Universal Maximum Power Extraction Controller for Wind Energy Conversion Systems Using Deep Belief Neural Network
Author:
Affiliation:
1. Intelligent Robotic and Energy Systems Research Group (IRES), the Department of Electronics, Carleton University, Ottawa, ON, Canada
2. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Renewable Energy, Sustainability and the Environment
Link
http://xplorestaging.ieee.org/ielx7/5165391/9994274/09954915.pdf?arnumber=9954915
Reference44 articles.
1. Energy Efficiency Modeling for Configuration-Dependent Machining via Machine Learning: A Comparative Study
2. Deep learning
3. Wind Energy Conversion Systems
4. Robust Control and Kalman MPPT for Grid-Assimilated Wind Energy Conversion System
5. Frame-Angle Controlled Wavelet Modulated Inverter and Self-Recurrent Wavelet Neural Network-Based Maximum Power Point Tracking for Wind Energy Conversion System
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1. A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system;Global Energy Interconnection;2023-10
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