scholarly journals Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis

2021 ◽  
Author(s):  
Ping Wu ◽  
Yichao Liu ◽  
Riccardo M.G. Ferrari ◽  
Jan‐Willem van Wingerden
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Wind Turbine ◽  
Canonical Correlation ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Fisher Discriminant Analysis ◽  
Floating Offshore Wind Turbine ◽  
Fisher Discriminant

Floating offshore wind turbine fault diagnosis using stacked denoising autoencoder with temporal information

2021 ◽  
pp. 014233122110579
Author(s):  
Xujie Zhang ◽  
Ping Wu ◽  
Jiajun He ◽  
Yichao Liu ◽  
Lin Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Energy ◽  
Wind Turbine ◽  
Temporal Information ◽  
Offshore Wind ◽  
Superior Performance ◽  
Series Data ◽  
Offshore Wind Turbine ◽  
Denoising Autoencoder ◽  
Floating Offshore Wind Turbine

Currently, the offshore wind turbine has become a hot research area in the wind energy industry. Among different offshore wind turbines, floating offshore wind turbine (FOWT) can harvest abundant wind energy in deepwater areas. However, the harsh working environment will dramatically increase the maintenance cost and downtime of FOWTs. Wind turbine fault diagnosis is being regarded as an indispensable system for maintenance issues. Owing to the complexity of FOWT, it imposes an enormous challenge for effective fault diagnosis. This paper develops a novel FOWT fault diagnosis method based on a stacked denoising autoencoder (SDAE). First, a sliding window technique is adopted for time-series data to preserve temporal information. Then, SDAE is employed to extract the features from high-dimensional data. Based on the extracted features from SDAE, a classifier using multilayer perceptrons (MLP) is developed to determine the health status of the FOWT. To verify the performance of the proposed method, a FOWT simulation benchmark based on the National Renewable Energy Laboratory (NREL) FAST simulator is employed. Results show the superior performance of the proposed method by comparison with other relevant methods.

scholarly journals Fault diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: A mixed model and signal-based approach

2021 ◽  
Vol 164 ◽  
pp. 391-406
Author(s):  
Yichao Liu ◽  
Riccardo Ferrari ◽  
Ping Wu ◽  
Xiaoli Jiang ◽  
Sunwei Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Mixed Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Floating Offshore Wind Turbine

scholarly journals Resonance Avoidance Control Algorithm for Semi-Submersible Floating Offshore Wind Turbine

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4138
Author(s):  
Kwansu Kim ◽  
Hyunjong Kim ◽  
Hyungyu Kim ◽  
Jaehoon Son ◽  
Jungtae Kim ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Control Algorithm ◽  
Torque Control ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Resonance Problem ◽  
Exclusion Zone ◽  
Mean Power ◽  
Floating Offshore Wind Turbine ◽  
Avoidance Control

In this study, a resonance avoidance control algorithm was designed to address the tower resonance problem of a semi-submersible floating offshore wind turbine (FOWT) and the dynamic performance of the wind turbine, floater platform, and mooring lines at two exclusion zone ranges were evaluated. The simulations were performed using Bladed, a commercial software for wind turbine analysis. The length of simulation for the analysis of the dynamic response of the six degrees of freedom (DoF) motion of the floater platform under a specific load case was 3600 s. The simulation results are presented in terms of the time domain, frequency domain, and using statistical analysis. As a result of applying the resonance avoidance control algorithm, when the exclusion zone range was ±0.5 rpm from the resonance rpm, the overall performance of the wind turbine was negatively affected, and when the range was sufficiently wide at ±1 rpm, the mean power was reduced by 0.04%, and the damage equivalent load of the tower base side–side bending moment was reduced by 14.02%. The tower resonance problem of the FOWT caused by practical limitations in design and cost issues can be resolved by changing the torque control algorithm.

Optimal multi-kernel local fisher discriminant analysis for feature dimensionality reduction and fault diagnosis

2021 ◽  
pp. 1748006X2110093
Author(s):  
Qing Zhang ◽  
Heng Li ◽  
Xiaolong Zhang ◽  
Haifeng Wang
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Feature Space ◽  
High Dimensional ◽  
Fisher Discriminant Analysis ◽  
Vibration Signals ◽  
Fisher Discriminant ◽  
Local Fisher Discriminant Analysis ◽  
Diagnosis Accuracy ◽  
Diagnosis Model

To achieve a more desirable fault diagnosis accuracy by applying multi-domain features of vibration signals, it is significative and challenging to refine the most representative and intrinsic feature components from the original high dimensional feature space. A novel dimensionality reduction method for fault diagnosis is proposed based on local Fisher discriminant analysis (LFDA) which takes both label information and local geometric structure of the high dimensional features into consideration. Multi-kernel trick is introduced into the LFDA to improve its performance in dealing with the nonlinearity of mapping high dimensional feature space into a lower one. To obtain an optimal diagnosis accuracy by the reduced features of low dimensionality, binary particle swarm optimization (BPSO) algorithm is utilized to search for the most appropriate parameters of kernels and K-nearest neighbor (kNN) recognition model. Samples with labels are used to train the optimal multi-kernel LFDA and kNN (OMKLFDA-kNN) fault diagnosis model to obtain the optimal transformation matrix. Consequently, the trained fault diagnosis model implements the recognition of machinery health condition with the most representative feature space of vibration signals. A bearing fault diagnosis experiment is conducted to verify the effectiveness of proposed diagnostic approach. Performance comparison with some other methods are investigated, and the improvement for fault diagnosis of the proposed method are confirmed in different aspects.

Coupled Dynamic Analysis for Multi-Unit Floating Offshore Wind Turbine in Maximum Operational and Survival Conditions

2015 ◽  
Author(s):  
H. K. Jang ◽  
H. C. Kim ◽  
M. H. Kim ◽  
K. H. Kim
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Time Domain ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Random Waves ◽  
Mooring Lines ◽  
Floating Offshore Wind Turbine ◽  
Coupled Dynamic Analysis ◽  
Floating Offshore Wind Turbines

Numerical tools for a single floating offshore wind turbine (FOWT) have been developed by a number of researchers, while the investigation of multi-unit floating offshore wind turbines (MUFOWT) has rarely been performed. Recently, a numerical simulator was developed by TAMU to analyze the coupled dynamics of MUFOWT including multi-rotor-floater-mooring coupled effects. In the present study, the behavior of MUFOWT in time domain is described through the comparison of two load cases in maximum operational and survival conditions. A semi-submersible floater with four 2MW wind turbines, moored by eight mooring lines is selected as an example. The combination of irregular random waves, steady currents and dynamic turbulent winds are applied as environmental loads. As a result, the global motion and kinetic responses of the system are assessed in time domain. Kane’s dynamic theory is employed to formulate the global coupled dynamic equation of the whole system. The coupling terms are carefully considered to address the interactions among multiple turbines. This newly developed tool will be helpful in the future to evaluate the performance of MUFOWT under diverse environmental scenarios. In the present study, the aerodynamic interactions among multiple turbines including wake/array effect are not considered due to the complexity and uncertainty.

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