveness of 99.3% with the quickest convergence time of 0.5 seconds, surpassing other methodologies. The results highlight that hybrid soft computing techniques markedly improve MPPT efficacy in fluctuating environmental conditions, providing a resilient and effective solution for future photovoltaic systems.

AB - This research presents a comparative analysis of Maximum Power Point Tracking (MPPT) algorithms for photovoltaic (PV) systems that employ soft computing approaches. Fluctuations in irradiance and temperature create nonlinearity and partial shading issues, resulting in several local maxima on the photovoltaic curve. Traditional techniques such as Perturb and Observe (P&O) encounter difficulties in these circumstances. This issue was addressed by evaluating intelligent and hybrid methodologies, including Particle Swarm Optimization (PSO), Adaptive PSO, Teaching-Learning-Based Optimization (TLBO), Multi-Objective TLBO (MO-TLBO), and a proposed Hybrid PSO-Flying Squirrel Search Optimization (PSO-FSSO) using MATLAB/Simulink simulations. The comparative results indicate that the Hybrid PSO-FSSO attained the maximum tracking effectiveness of 99.3% with the quickest convergence time of 0.5 seconds, surpassing other methodologies. The results highlight that hybrid soft computing techniques markedly improve MPPT efficacy in fluctuating environmental conditions, providing a resilient and effective solution for future photovoltaic systems.

KW - Flying squirrel search optimization

KW - Maximum power point tracking

KW - Particle swarm optimization

KW - Photovoltaic

KW - Teaching learning-based optimization

UR - https://www.scopus.com/pages/publications/105032898648

U2 - 10.1109/ICRERA66237.2025.11283845

DO - 10.1109/ICRERA66237.2025.11283845

M3 - Conference contribution

AN - SCOPUS:105032898648

T3 - 14th International Conference on Renewable Energy Research and Applications, ICRERA 2025

SP - 1303

EP - 1310

BT - 14th International Conference on Renewable Energy Research and Applications, ICRERA 2025

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th International Conference on Renewable Energy Research and Applications, ICRERA 2025

Y2 - 27 October 2025 through 30 October 2025

ER -

Alam MS, Gulzar MM , Qwbaiban AM, Soliman M. Soft Computing Driven Hybrid MPPT Strategy for Photovoltaic Systems in Variable Environments. In 14th International Conference on Renewable Energy Research and Applications, ICRERA 2025. Institute of Electrical and Electronics Engineers Inc. 2025. p. 1303-1310. (14th International Conference on Renewable Energy Research and Applications, ICRERA 2025). doi: 10.1109/ICRERA66237.2025.11283845

Soft Computing Driven Hybrid MPPT Strategy for Photovoltaic Systems in Variable Environments

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this