Machine learning applications in wind turbine generating systems. (2021)

• Record Type: Journal Article
• Authors: Lydia, M.; Edwin Prem Kumar, G.
• Is Part Of: Materials today. Volume 45:Part 7(2021)
• Page Start: 6411
• Page End: 6414
• Publication Date: 2021
• Subjects: Machine learning -- Modeling -- Monitoring -- Wind turbine generating system -- Prediction; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.11.268
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Wind energy is emerging as a leading source of power in our Country. Wind energy conversion system are used to harness the power in the wind and convert it to electrical power. Accelerating energy demand and depleting fossil fuel reserves have resulted in the world-wide transition from conventional sources to non-conventional sources of energy. They also play a major role in mitigation of climate change and promotion of sustainable growth by reducing carbon-di-oxide emissions. Though the stochastic nature of wind has posed a stiff challenge to harnessing of wind power, several research works have been undertaken to overcome these issues. Machine learning based algorithms have been proven to solve critical problems in prediction, detection and maintenance. Incorporation of machine learning in several aspects of wind turbine generating systems has revolutionized the performance of wind farms. This paper presents a brief review of applications of machine learning in wind farm monitoring, modeling and prediction. The research challenges yet to be explored have also been presented.