Effective optimal control of a wind turbine system with hybrid energy storage and hybrid MPPT approach

This research paper discusses a wind turbine system and its integration in remote locations using a hybrid power optimization approach and a hybrid storage system. Wind turbine systems' optimization controllers operate MPPT strategies efficiently, optimizing the system's overall performance. The proposed approach is HTb(P&O/FLC), which combines the P&O and FLC methods. This innovative hybrid MPPT strategy takes advantage of the two methods. It maximizes the wind power thus minimizing stress on the storage system. For storage, batteries are important in isolated renewable energy systems due the interminent renewable sources. Unfortunately, they have some drawbacks as lower energy density, and limited charge-discharge. Besides, supercapacitors (SCs) have advantages as quick charging and discharging and long cycle life and some drawbacks as lower energy density and specific voltage requirements. This paper proposes a hybrid storage system combining batteries and SCs. To manage the two storage technologies, an effective algorithm isproposed focusing on managing their storage systems. The findings indicate that the proposed strategy effectively maintains the state of charge (SOC) of both the batteries and SCs in the intended limits. Simulation under MATLAB and Real-time with RT-LAB were carried out to validate the proposed control approach for the wind turbine system.

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Publication Details

Journal
Scientific Reports
Published
2024-12-03
DOI
https://doi.org/10.1038/s41598-024-78847-9
Citations
15
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
4.92
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article

Effective optimal control of a wind turbine system with hybrid energy storage and hybrid MPPT approach

Mohit Bajaj, Pierre‐Olivier Logerais, Zahra Mokrani, Milkias Berhanu Tuka et al.
15 citations
Scientific Reports
Microgrid Control and Optimization
4.92
article

Effective optimal control of a wind turbine system with hybrid energy storage and hybrid MPPT approach

Mohit Bajaj, Pierre‐Olivier Logerais, Zahra Mokrani, Milkias Berhanu Tuka, Adel Oubelaid, Djamila Rekioua, Sherif S. M. Ghoneim, Muhannad Alshareef, Nabil Mezzai, Khoudir Kakouche
article en
15 citations

Abstract

This research paper discusses a wind turbine system and its integration in remote locations using a hybrid power optimization approach and a hybrid storage system. Wind turbine systems' optimization controllers operate MPPT strategies efficiently, optimizing the system's overall performance. The proposed approach is HTb(P&O/FLC), which combines the P&O and FLC methods. This innovative hybrid MPPT strategy takes advantage of the two methods. It maximizes the wind power thus minimizing stress on the storage system. For storage, batteries are important in isolated renewable energy systems due the interminent renewable sources. Unfortunately, they have some drawbacks as lower energy density, and limited charge-discharge. Besides, supercapacitors (SCs) have advantages as quick charging and discharging and long cycle life and some drawbacks as lower energy density and specific voltage requirements. This paper proposes a hybrid storage system combining batteries and SCs. To manage the two storage technologies, an effective algorithm isproposed focusing on managing their storage systems. The findings indicate that the proposed strategy effectively maintains the state of charge (SOC) of both the batteries and SCs in the intended limits. Simulation under MATLAB and Real-time with RT-LAB were carried out to validate the proposed control approach for the wind turbine system.

Scientific ReportsVol. 14(1)
Taif University (SA), University of Béjaïa (DZ), Université Paris-Est Créteil (FR), Umm al-Qura University (SA), Paris-Est Sup (FR), Centre d'Études et de Recherche en Thermique, Environnement et Systèmes (FR), Addis Ababa Science and Technology University (ET), Graphic Era University (IN)
Affordable and clean energy
Openalex Percentile: Top 4%
Microgrid Control and Optimization
4.92
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