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Machine Learning Controller for DFIG Based Wind Conversion System

P. Srinivasan1,*, P. Jagatheeswari2

1 Department of Electronics and Communication Engineering, Amrita College of Engineering and Technology, Nagercoil, 629002, India
2 Department of Electrical and Electronics Engineering, Ponjesly College of Engineering, Nagercoil, 629002, India

* Corresponding Author: P. Srinivasan. Email: email

Intelligent Automation & Soft Computing 2023, 35(1), 381-397.


Renewable energy production plays a major role in satisfying electricity demand. Wind power conversion is one of the most popular renewable energy sources compared to other sources. Wind energy conversion has two major types of generators such as the Permanent Magnet Synchronous Generator (PMSG) and the Doubly Fed Induction Generator (DFIG). The maximum power tracking algorithm is a crucial controller, a wind energy conversion system for generating maximum power in different wind speed conditions. In this article, the DFIG wind energy conversion system was developed in Matrix Laboratory (MATLAB) and designed a machine learning (ML) algorithm for the rotor and grid side converter. The ML algorithm has been developed and trained in a MATLAB environment. There are two types of learning algorithms such as supervised and unsupervised learning. In this research supervised learning is used to power the neural networks and analysis is made for various hidden layers and activation functions. Simulation results are assessed to demonstrate the efficiency of the proposed system.


Cite This Article

APA Style
Srinivasan, P., Jagatheeswari, P. (2023). Machine learning controller for DFIG based wind conversion system. Intelligent Automation & Soft Computing, 35(1), 381-397.
Vancouver Style
Srinivasan P, Jagatheeswari P. Machine learning controller for DFIG based wind conversion system. Intell Automat Soft Comput . 2023;35(1):381-397
IEEE Style
P. Srinivasan and P. Jagatheeswari, "Machine Learning Controller for DFIG Based Wind Conversion System," Intell. Automat. Soft Comput. , vol. 35, no. 1, pp. 381-397. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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