A survey of artificial neural network in wind energy systems
By A. P. Marugán, F. Márquez, Jesús María Pinar Pérez, Diego Ruiz-Hernández. Applied Energy, 2018
401 citations recorded by the source corpus.
Abstract Wind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases. More than 85% of the 190 references employed in this paper have been published in the last 5 years. The methods are classified and analysed into four groups according to the application: forecasting and predictions; design optimization; fault detection and diagnosis; and optimal control. A statistical analysis of the current state and future trends in this field is carried out. An analysis of each application group about the strengths and weaknesses of each ANN structure is carried out. A quantitative analysis of the main references is carried out showing new statistical results of the current state and future trends of the topic. The paper describes the main challenges and technological gaps concerning the application of ANN to wind turbines, according to the literature review. An overall table is provided to summarize the most important references according to the application groups and case studies.
Subfields: Aerospace Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/343ce75308263b9595f3fff05aaffe68ffa16534
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In Aerospace Engineering
In Electrical and Electronic Engineering
Citations in the corpus
- Grid Integration Challenges of Wind Energy: A Review (2020, background): “The outcomes of forecasting using an intelligent approach proved a high degree of accuracy and consistency along with another advantage where it can deal with nonlinear time series [29] due to their adaptive nature and flexibility [30].”
- A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, methodology): “ANN have been widely employed for processing large amounts of data obtained from data acquisition systems of wind turbines [203].”