<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Solar energy harnessed through photovoltaic technology plays a crucial role in generating electrical energy. Maximising the power output of solar modules requires optimal solar radiation. However, challenges arise due to obstacles such as stationary objects, buildings, and sand-laden winds, resulting in multiple points of maximum power on the P–V curve. This problem requires the use of maximum power point tracking algorithms, especially in unstable climatic conditions and partial shading scenarios. In this study, we propose a comparative analysis of three MPPT methods: particle swarm optimisation (PSO), grey wolf optimisation (GWO) and Horse Herd Optimization Algorithm (HOA) under dynamic partial shading conditions. We evaluate the accuracy of these methods using Matlab / Simulink simulations. The results show that all three methods solve partial shading problems effectively and with high precision. Furthermore, the Horse Herd Optimization approach has superior tracking accuracy and faster convergence compared with the other proposed methods.</p>
</abstract>

Solar energy harnessed through photovoltaic technology plays a crucial role in generating electrical energy. Maximising the power output of solar modules requires optimal solar radiation. However, challenges arise due to obstacles such as stationary objects, buildings, and sand-laden winds, resulting in multiple points of maximum power on the P–V curve. This problem requires the use of maximum power point tracking algorithms, especially in unstable climatic conditions and partial shading scenarios. In this study, we propose a comparative analysis of three MPPT methods: particle swarm optimisation (PSO), grey wolf optimisation (GWO) and Horse Herd Optimization Algorithm (HOA) under dynamic partial shading conditions. We evaluate the accuracy of these methods using Matlab / Simulink simulations. The results show that all three methods solve partial shading problems effectively and with high precision. Furthermore, the Horse Herd Optimization approach has superior tracking accuracy and faster convergence compared with the other proposed methods.

A Comparative Study of PSO, GWO, and HOA Algorithms for Maximum Power Point Tracking in Partially Shaded Photovoltaic Systems

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Solar energy harnessed through photovoltaic technology plays a crucial role in generating electrical energy. Maximising the power output of solar modules...

A Comparative Study of PSO, GWO, and HOA Algorithms for Maximum Power Point Tracking in Partially Shaded Photovoltaic Systems

Online veröffentlicht: 15. Feb. 2024

Seitenbereich: 86 - 105

Eingereicht: 24. Juli 2023

Akzeptiert: 16. Jan. 2024

DOI: https://doi.org/10.2478/pead-2024-0006

Schlüsselwörter
grey wolf optimization (GWO), horse herd optimization algorithm (HOA), maximum power point tracking (MPPT), partial shading, particle swarm optimization (PSO), photovoltaic generation systems

© 2024 Fares Berttahar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Comparative Study of PSO, GWO, and HOA Algorithms for Maximum Power Point Tracking in Partially Shaded Photovoltaic Systems

Online veröffentlicht: 15. Feb. 2024

Seitenbereich: 86 - 105

Eingereicht: 24. Juli 2023

Akzeptiert: 16. Jan. 2024

DOI: https://doi.org/10.2478/pead-2024-0006

Schlüsselwörtergrey wolf optimization (GWO), horse herd optimization algorithm (HOA), maximum power point tracking (MPPT), partial shading, particle swarm optimization (PSO), photovoltaic generation systems

© 2024 Fares Berttahar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
grey wolf optimization (GWO), horse herd optimization algorithm (HOA), maximum power point tracking (MPPT), partial shading, particle swarm optimization (PSO), photovoltaic generation systems