Machine Learning Controller for DFIG Based Wind Conversion System

作     者：P.Srinivasan P.Jagatheeswari 

作者机构：Department of Electronics and Communication EngineeringAmrita College of Engineering and TechnologyNagercoil629002India Department of Electrical and Electronics EngineeringPonjesly College of EngineeringNagercoil629002India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第1期

页      面：381-397页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Doubly fed induction generator machine learning convertors generators activation function 

摘      要：Renewable energy production plays a major role in satisfying electricity *** power conversion is one of the most popular renewable energy sources compared to other *** 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 algo-rithm is a crucial controller,a wind energy conversion system for generating maximum power in different wind speed *** 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 *** ML algorithm has been developed and trained in a MATLAB *** are two types of learning algorithms such as supervised and unsupervised *** this research supervised learning is used to power the neural networks and analysis is made for various hidden layers and activation *** results are assessed to demonstrate the efficiency of the proposed system.