Wind power generation fault diagnosis based on deep learning model in internet of things (IoT) with clusters

2018 ◽  
Vol 22 (S6) ◽  
pp. 14013-14025 ◽  
Author(s):  
Fei Chen ◽  
Zhongguang Fu ◽  
Zhiling Yang
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Internet Of Things ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Deep Learning Model

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model

Application of Multiple Level Rules Set in Fault Diagnosis of the Wind Power Generation System

2012 ◽  
Vol 512-515 ◽  
pp. 679-685
Author(s):  
Gui Mei Gu
Keyword(s):  
Power Generation ◽  
Fault Diagnosis ◽  
Wind Power ◽  
Comprehensive Evaluation ◽  
Wind Power Generation ◽  
Decision Table ◽  
Multiple Level ◽  
Generation System ◽  
Power Generation System ◽  
Rules Set

For the incompletion problem of sensors’ collected data in fault diagnosis of the wind power system, this article puts forward a kind of multiple level rules set based on rough set. First, let the sensors’ collected data go through Fourier transform and extract its feature attributes as well as discrete them. Establish the decision table of fault diagnosis according to attribute values. Then set out from the decision table to establish a multiple level set of nodes with diverse reduced levels and deduce the rules of each node, which has a corresponding belief level. When in reasoning and decision-making of the new data using the multiple level rules set, match the information of the new data with the rule of its corresponding node. Finally, achieve the fault diagnosis of wind power generation system by choosing comprehensive evaluation algorithm. The result of the diagnosis example shows the reliability and accuracy of this method in the diagnosis of fault types for wind power generation system.

Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

2020 ◽  
Author(s):  
Do-Eun Choe ◽  
Gary Talor ◽  
Changkyu Kim
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Weather Data ◽  
Local Wind ◽  
Offshore Wind Power ◽  
The Future

Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

scholarly journals A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signal

2017 ◽  
Author(s):  
Wei Zhang ◽  
Gaoliang Peng ◽  
Chuanhao Li ◽  
Yuanhang Chen ◽  
Zhujun Zhang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Learning Model ◽  
Frequency Noise ◽  
Nonlinear Mapping ◽  
Intelligent Fault Diagnosis ◽  
Deep Learning Model ◽  
Adaptation Ability

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

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