Proposal Title

Horizontal Axis Wind Turbines Modeling for Smart Grids Applications

Primary Faculty Mentor’s Name

Dr. Adel El Shahat

Proposal Track

Student

Session Format

Poster

Abstract

This research proposes Horizontal Axis Wind Turbines (HWT) modeling for smart grid systems. The HWT is modelled by the use of actual data. Artificial Neural Network (ANN) numerical technique is used to simulate and evaluate the designed proposed work. In order to simulate and predict the characteristics of different types of wind turbines; a lot of real data are taken from the manufacture manual of each type. It is proposed that by identifying the output power from the turbine unit, the design limits would be calculated. The design limits are summarized as follows: Starting wind speed (m/s), Average wind speed (m/s), Hub height (m), Fin length (m), Rotor diameter (m), Rotor speed (r.p.m), and Unit cost ($). MATLAB tool box is used to predict the characteristics correlations based on a non - linear and Artificial Neural Network (ANN) techniques. The data were obtained and checked from about fifty different companies working in the field of manufacturing of wind turbines. The data points are fitted by two methods. The first method is done by the curve fitting tool box, and the second is done by the Neural Network technique with feed-forward back-propagation. Based on the available data units; the hidden layer would be a nine neurons and the output layer would be six neurons. The input is one parameter (Power) and the outputs are six parameters. The configuration here is a general approximator to any function with a log-sigmoid function in the hidden layer and pure- line for output layer. Number of neurons in hidden layer is selected by inspection or by try and error until reaching the desired performance goal, accuracy, and minimum error with little time for training and with low number of neurons as possible. The ANN regression function for each unit is introduced to be used directly.

Keywords

Electrical engineering, Grid application, Renewable energy, Power

Award Consideration

1

Location

Concourse/Atrium

Presentation Year

2014

Start Date

11-15-2014 9:40 AM

End Date

11-15-2014 10:55 AM

Publication Type and Release Option

Presentation (Open Access)

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Nov 15th, 9:40 AM Nov 15th, 10:55 AM

Horizontal Axis Wind Turbines Modeling for Smart Grids Applications

Concourse/Atrium

This research proposes Horizontal Axis Wind Turbines (HWT) modeling for smart grid systems. The HWT is modelled by the use of actual data. Artificial Neural Network (ANN) numerical technique is used to simulate and evaluate the designed proposed work. In order to simulate and predict the characteristics of different types of wind turbines; a lot of real data are taken from the manufacture manual of each type. It is proposed that by identifying the output power from the turbine unit, the design limits would be calculated. The design limits are summarized as follows: Starting wind speed (m/s), Average wind speed (m/s), Hub height (m), Fin length (m), Rotor diameter (m), Rotor speed (r.p.m), and Unit cost ($). MATLAB tool box is used to predict the characteristics correlations based on a non - linear and Artificial Neural Network (ANN) techniques. The data were obtained and checked from about fifty different companies working in the field of manufacturing of wind turbines. The data points are fitted by two methods. The first method is done by the curve fitting tool box, and the second is done by the Neural Network technique with feed-forward back-propagation. Based on the available data units; the hidden layer would be a nine neurons and the output layer would be six neurons. The input is one parameter (Power) and the outputs are six parameters. The configuration here is a general approximator to any function with a log-sigmoid function in the hidden layer and pure- line for output layer. Number of neurons in hidden layer is selected by inspection or by try and error until reaching the desired performance goal, accuracy, and minimum error with little time for training and with low number of neurons as possible. The ANN regression function for each unit is introduced to be used directly.