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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems
Al Ibrahmi, Elmehdi
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Enhancing the Performance of a Wind Turbine Based DFIG Generation System Using an Effective ANFIS Control Technique Ouhssain, Said; Chojaa, Hamid; Aljarhizi, Yahya; Al Ibrahmi, Elmehdi; Maarif, Alfian; A. Mossa, Mahmoud
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1451

Abstract

This paper gives a look on producing energy using wind turbines and imposing robust Maximum Power Point Tracking (MPPT) technique to operate around an optimal rotational speed. A mechanical speed control based on PI controller is presented in order to extract the maximum power and optimizing the conversion efficiency of wind's kinetic energy into electric energy. A doubly-fed induction generator (DFIG) is utilized because it is preferable for applications in wind energy systems referring to the capability to regulate the output voltage and improve the stability of the grid. Its operational characteristics and the regulating procedures such as Indirect Vector Control (IVC) and other sophisticated strategies for instance the ANFIS controller enhance operating flexibility and optimum performance under diverse conditions. This has attributed the split to the improved ANFIS in that it includes the artificial neural networks besides the fuzzy logic since they improve on learning as well as parameter fine tuning. Some of them are working with a comparatively fewer number of data sets; and therefore, it can be useful in classification, modeling and control. This configuration enables to regulate the generator's magnetic flux, torque, and reactive power, adjusting to changes inside wind velocity and disruptions within the grid. The performance of the proposed MPPT-IIVC method is examined by way of simulations in Matlab/Simulink. The simulations concerned a dynamic model incorporating the wind turbine, the DFIG, and the electric grid. The results show that the proposed technique can incredibly enhance the wind energy, maintain precise regulation over speed, and effectively adjust and regulate grid voltage and frequency. The performance of the proposed ANFIS controller is compared with a PI controller and discovered that ANFIS enhances the robustness, precision, dynamic response, total harmonic distortion THD (%) of the injected current into the grid, the reference tracking ability and Overshoot (%).
Performance Optimization of a DFIG-based Variable Speed Wind Turbines by IVC-ANFIS Controller Ouhssain, Said; Chojaa, Hamid; Aljarhizi, Yahya; Al Ibrahmi, Elmehdi; Hadoune, Aziz; Maarif, Alfian; Suwarno, Iswanto; Mossa, Mahmoud A.
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22118

Abstract

An improved indirect vector control (IVC) method for a wind energy conversion system (WECS) is presented in this research. Field-oriented control or indirect vector control as it is sometimes called is a very important element of contemporary WECS that employs DFIGs. This control strategy is pivotal for achieving high performance and efficiency of DFIG-based wind turbines because it offers direct control on the torque and power ratings of the generator. A doubly fed induction generator (DFIG) is used by the WECS to inject power to the grid. An adaptive network-based fuzzy inference system (ANFIS), which is proposed to replace traditional methods like linear PI controllers, is the basis for this IVC. In this paper we chose ANFIS controller over traditional linear Proportional-Integral (PI) controllers due to its ability to adapt and learn from the system, leading to improved performance. The rotor voltage is controlled by the proposed IVC in order to regulate the exchanged active and reactive power between the stator and the grid. In order to verify the proposed control in terms of performance and robustness, a comparative analysis between the proposed ANFIS and linear PI controllers for the WECS-DFIG system is performed by a simulation study in a MATLAB/Simulink environment. This analysis covers both the transient and steady states of operation. As a result, the proposed ANFIS controller shows improved efficiency and robustness compared to the linear PI controllers. This superiority stems from its ability to integrate the flexibility and effectiveness inherent in diverse artificial intelligence controllers, specifically the synergistic use of Neural Network (NN) and Fuzzy Logic (FL) algorithms. The ANFIS controller's adaptability to diverse operating conditions and its capability to learn and optimize its performance play pivotal roles in enhancing its control capabilities within the WECS-DFIG system.
Performance Optimization of a DFIG-based Variable Speed Wind Turbines by IVC-ANFIS Controller Ouhssain, Said; Chojaa, Hamid; Aljarhizi, Yahya; Al Ibrahmi, Elmehdi; Hadoune, Aziz; Maarif, Alfian; Suwarno, Iswanto; Mossa, Mahmoud A.
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22118

Abstract

An improved indirect vector control (IVC) method for a wind energy conversion system (WECS) is presented in this research. Field-oriented control or indirect vector control as it is sometimes called is a very important element of contemporary WECS that employs DFIGs. This control strategy is pivotal for achieving high performance and efficiency of DFIG-based wind turbines because it offers direct control on the torque and power ratings of the generator. A doubly fed induction generator (DFIG) is used by the WECS to inject power to the grid. An adaptive network-based fuzzy inference system (ANFIS), which is proposed to replace traditional methods like linear PI controllers, is the basis for this IVC. In this paper we chose ANFIS controller over traditional linear Proportional-Integral (PI) controllers due to its ability to adapt and learn from the system, leading to improved performance. The rotor voltage is controlled by the proposed IVC in order to regulate the exchanged active and reactive power between the stator and the grid. In order to verify the proposed control in terms of performance and robustness, a comparative analysis between the proposed ANFIS and linear PI controllers for the WECS-DFIG system is performed by a simulation study in a MATLAB/Simulink environment. This analysis covers both the transient and steady states of operation. As a result, the proposed ANFIS controller shows improved efficiency and robustness compared to the linear PI controllers. This superiority stems from its ability to integrate the flexibility and effectiveness inherent in diverse artificial intelligence controllers, specifically the synergistic use of Neural Network (NN) and Fuzzy Logic (FL) algorithms. The ANFIS controller's adaptability to diverse operating conditions and its capability to learn and optimize its performance play pivotal roles in enhancing its control capabilities within the WECS-DFIG system.
Co-Authors A. Mossa, Mahmoud Alfian Ma’arif Aljarhizi, Yahya Chojaa, Hamid Hadoune, Aziz Mossa, Mahmoud A. Ouhssain, Said Suwarno, Iswanto