Load Component Analysis of a Quad-Rotor Wind Turbine

2022 ◽  
Author(s):  
Alexander N. Stillman ◽  
Etana Ferede ◽  
Farhan Gandhi
Keyword(s):  
Wind Turbine ◽  
Component Analysis ◽  
Load Component

scholarly journals Modelling tower fatigue loads of a wind turbine using data mining techniques on SCADA data

2020 ◽  
Author(s):  
Artur Movsessian ◽  
Marcel Schedat ◽  
Torsten Faber
Keyword(s):  
Feature Selection ◽  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Rapid Development ◽  
Component Analysis ◽  
Structural Health ◽  
Partial Load ◽  
Measuring Period ◽  
Feature Selection Techniques

Abstract. The rapid development of the wind industry in recent decades and the establishment of this technology as a mature and cost-competitive alternative have stressed the need for sophisticated maintenance and monitoring methods. Structural health monitoring has risen as a diagnosis strategy to detect damage or failures in wind turbine structures with the help of measuring sensors. The amount of data recorded by the structural health monitoring system can potentially be used to obtain knowledge about the condition and remaining lifetime of wind turbines. Machine learning techniques provide the opportunity to extract this information, thereby improving the reliability and cost-effectiveness of the wind industry as well. This paper demonstrates modeling damage equivalent loads of the fore-aft bending moments of a wind turbine tower with the advantage of using the neighborhood component analysis as a feature selection technique in comparison to common dimension reduction/feature selection techniques such as correlation analysis, stepwise regression or principal component analysis. For this study a one-year measuring period of data was gathered, pre-processed, and filtered by different operational modes, namely stand still, full load, and partial load. Finally, a sensitivity analysis was performed in the partial load model to determine the required length of the data collection campaign that guarantees the most precise results. The results indicate that applying neighborhood component analysis yields more conservative models regarding the number of features and equally accurate outcomes than traditional feature selection techniques.

scholarly journals Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1066 ◽  
Author(s):  
Yichuan Fu ◽  
Zhiwei Gao ◽  
Yuanhong Liu ◽  
Aihua Zhang ◽  
Xiuxia Yin
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Fault Diagnosis ◽  
Fast Fourier Transform ◽  
Wind Turbine ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Classification ◽  
Analysis Techniques ◽  
Diagnosis And Classification

In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios. A novel algorithm is addressed by integrating fast Fourier transform and uncorrelated multi-linear principal component analysis techniques in order to achieve effective three-dimensional space visualization for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems. Moreover, comparison studies are implemented by using multi-linear principal component analysis with and without fast Fourier transform, and uncorrelated multi-linear principal component analysis with and without fast Fourier transformation data pre-processing, respectively. The effectiveness of the proposed algorithm is demonstrated and validated via the wind turbine benchmark.

scholarly journals Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks

2021 ◽  
Vol 6 (2) ◽  
pp. 539-554
Author(s):  
Artur Movsessian ◽  
Marcel Schedat ◽  
Torsten Faber
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Component Analysis ◽  
Full Load ◽  
Structural Health ◽  
Partial Load ◽  
Feature Selection Techniques

Abstract. The rapid development of the wind industry in recent decades and the establishment of this technology as a mature and cost-competitive alternative have stressed the need for sophisticated maintenance and monitoring methods. Structural health monitoring has risen as a diagnosis strategy to detect damage or failures in wind turbine structures with the help of measuring sensors. The amount of data recorded by the structural health monitoring system can potentially be used to obtain knowledge about the condition and remaining lifetime of wind turbines. Machine learning techniques provide the opportunity to extract this information, thereby improving the reliability and cost-effectiveness of the wind industry as well. This paper demonstrates the modelling of damage-equivalent loads of the fore–aft bending moments of a wind turbine tower, highlighting the advantage of using the neighbourhood component analysis. This feature selection technique is compared to common dimension reduction/feature selection techniques such as correlation analysis, stepwise regression, or principal component analysis. For this study, recordings of data were gathered during approximately 11 months, preprocessed, and filtered by different operational modes, namely standstill, partial load, and full load. The results indicate that all feature selection techniques were able to maintain high accuracy when trained with artificial neural networks. The neighbourhood component analysis yields the lowest number of features required while maintaining the interpretability with an absolute mean squared error of around 0.07 % for full load. Finally, the applicability of the resulting model for predicting loads in the wind turbine is tested by reducing the amount of data used for training by 50 %. This analysis shows that the predictive model can be used for continuous monitoring of loads in the tower of the wind turbine.

Sign in / Sign up

Export Citation Format

Share Document