Enhanced Power Transition for Variable PitchWind Turbine using Artificial Neural Network
Alt Text: Enhanced Power Transition for Wind Turbines using Artificial Neural Network
Title: Enhanced Power Transition for Variable-Pitch Wind Turbine using ANN
Caption: Improving wind energy stability and power quality using artificial neural networks
Description: This research explores how artificial neural networks (ANN) help regulate power fluctuations in variable-pitch wind turbines, enhancing efficiency and reliability.
Keywords: ijet journal, IJET, Wind Energy, Artificial Neural Network, Power Quality, Harmonics, Renewable Energy
International Journal of Engineering and Techniques – Volume 10 Issue 5, September 2024
Mohammad Siyanda Khan1, Dr. E. Vijay Kumar2
1Research Scholar, RKDF Institute of Science Technology, SRK University, Bhopal
2Research Guide, HOD, Department of Electrical Engineering, RKDF Institute of Science Technology, SRK University, Bhopal
Abstract
The unconventional sources of power generation have gained significant attention over the past few decades due to their clean properties—producing energy with zero pollution. Researchers are continuously working on making renewable energy sources more efficient and reliable to ensure consistent power delivery to end users. Wind energy systems convert wind energy into electrical energy, but due to their fluctuating nature, the power generated changes rapidly, potentially damaging load-side devices. This study focuses on controlling harmonics to improve power quality and stability. Artificial neural networks (ANN) have been implemented in this work to regulate harmonics and enhance the reliability of power supply.
Keywords
ANN, Wind Energy, Power Quality, Fluctuations, Harmonics, Renewable Energy
How to Cite
Mohammad Siyanda Khan, Dr. E. Vijay Kumar, “Enhanced Power Transition for Variable Pitch Wind Turbine using Artificial Neural Network,” International Journal of Engineering and Techniques, Volume 10, Issue 5, September 2024. ISSN 2395-1303.
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