دانلود رایگان مقاله انگلیسی بررسی شبکه های عصبی مصنوعی در سیستم های انرژی باد - الزویر 2018

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.

Conclusions

The paper presents state-of-the-art artificial neural networks (ANN) applied to wind energy systems. The complexity of these systems is rising, and the methods and algorithms to ensure their efficiency are becoming more robust due to the volume of data and diversity of variables. The main ANN based models applied in wind energy systems and their characteristics have been explained in this paper. An extensive compilation of methods, algorithms and models has been developed. These methods have been grouped into four major categories. Some conclusions have been extracted concerning each category:

- Forecasting and predictions: Besides the list of the main references, a comparison of the errors in different forecasting models has been carried out. Neural networks are proved to be more efficient for short-term wind speed prediction, and the hybrid ANN based method provides better results for short term predictions than other conventional techniques.

- Design optimization: ANN based models for design optimization have been discussed. These models not only focus on wind turbines, but also on wind farms characteristics. In this field, the most employed methods are adaptive neuro-fuzzy inference systems and Back-propagation neural networks, since high accuracy is required and the computational time is not a determinant factor.