Dynamic Model Development of Wind Turbine Drivetrains by Using Sensor Measurements

2021 ◽  
Author(s):  
Diederik van Binsbergen ◽  
Amir R. Nejad ◽  
Jan Helsen
Keyword(s):  
Wind Turbine ◽  
Angular Displacement ◽  
Model Development ◽  
Rotational Frequency ◽  
Sensor Data ◽  
Gear Mesh ◽  
Modal Properties ◽  
Vibration Sensors ◽  
Displacement Sensors ◽  
Simulation Based

Abstract This paper aims to analyze the feasibility of establishing a dynamic drivetrain model from condition monitoring measurements. In this study SCADA data and further sensor data is analyzed from a 1.5MW wind turbine, provided by the National Renewable Energy Laboratory. A multibody model of the drivetrain is made and simulation based sensors are placed on bearings to look at the possibility to obtain geometrical and modal properties from simulation based vibration sensors. Results show that the axial proxy sensor did not provide any usable system information due to its application purpose. SCADA data did not meet the Nyquist frequency and cannot be used to determine geometrical or modal properties. Strain gauges on the shaft can provide the shaft rotational frequency, while torque and angular displacement sensors can provide the torsional eigenfrequency of the system. Simulation based vibration sensors are able to capture gear mesh frequencies, harmonics, sideband frequencies and shaft rotational frequencies.

scholarly journals Experimental Validation of Angular Velocity Measurements for Wind Turbines Drivetrain Condition Monitoring

2019 ◽  
Author(s):  
Farid K. Moghadam ◽  
Amir R. Nejad
Keyword(s):  
Angular Velocity ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
High Speed ◽  
Performance Monitoring ◽  
Error Function ◽  
Sensor Data ◽  
Radial Acceleration ◽  
Vibration Sensors ◽  
Defect Frequency

Abstract Drivetrain bearings are seen as the most common reason of the wind turbine drivetrain system failures and the consequent downtimes. In this study, the angular velocity error function is used for the condition monitoring of the bearings and gears in the wind turbine drivetrain. This approach benefits from using the sensor data and the dedicated communication network which already exist in the turbine for performance monitoring purposes. Minor required modification includes an additional moderate sampling frequency encoder without any need of adding an extra condition monitoring system. The additional encoder is placed on the low speed shaft and can also be used as the backup for the high speed shaft encoder which is critical for turbine control purposes. A theory based on the variations of the energy of response around the defect frequency is suggested to detect abnormalities in the drivetrain operation. The proposed angular velocity based method is compared with the classical vibration-based detection approach based on axial/radial acceleration data, for the faults initiated by different types of excitation sources. The method is experimentally evaluated using the data obtained from the encoders and vibration sensors of an operational wind turbine.

System Level Dynamic Modeling Framework Being Developed at Clemson University’s Wind Turbine Drivetrain Testing Facility

2013 ◽  
Author(s):  
Ryan Schkoda ◽  
Konstantin Bulgakov ◽  
Kalyan Chakravarthy Addepalli ◽  
Imtiaz Haque
Keyword(s):  
Wind Turbine ◽  
Dynamic Modeling ◽  
Preliminary Analysis ◽  
Collaborative Work ◽  
Model Development ◽  
System Level ◽  
Modeling Framework ◽  
Testing Facility ◽  
Simulation Strategy ◽  
North Charleston

This paper describes the system level, dynamic modeling and simulation strategy being developed at the Wind Turbine Drivetrain Testing Facility (WTDTF) at Clemson University’s Restoration Institute in North Charleston, SC, USA. An extensible framework that allows various workflows has been constructed and used to conduct preliminary analysis of one of the facility’s test benches. The framework dictates that component and subsystem models be developed according to a list of identified needs and modeled in software best suited for the particular task. Models are then integrated according to the desired execution target. This approach allows for compartmentalized model development which is well suited for collaborative work. The framework has been applied to one of the test benches and has allowed researches to begin characterizing its behavior in the time and frequency domain.

scholarly journals Time-Domain Modeling of Tower Shadow and Wind Shear in Wind Turbines

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Swagata Das ◽  
Neeraj Karnik ◽  
Surya Santoso
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Time Domain ◽  
Wind Shear ◽  
Electrical Power ◽  
Rotational Frequency ◽  
Domain Modeling ◽  
Periodic Fluctuations ◽  
Electrical Power Output ◽  
Tower Shadow

Tower shadow and wind shear contribute to periodic fluctuations in electrical power output of a wind turbine generator. The frequency of the periodic fluctuations is times the blade rotational frequency , where is the number of blades. For three-bladed wind turbines, this inherent characteristic is known as the effect. In a weak-power system, it results in voltage fluctuation or flicker at the point of common coupling of the wind turbine to the grid. The phenomenon is important to model so as to evaluate the flicker magnitude at the design level. Hence, the paper aims to develop a detailed time-domain upwind fixed speed wind turbine model which includes the turbine's aerodynamic, mechanical, electrical, as well as tower shadow and wind shear components. The model allows users to input factors such as terrain, tower height, and tower diameter to calculate the oscillations. The model can be expanded to suit studies involving variable speed wind turbines. Six case studies demonstrate how the model can be used for studying wind turbine interconnection and voltage flicker analysis. Results indicate that the model performs as expected.

Design of a High-Level Diagnostic System

1984 ◽  
Vol 106 (1) ◽  
pp. 17-21 ◽  
Author(s):  
R. H. Lyon ◽  
R. G. DeJong
Keyword(s):  
Signal Processing ◽  
Diagnostic System ◽  
Multiple Sensors ◽  
Gear Mesh ◽  
Vibration Sensors ◽  
Internal Sources ◽  
High Level ◽  
Signal Processing Methods ◽  
Excitation Propagation ◽  
Preliminary Application

The use of vibration signals produced by the operation of a machine to control its operations and detect developing faults is appealing because of the ruggedness of vibration sensors and their ease of placement. A diagnostic system that employs a few sensors, remotely located from a number of vibration generating mechanisms, to analyze the performance of these mechanisms is termed a “high-level” diagnostic system. The goal of such a diagnostic system is to infer from the remote vibration sensors the characteristics of the internal sources (such as forces and pressures) which could not be easily measured directly. The use of multiple sensors and advanced signal processing methods is necessary for such a system to be viable. A series of studies on vibration excitation, propagation, sensing, and signal processing that demonstrate the basic feasibility of such a diagnostic system are described. The design of a high-level diagnostic system for detecting the combustion and gear mesh excitations in a diesel engine analysis is presented, along with the results of a preliminary application of the system.

scholarly journals Wind Turbine Noise and Sleep: Pilot Studies on the Influence of Noise Characteristics

2018 ◽  
Vol 15 (11) ◽  
pp. 2573 ◽  
Author(s):  
Julia Ageborg Morsing ◽  
Michael Smith ◽  
Mikael Ögren ◽  
Pontus Thorsson ◽  
Eja Pedersen ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Amplitude Modulation ◽  
Wind Farms ◽  
Rotational Frequency ◽  
Pilot Studies ◽  
Wind Turbine Noise ◽  
Acoustical Properties ◽  
Noise Characteristics ◽  
Sound Environment ◽  
The Impact

The number of onshore wind turbines in Europe has greatly increased over recent years, a trend which can be expected to continue. However, the effects of wind turbine noise on long-term health outcomes for residents living near wind farms is largely unknown, although sleep disturbance may be a cause for particular concern. Presented here are two pilot studies with the aim of examining the acoustical properties of wind turbine noise that might be of special relevance regarding effects on sleep. In both pilots, six participants spent five consecutive nights in a sound environment laboratory. During three of the nights, participants were exposed to wind turbine noise with variations in sound pressure level, amplitude modulation strength and frequency, spectral content, turbine rotational frequency and beating behaviour. The impact of noise on sleep was measured using polysomnography and questionnaires. During nights with wind turbine noise there was more frequent awakening, less deep sleep, less continuous N2 sleep and increased subjective disturbance compared to control nights. The findings indicated that amplitude modulation strength, spectral frequency and the presence of strong beats might be of particular importance for adverse sleep effects. The findings will be used in the development of experimental exposures for use in future, larger studies.

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.

Risk Evaluation by Human Trajectory Simulation Based on Real Data

2011 ◽  
Vol 15 (2) ◽  
pp. 220-225
Author(s):  
Kiyoshi Izumi ◽  
◽  
Yoshifumi Nishida ◽  
Yoichi Motomura ◽  
Keyword(s):  
Risk Evaluation ◽  
Real Data ◽  
Public Spaces ◽  
Layout Design ◽  
Sensor Data ◽  
Agent Based Simulation ◽  
New Approach ◽  
Agent Based ◽  
Trajectory Simulation ◽  
Simulation Based

This paper proposes a new approach integrating the modeling of moving persons from sensor data and agent-based simulation for indoor layout design viewed from preventing children’s accidents. Our model focuses on interaction between indoor objects and children to estimate the risk of indoor accidents. We discuss the agent-based simulation of multiple persons moving in public spaces and its application to evaluating information presentation for guidance.

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