Dynamic Characteristics of Planetary Gears Train of Semi-Direct Drive Wind Turbine

2012 ◽  
Vol 271-272 ◽  
pp. 868-871 ◽  
Author(s):  
Zheng Ming Xiao ◽  
Zhi Hong Yin ◽  
Yu Guo
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Dynamic Characteristics ◽  
Design Parameters ◽  
Support Vector ◽  
Parameter Method ◽  
Direct Drive ◽  
Heavy Load ◽  
Planetary Gears ◽  
Torque Load

The speed increasing gearbox is the key part of the wind turbine, and it requires higher reliability and service life than general mechanical system. The single-stage planetary gears train(PGT) are commonly used in the semi-direct drive wind turbines, which sustain low speed, heavy load, varying speed and varying load. The dynamic characteristics are very complex, due to the frequent disturbance under the random wind and have a greater impact on reliability and stability of wind turbines. In this paper, the torsional dynamic model for PGT of semi-direct drive wind turbine was developed by lumped parameter method. According to the configuration and design parameters of the planetary gears, the natural frequencies are calculated, and the vibration modes are also analyzed. The actual wind speed is simulated by the weight sparse least squares support vector machines (WSLS- SVM), and the input torque load is also obtained. Considering the varying wind load and parameter excitations of system, the dynamic response of the PGT is calculated by numerical method.

Simulation Analysis of Planetary Gears Train of Semi-Direct Drive Wind Turbine Based on ADAMS

2015 ◽  
Vol 789-790 ◽  
pp. 311-315 ◽  
Author(s):  
Yan Li Cheng ◽  
Zheng Ming Xiao ◽  
Li Rong Huan ◽  
Fu Chen
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Simulation Analysis ◽  
Three Dimensional ◽  
Planetary Gear ◽  
Gear System ◽  
Direct Drive ◽  
Three Dimensional Model ◽  
Velocity Change ◽  
Planetary Gears

The speed increasing gearbox is the key part of the wind turbine and its role is to transmit power which is generated by wind turbines to the generator through the gear system. The single-stage planetary gears train system is commonly used in the semi-direct drive wind turbines. In this paper Pro/E is used to establish the three-dimensional model of the speed increasing planetary gear system of the semi-direct drive wind turbine. Motion pairs, drive and load of the model are added by ADAMS. Angular velocity change rule of the parts is obtained. The change rules of the mesh force of the planetary gears, ring and sun gear can be obtained through the dynamic simulation and analysis using the contact algorithm. These are useful to study the vibration and noise of the system.

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

scholarly journals Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1105 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Andrea Lombardi ◽  
Ludovico Terzi
Keyword(s):  
Standard Deviation ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Curve Analysis ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Features Selection ◽  
Power Curve ◽  
Working Parameters ◽  
Extracted Power

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

scholarly journals Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3396 ◽  
Author(s):  
Mingzhu Tang ◽  
Wei Chen ◽  
Qi Zhao ◽  
Huawei Wu ◽  
Wen Long ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Support Vector Regression ◽  
Confidence Intervals ◽  
Wind Turbines ◽  
Large Scale ◽  
Support Vector ◽  
Effective Response ◽  
Support Vector Regression Model ◽  
Doubly Fed

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

scholarly journals An experimental study of the optimal design parameters of a wind power tower used to improve the performance of vertical axis wind turbines

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879954
Author(s):  
Soo-Yong Cho ◽  
Sang-Kyu Choi ◽  
Jin-Gyun Kim ◽  
Chong-Hyun Cho
Keyword(s):  
Experimental Study ◽  
Optimal Design ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Turbines ◽  
Vertical Axis ◽  
Power Coefficient ◽  
Design Parameters ◽  
Vertical Axis Wind Turbine ◽  
Vertical Axis Wind Turbines

In order to augment the performance of vertical axis wind turbines, wind power towers have been used because they increase the frontal area. Typically, the wind power tower is installed as a circular column around a vertical axis wind turbine because the vertical axis wind turbine should be operated in an omnidirectional wind. As a result, the performance of the vertical axis wind turbine depends on the design parameters of the wind power tower. An experimental study was conducted in a wind tunnel to investigate the optimal design parameters of the wind power tower. Three different sizes of guide walls were applied to test with various wind power tower design parameters. The tested vertical axis wind turbine consisted of three blades of the NACA0018 profile and its solidity was 0.5. In order to simulate the operation in omnidirectional winds, the wind power tower was fabricated to be rotated. The performance of the vertical axis wind turbine was severely varied depending on the azimuthal location of the wind power tower. Comparison of the performance of the vertical axis wind turbine was performed based on the power coefficient obtained by averaging for the one periodic azimuth angle. The optimal design parameters were estimated using the results obtained under equal experimental conditions. When the non-dimensional inner gap was 0.3, the performance of the vertical axis wind turbine was better than any other gaps.

scholarly journals Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1671 ◽  
Author(s):  
Chia-Hung Yeh ◽  
Min-Hui Lin ◽  
Chien-Hung Lin ◽  
Cheng-En Yu ◽  
Mei-Juan Chen
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Sensor Data ◽  
Hybrid Network ◽  
Support Vector ◽  
Significant Information ◽  
Long Cycle ◽  
Power Company ◽  
Maintenance Time

Within Internet of Things (IoT) sensors, the challenge is how to dig out the potentially valuable information from the collected data to support decision making. This paper proposes a method based on machine learning to predict long cycle maintenance time of wind turbines for efficient management in the power company. Long cycle maintenance time prediction makes the power company operate wind turbines as cost-effectively as possible to maximize the profit. Sensor data including operation data, maintenance time data, and event codes are collected from 31 wind turbines in two wind farms. Data aggregation is performed to filter out some errors and get significant information from the data. Then, the hybrid network is built to train the predictive model based on the convolutional neural network (CNN) and support vector machine (SVM). The experimental results show that the prediction of the proposed method reaches high accuracy, which helps drive up the efficiency of wind turbine maintenance.

Probabilistic Multibody Modeling of Gearboxes for Wind Turbines

2011 ◽  
Author(s):  
Fisseha M. Alemayehu ◽  
Stephen Ekwaro-Osire
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Loading ◽  
Design Parameters ◽  
Probabilistic Design ◽  
Deterministic Approach ◽  
Early Failure ◽  
Multibody Modeling ◽  
Design Life ◽  
Multibody Dynamic

Gearboxes have been prone to early failure rather than any mechanical part of modern wind turbines, much earlier than their predicted design life. Some studies indicated that gearboxes of wind turbines fail during the first 3 to 5 years of operation of the system as opposed to the total design life of the wind turbine, which usually is 20 years. Consequently, such failures cause the highest down time and extremely expensive replacement activities. Gearboxes are subjected to torsional, bending and axial wind loads which are yet not fully defined. The uncertainty in loading conditions and system design parameters has brought about the importance of considering probabilistic design and modeling approach than the traditional deterministic approach. Accordingly, the motivation of this study is to improve the reliability of gearboxes for wind turbine applications. A probabilistic multibody dynamic modeling of the gearbox, that fully integrates uncertainties in wind loading and design parameters, is sought. This paper presents previous studies and finally proposes the above mentioned approach as a potential way of improving, in general, the reliability of wind energy and, in particular, the gearboxes in wind turbines.

Dynamic Modeling of Multi-Stage Planetary Gears Coupled with Bearings in Housing

2011 ◽  
Vol 86 ◽  
pp. 30-34
Author(s):  
Zheng Ming Xiao ◽  
Da Tong Qin
Keyword(s):  
Planetary Gear ◽  
Degree Of Freedom ◽  
Design Parameters ◽  
Parameter Method ◽  
Rotational Degree ◽  
Planetary Gears ◽  
Multi Stage ◽  
Lumped Parameter ◽  
Shield Tunnelling ◽  
Gyroscopic Effects

This work develops an analytical model of multi-stages planetary gear transmission (PGT) coupled with bearings in housing based on analyzing the displacement relationships of gearing system. The model adopts three planar degree-of-freedom for each of the central components, and the rotational degree-of-freedom for the planets of each stage. Considering the gyroscopic effects, the modified transverse-torsional model is established in the rotating Cartesian coordinates by lumped-parameter method, which is more accurate and may match with the physical model better than the purely torsional model. According to the design parameters of the 3-stage planetary gears of main reducer of shield tunnelling machine, the natural frequencies and vibration modes are investigated by using this transverse-torsional model.

Design Requirements for Floating Offshore Wind Turbines

2013 ◽  
Author(s):  
Xiaohong Chen ◽  
Qing Yu
Keyword(s):  
Wind Turbine ◽  
Case Studies ◽  
Wind Turbines ◽  
Offshore Wind ◽  
Design Parameters ◽  
Offshore Wind Turbine ◽  
Support Structure ◽  
Offshore Wind Turbines ◽  
Floating Offshore Wind Turbines ◽  
Design Requirements

This paper presents the research in support of the development of design requirements for floating offshore wind turbines (FOWTs). An overview of technical challenges in the design of FOWTs is discussed, followed by a summary of the case studies using representative FOWT concepts. Three design concepts, including a Spar-type, a TLP-type and a Semisubmersible-type floating support structure carrying a 5-MW offshore wind turbine, are selected for the case studies. Both operational and extreme storm conditions on the US Outer Continental Shelf (OCS) are considered. A state-of-the-art simulation technique is employed to perform fully coupled aero-hydro-servo-elastic analysis using the integrated FOWT model. This technique can take into account dynamic interactions among the turbine Rotor-Nacelle Assembly (RNA), turbine control system, floating support structure and stationkeeping system. The relative importance of various design parameters and their impact on the development of design criteria are evaluated through parametric analyses. The paper also introduces the design requirements put forward in the recently published ABS Guide for Building and Classing Floating Offshore Wind Turbine Installations (ABS, 2013).

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