scholarly journals Statistical Data Mining through Credal Decision Tree Classifiers for Fault Prediction on Wind Turbine Blades Using Vibration Signals

2020 ◽  
Vol 988 ◽  
pp. 012078
Author(s):  
Joshuva Arockia Dhanraj ◽  
P Jayaraman ◽  
Kuppan Chetty Ramanathan ◽  
J Pravin Kumar ◽  
T Jayachandran
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Wind Turbine ◽  
Statistical Data ◽  
Turbine Blades ◽  
Fault Prediction ◽  
Wind Turbine Blades ◽  
Vibration Signals ◽  
Statistical Data Mining

scholarly journals A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach

2018 ◽  
Vol 7 (4.10) ◽  
pp. 190 ◽  
Author(s):  
A. Joshuva ◽  
V. Sugumaran
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Phase 1 ◽  
Turbine Blades ◽  
Computational Time ◽  
Learning Approach ◽  
Statistical Features ◽  
Wind Turbine Blades ◽  
Vibration Signals ◽  
Machine Learning Approach

This study is to identify whether the wind turbine blades are in good or faulty conditions. If faulty, then the objective to find which fault condition are the blades subjected to. The problem identification is carried out by machine learning approach using vibration signals through statistical features. In this study, a three bladed wind turbine was chosen and faults like blade cracks, hub-blade loose connection, blade bend, pitch angle twist and blade erosion were considered. Here, the study is carried out in three phases namely, feature extraction, feature selection and feature classification. In phase 1, the required statistical features are extracted from the vibration signals which obtained from the wind turbine through accelerometer. In phase 2, the most dominating or the relevant feature is selected from the extracted features using J48 decision tree algorithm. In phase 3, the selected features are classified using machine learning classifiers namely, K-star (KS), locally weighted learning (LWL), nearest neighbour (NN), k-nearest neighbours (kNN), instance based K-nearest using log and Gaussian weight kernels (IBKLG) and lazy Bayesian rules classifier (LBRC). The results were compared with respect to the classification accuracy and the computational time of the classifier.  

scholarly journals Research on Hyper pipes and Voting Feature Intervals Classifier for Condition Monitoring of Wind Turbine Blades using Vibration Signals

2019 ◽  
Vol 8 (2S11) ◽  
pp. 310-319
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Fault Classification ◽  
Vibration Source ◽  
Wind Turbine Blades ◽  
Energy Field ◽  
Learning Techniques

Renewable energy is viewed as a vital energy field due to the present energy devastations. Among the vital substitutions being considered, wind energy is a strong challenger as a result of its reliability. “To yield wind energy more effectively, the structure of wind turbines has designed bigger, making protection and restoration works difficult. Because of different natural conditions, wind turbine blades are exposed to vibration and it prompts failure. If the failure is not analyzed initially, then it will haste dreadful destruction of the turbine structure. To increase safety perceptions, to decrease down time and to cut down the repeat of unpredictable breakdowns, the wind turbine blades must be examined from time to time to guarantee that they are in great condition. In this paper, a three bladed wind turbine was preferred and using vibration source through statistical features, the state of a wind turbine blade is inspected. The fault classification is carried out using machine learning techniques like hyperpipes (HP) and voting feature intervals (VFI) algorithm. The performance of these algorithms is compared and better algorithm is suggested for fault prediction on wind turbine blades.”

Experiments of Wind Turbine Blades with Rocket Triggered Lightning

2009 ◽  
Vol 129 (5) ◽  
pp. 689-695
Author(s):  
Masayuki Minowa ◽  
Shinichi Sumi ◽  
Masayasu Minami ◽  
Kenji Horii
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Wind Turbine Blades ◽  
Triggered Lightning

Lightning Striking Characteristics to Wind Turbine Blades -Experimental Study of Effects of the Receptor Configuration and Other Parameters-

2009 ◽  
Vol 129 (2) ◽  
pp. 331-339
Author(s):  
Takatoshi Shindo ◽  
Akira Asakawa ◽  
Megumu Miki
Keyword(s):  
Experimental Study ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Wind Turbine Blades

The Surface Damage Identifications of Wind Turbine Blades Based on ResNet50 Algorithm

2020 ◽  
Author(s):  
Peng Yang ◽  
Chaoyi Dong ◽  
Xiaoyi Zhao ◽  
Xiaoyan Chen
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Surface Damage ◽  
Wind Turbine Blades

Folding of flexible hinges for aircraft wingtips and wind turbine blades

2021 ◽  
Author(s):  
Aileen G. Bowen Perez ◽  
Giovanni Zucco ◽  
Paul Weaver
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Wind Turbine Blades

Evaluation of the influence of wind speed and angle of attack on the aerodynamic effect of wind turbine blades

2017 ◽  
Author(s):  
Salete Alves ◽  
Luiz Guilherme Vieira Meira de Souza ◽  
Edália Azevedo de Faria ◽  
Maria Thereza dos Santos Silva ◽  
Ranaildo Silva
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Angle Of Attack ◽  
Wind Turbine Blades ◽  
Aerodynamic Effect
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