Tooth modification for optimizing gear contact of a wind-turbine gearbox

2015 ◽  
Vol 230 (7-8) ◽  
pp. 1318-1330 ◽  
Author(s):  
JeongSu Kim ◽  
NoGill Park ◽  
HyoungWoo Lee
Keyword(s):  
Wind Turbine ◽  
High Reliability ◽  
Alignment Error ◽  
Trial And Error ◽  
Wind Turbine Gearbox ◽  
Tooth Surfaces ◽  
Tooth Modification ◽  
Shaft Alignment ◽  
Gear Contact ◽  
Gear Meshes

This study investigates a method of calculating tooth modification amount of a wind-turbine gearbox. The requirements of high reliability and a service life of 20 years or longer for wind power gearboxes necessitate appropriate gear meshes, which require elaborate tooth modification. The interference between teeth caused by deformation of the gear body and the shaft alignment error, which is caused by deformation of system components such as the housing, the bearings, and the shaft, was calculated, and the result was used to determine the tooth modification parameters. Thus, a method of calculating tooth modification amount was developed that can be performed without trial and error; its reliability was confirmed by an evaluation through simulation. Contact was evenly distributed over the tooth surfaces after the tooth profile was corrected, thus improving the edge contact pattern relative to the situation before correction.

scholarly journals Analytical and Experimental Study of Gear Surface Micropitting Due to Variable Loading

2015 ◽  
Vol 750 ◽  
pp. 96-103 ◽  
Author(s):  
Hui Long ◽  
I.S. Al-Tubi ◽  
M.T.M. Martinze
Keyword(s):  
Wind Turbine ◽  
Contact Stress ◽  
Experimental Testing ◽  
Gear Tooth ◽  
Tooth Surface ◽  
Variable Loading ◽  
Wind Turbine Gearbox ◽  
Load Variation ◽  
Tooth Surfaces ◽  
Thickness Variations

This paper presents an investigation of the effect of load variation on gear tooth surface micropitting, for an application in planet gears in a wind turbine gearbox. To study the effect of load variation, two methods are employed: an experimental testing of gear micropitting under variable loading and a probabilistic analysis of gear contact stress and specific lubricant film thickness variations using the ISO Technical Report ISO/TR 15144-1:2010. The load variation of wind turbine gearbox is derived from SCADA (Supervisory Control and Data Acquisition) data recorded in operation. Both experimental and analytical results show that high levels of contact stress, load variations and repeated load cycles are determinant factors for the initiation and propagation of micropitting of gear tooth surfaces.

Fault Diagnosis of Wind Turbine Gearbox Based on Neighborhood QPSO and Improved D-S Evidence Theory

2020 ◽  
Vol 13 (2) ◽  
pp. 248-255
Author(s):  
Jiatang Cheng ◽  
Yan Xiong ◽  
Li Ai
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
High Reliability ◽  
Evidence Theory ◽  
Population Diversity ◽  
Weight Of Evidence ◽  
Wind Turbine Gearbox ◽  
Diagnosis Method ◽  
Drive Chain ◽  
The Individual

Background: Gearbox is the key equipment of wind turbine drive chain. Due to the harsh operating environment of wind turbine, gearbox failures occur frequently. Methods: To improve the accuracy of fault identification for wind turbine gearbox, an intelligent fault diagnosis method based on Neighborhood Quantum Particle Swarm Optimization (NQSPO) and improved Dempster-Shafer (D-S) evidence theory is proposed. In NQPSO algorithm, the best solution information in the neighborhood is introduced to guide the individual search behavior and enhance the population diversity. Also, the consistency coefficient is used to determine the weight of evidence, and the original evidence is amended to enhance the ability of D-S theory to fuse conflict evidence. Results: Experimental results show that the proposed method can overcome the influence of bad evidence on the diagnosis result and has high reliability. Conclusion: The research can effectively improve the accuracy of fault diagnosis of wind turbine gearbox, and provide a feasible idea for the fault diagnosis of nonlinear complex system.

Analysis of Wind Turbine Gearbox Based on RomaxWIND Flexible FE Structures

2012 ◽  
Vol 512-515 ◽  
pp. 669-674
Author(s):  
You Liang Su ◽  
Chun Xiu Wang ◽  
Wei Jiang ◽  
Li Sun
Keyword(s):  
Simulation Model ◽  
Wind Turbine ◽  
High Reliability ◽  
Ring Gear ◽  
Wind Turbine Gearbox ◽  
Transmit Power ◽  
Gear Mesh ◽  
Gear Mesh Misalignment

Wind turbine gearbox is a key component to transmit power in wind turbine driveline. It is necessary for gearbox to have high reliability and durable quality, and so, it is obviously important to analyze the reliability of key components and the root of common failure. By means of RomaxWIND software, gearbox simulation model was built which is based on the flexible FE structures. Considering the effects of flexible FE structures, such as housing and ring gear, and capturing system effects, analysis is performed to estimate the performance of components under 20 years’ LDD, and the bearing and gear report were generated. Gear mesh misalignment is one of the major gearbox failure causes. In view of it, three different models were built for the analysis of mesh misalignment, the result reveal that the effect of flexible FE structures should not be ignored.

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

2019 ◽  
Vol 12 (3) ◽  
pp. 277-283
Author(s):  
Jiatang Cheng ◽  
Yan Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Wind Turbine Gearbox ◽  
Bp Network ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

scholarly journals Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

2021 ◽  
Author(s):  
Baher Azzam ◽  
Ralf Schelenz ◽  
Björn Roscher ◽  
Abdul Baseer ◽  
Georg Jacobs
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
High Speed ◽  
Time Series Data ◽  
Development Trend ◽  
Series Data ◽  
Ann Model ◽  
Wind Turbine Gearbox ◽  
Virtual Sensor ◽  
Artificial Neural

AbstractA current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

Sign in / Sign up

Export Citation Format

Share Document