Scipediacontent: Scipediacontent moved page Review 441392782982 to Boujaghama et al 2025a

2025-12-29T14:40:39Z

Scipediacontent moved page Review 441392782982 to Boujaghama et al 2025a

Scipediacontent: Scipediacontent moved page Draft content 664031967 to Review 441392782982

2025-12-29T14:38:38Z

Scipediacontent moved page Draft content 664031967 to Review 441392782982

Scipediacontent: Created page with " == Abstract ==
Photovoltaic (PV) energy is among the renewable and clean energies which are been widely used in recent years worldwide. To ensure optimal energy extractio..."

2025-12-29T14:38:36Z

Created page with " == Abstract == <p>Photovoltaic (PV) energy is among the renewable and clean energies which are been widely used in recent years worldwide. To ensure optimal energy extractio..."

New page

== Abstract ==

<p>Photovoltaic (PV) energy is among the renewable and clean energies which are been widely used in recent years worldwide. To ensure optimal energy extraction under dynamic irradiance and temperature conditions, improving the efficiency of PV systems requires advanced Maximum Power Point Tracking (MPPT) techniques. To identify the most suitable technique that can be implemented practically, we conduct a comparative study in this paper between MPPT algorithms, namely Incremental Conductance (INC), Perturb and Observe (P&O), Fuzzy Logic (FL), and Artificial Neural Network (ANN). Using MATLAB/Simulink, our study was conducted under the same operating conditions, with a focus on efficiency, statistical analysis of robustness, and computational complexity. Our results show that the FL controller delivered the best overall performance, whose effectiveness depends on the accuracy of the rule base and scaling factors. It is characterized by a mean efficiency of 97.17%, a rapid response of 0.0585 s, minimal steady-state oscillations, and strong adaptability to environmental variations. The ANN-based approach achieves a mean efficiency of 94.91% and exhibits high performance at medium to high irradiance levels. However, its efficiency decreases significantly at low irradiance, resulting in reduced stability and increased deviation. INC and P&O achieve mean efficiencies of 95.20% and 95.15%, respectively. Moreover, due to their low computational cost, both techniques can be easily implemented. However, under rapidly changing conditions, they exhibit slower dynamics and more pronounced oscillations around the maximum power point, resulting in less stability.OPEN ACCESS Received: 01/08/2025 Accepted: 14/10/2025</p>

== Document ==
<pdf>Media:Draft_content_664031967-2299-document.pdf</pdf>

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Boujaghama et al 2025a - Revision history

Scipediacontent: Scipediacontent moved page Review 441392782982 to Boujaghama et al 2025a

Scipediacontent: Scipediacontent moved page Draft content 664031967 to Review 441392782982

Scipediacontent: Created page with " == Abstract == Photovoltaic (PV) energy is among the renewable and clean energies which are been widely used in recent years worldwide. To ensure optimal energy extractio..."

Scipediacontent: Created page with " == Abstract ==
Photovoltaic (PV) energy is among the renewable and clean energies which are been widely used in recent years worldwide. To ensure optimal energy extractio..."