Modelling of Smart Airborne Wind turbine System with Neural Network based on MPPT

2018 ◽  
Author(s):  
Sivaraman Karthikeyan ◽  
A. K. Veeraraghavan ◽  
Uvais Karni ◽  
Shreyas Ramachandran Srinivasan
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Wind Turbine System

scholarly journals A RBF Neural Network based MPPT Method for Variable Speed Wind Turbine System

2015 ◽  
Vol 48 (21) ◽  
pp. 244-250 ◽  
Author(s):  
Shanzhi LI ◽  
Haoping WANG ◽  
Yang TIAN ◽  
Abdel AITOUCHE
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Rbf Neural Network ◽  
Variable Speed ◽  
Wind Turbine System ◽  
Variable Speed Wind Turbine

Multi-fault diagnosis method for wind power generation system based on recurrent neural network

2019 ◽  
Vol 233 (5) ◽  
pp. 604-615 ◽  
Author(s):  
Junnian Wang ◽  
Yao Dou ◽  
Zhenheng Wang ◽  
Dan Jiang
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Power ◽  
Recurrent Neural Network ◽  
Generation System ◽  
Wind Turbine System ◽  
Diagnosis Method ◽  
Power Generation System

With the continuous expansion of the scale of wind turbine system, wind power production, operation and equipment control of wind turbine have become more and more significant. To improve the reliability of wind turbine systems fault diagnosis, combining with data-driven technology, this paper proposes a multi-fault diagnosis method for wind power system based on recurrent neural network. According to the actual wind speed data, the normal operation and fault data of the wind turbine system are obtained by system modeling, and the classification and prediction model based on the recurrent neural network algorithm is established, which takes 30 characteristic parameters such as wind speed, rotor speed, generator speed and power generation as input, and 10 different types faults labels of the wind turbine as output. Specific rules formed inside the sample data of the wind turbine system are learned intelligently by the model which is continuously trained, optimized and tested to verify the feasibility of the algorithm. The results of evaluation standards such as accuracy rate, missed detection rate and F1-measure that compared with other related algorithms such as deep belief network show that the proposed algorithm can solve the problem of multi-classification fault diagnosis for wind power generation system efficiently.

scholarly journals Pitch Angle Controller of Wind Turbine System Using Neural Network

2014 ◽  
Vol 15 (2) ◽  
pp. 1059-1065 ◽  
Author(s):  
Min-Ho Hong ◽  
Seung-Youn Ko ◽  
Ho-Chan Kim ◽  
Jong-Chul Hur ◽  
Min-Jae Kang
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Pitch Angle ◽  
Wind Turbine System

scholarly journals Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2561 ◽  
Author(s):  
Wenxin Yu ◽  
Shoudao Huang ◽  
Weihong Xiao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Wind Turbine ◽  
Back Propagation ◽  
Original Data ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Time Frequency ◽  
Wind Turbine System

To investigate problems involving wind turbines that easily occur but are hard to diagnose, this paper presents a wind turbine (WT) fault diagnosis algorithm based on a spectrogram and a convolutional neural network. First, the original data are sampled into a phonetic form. Then, the data are transformed into a spectrogram in the time-frequency domain. Finally, the data are sent into a convolutional neural network (CNN) model with batch regularization for training and testing. Experimental results show that the method is suitable for training a large number of samples and has good scalability. Compared with Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and other fault diagnosis methods, the average diagnostic correctness rate is higher; so, the method can provide more accurate reference information for wind turbine fault diagnosis.

scholarly journals Failure Prediction of Wind Turbine using Neural Network and Operation Signal

2021 ◽  
Vol 10 (4) ◽  
pp. 261-268
Author(s):  
Dr. Dong Hwa Kim ◽  
◽  
Young Sung Kim ◽  
Keyword(s):  
Neural Network ◽  
Wind Energy ◽  
Wind Turbine ◽  
High Reliability ◽  
Electrical Power ◽  
Prediction Method ◽  
Electrical Energy ◽  
High Mountain ◽  
Wind Turbine System ◽  
Electrical Part

This paper deals with a novel prediction method for wind turbine by using neural network and operating data. As wind turbine transfer wind energy to electrical power energy, its structure has rotation part that capture wind energy, mechanical part, and electrical part that convert from mechanical rotation to electrical energy. Its working environmental situation is so bad like high mountain, sand desert, and offshore to capture good wind situation. Therefore, its control and monitoring should have high reliability for long terms during operation because its maintenance and repairing is very difficult and economically high cost. As wind turbine system is composed of three parts, there are many components that should be monitored to failure. This paper suggests neural network and operation data-based prediction method that can predict components' failure through data comparison and neural network's training function with easy expression of 'Yes' or 'No' for operator.

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