Wind Turbine Drivetrain Test Bench Capability to Replicate Design Loads: Part II — Case Study of a Multi-MW Drivetrain

2017 ◽  
Author(s):  
Philippe Giguère ◽  
John R. Wagner
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
Test Bench ◽  
Combined Loading ◽  
Load Application ◽  
Systematic Evaluation ◽  
Compensation Methods ◽  
Design Loads ◽  
Stochastic Time Series ◽  
Resultant Forces

The systematic evaluation of wind turbine drivetrains using hardware-in-the-loop strategies (previously presented in Part I) is demonstrated using a state-of-the-art multi-MW drivetrain design and a 7.5-MW test bench. The test bench has the capability to apply both torque and non-torque loads to the electro-mechanical drivetrain. The proposed method to evaluate the capability of a test bench to impose the loads of interest uses design loads of the drivetrain and the test bench load application unit limits as inputs. The design loads are defined by stochastic time series of the longitudinal, lateral, and vertical forces as well as the yawing and nodding bending moments that the load application unit can concurrently apply to the drivetrain (i.e., combined loading). A total of 14 time series sets are considered to capture the minimum and maximum values of the longitudinal, lateral, vertical, and resultant forces as well as the yawing, nodding, and resultant moments. These time series are processed individually to calculate two metrics: the coverage and the capability ratio of the test bench. The former is a percentage of the time series that be applied by the test bench, and the latter indicates an excess (or deficit) in load application capability as compared with the selected design loads. The results are presented and interpreted using the previously described methodology. The findings suggest a good match between test bench capability and the loads of interest in general, and also points to challenges. These discoveries establish a basis for the experimental verification and the development of compensation methods to enhance test bench capabilities.

Wind Turbine Drivetrain Test Bench Capability to Replicate Design Loads: Part I — Evaluation Methodology

2017 ◽  
Author(s):  
Philippe Giguère ◽  
John R. Wagner
Keyword(s):  
Wind Turbine ◽  
Test Bench ◽  
Task Force ◽  
Field Measurements ◽  
Evaluation Methodology ◽  
Systematic Method ◽  
Mechanical Loads ◽  
Testing Facility ◽  
Design Loads ◽  
Replicate Design

The utilization of a ground-based testing facility for full-size wind turbine drivetrains is growing. Several test benches have been developed to apply torque and non-torque loads. These mechanical loads can be the loads used to design the drivetrain components or loads obtained from field measurements. Irrespective of the reason for testing a drivetrain, the selected test bench should have the capability to impose the loads of interest. The design of these test benches and their capabilities vary, and the loads of interest vary between drivetrain designs. A systematic method to evaluate the capability of a test bench to impose the loads of interest has been developed. This method can be applied to any test bench and drivetrain design. Part I of this paper presents the methodology and recommendations for presenting and interpreting the results. The demonstration of the method is the focus of part II. Overall, this two-part paper aims to establish guidelines for consideration by the IEA task force 35 for ground based testing for wind turbines and their components.

scholarly journals Characterization of Export Regimes in Concentration–Discharge Plots via an Advanced Time-Series Model and Event-Based Sampling Strategies

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1723
Author(s):  
Ana Gonzalez-Nicolas ◽  
Marc Schwientek ◽  
Michael Sinsbeck ◽  
Wolfgang Nowak
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Time Series Model ◽  
Data Series ◽  
Additional Parameter ◽  
Sampling Strategies ◽  
Discharge Rates ◽  
Regression Approach ◽  
Event Based ◽  
Stochastic Time Series

Currently, the export regime of a catchment is often characterized by the relationship between compound concentration and discharge in the catchment outlet or, more specifically, by the regression slope in log-concentrations versus log-discharge plots. However, the scattered points in these plots usually do not follow a plain linear regression representation because of different processes (e.g., hysteresis effects). This work proposes a simple stochastic time-series model for simulating compound concentrations in a river based on river discharge. Our model has an explicit transition parameter that can morph the model between chemostatic behavior and chemodynamic behavior. As opposed to the typically used linear regression approach, our model has an additional parameter to account for hysteresis by including correlation over time. We demonstrate the advantages of our model using a high-frequency data series of nitrate concentrations collected with in situ analyzers in a catchment in Germany. Furthermore, we identify event-based optimal scheduling rules for sampling strategies. Overall, our results show that (i) our model is much more robust for estimating the export regime than the usually used regression approach, and (ii) sampling strategies based on extreme events (including both high and low discharge rates) are key to reducing the prediction uncertainty of the catchment behavior. Thus, the results of this study can help characterize the export regime of a catchment and manage water pollution in rivers at lower monitoring costs.

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

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.

scholarly journals Identification of an unstable ARMA equation

2001 ◽  
Vol 7 (1) ◽  
pp. 97-112 ◽  
Author(s):  
Yulia R. Gel ◽  
Vladimir N. Fomin
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Infinite Order ◽  
Time Series Model ◽  
Identification Algorithm ◽  
Data Handling ◽  
Stochastic Time Series ◽  
Stochastic Time ◽  
Strongly Consistent ◽  
Minimal Phase

Usually the coefficients in a stochastic time series model are partially or entirely unknown when the realization of the time series is observed. Sometimes the unknown coefficients can be estimated from the realization with the required accuracy. That will eventually allow optimizing the data handling of the stochastic time series.Here it is shown that the recurrent least-squares (LS) procedure provides strongly consistent estimates for a linear autoregressive (AR) equation of infinite order obtained from a minimal phase regressive (ARMA) equation. The LS identification algorithm is accomplished by the Padé approximation used for the estimation of the unknown ARMA parameters.

Fractal and Multifractal in Stochastic Time Series

2020 ◽  
pp. 129-151
Author(s):  
Santo Banerjee ◽  
M K Hassan ◽  
Sayan Mukherjee ◽  
A Gowrisankar
Keyword(s):  
Time Series ◽  
Stochastic Time Series ◽  
Stochastic Time

scholarly journals Foundations, Design, and Dynamic Performance of Wind Turbines: Overview and Challenges in Sudan

2021 ◽  
Vol 9 (1) ◽  
pp. 96-103
Author(s):  
Ruba Asim Hamza ◽  
Amged Osman Abdelatif
Keyword(s):  
Dynamic Response ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Structural Damage ◽  
Renewable Energy Sources ◽  
Dynamic Performance ◽  
Scaled Model ◽  
Static Loads ◽  
Design Loads ◽  
Foundation Type

Sudan is one of the developing countries that suffers from a lack of electricity, where the national electrification rate is estimated at 38.5%. In order to solve this problem, it is possible to use renewable energy sources such as wind energy. Beside many aspects to be considered at the design of wind turbine foundations, more attention should be given to the geotechnical part. There are many types of foundations for wind turbines. The foundation must satisfy two design criteria: 1) It should be safe against bearing failure in soils under design loads and settlements during the life of the structure must not cause structural damage; 2) In addition to static loads, wind turbine foundations loads are extremely eccentrically and the loading is usually highly dynamic. Therefore, the selection of foundation type should consider these two criteria taking into account the nature and magnitude of these loads. This paper presents a review of different types of wind turbine foundations of focusing on on-shore wind turbine foundation types and the dynamic response of wind turbine. The paper also demonstrate experimentally the dynamic response of the wind turbines using wind tunnel facility test on a scaled model.  

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