scholarly journals Damping Measurements Using Operational Data

1996 ◽  
Vol 118 (3) ◽  
pp. 190-193 ◽  
Author(s):  
G. H. James ◽  
T. G. Carne ◽  
P. S. Veers
Keyword(s):  
Wind Turbine ◽  
Strain Gauge ◽  
Test Bed ◽  
Data Set ◽  
Response Data ◽  
Modal Damping ◽  
Gauge Data ◽  
Prediction And Control ◽  
And Control ◽  
Operational Data

We have measured modal damping using strain-gauge data from an operating wind turbine. This new technique for measuring modal damping is easier and less expensive than previously used methods. Auto-correlation and cross-correlation functions of the strain-gauge data have been shown to consist of decaying sinusoids which correspond to the modal frequencies and damping ratios of the wind turbine. We have verified the method by extracting damping values from an analytically generated data set. Actual operating response data from the DOE/Sandia 34-m Test Bed has been used to calculate modal damping ratios as a function of rotor rotation rate. This capability will allow more accurate fatigue life prediction and control.

Stochastic Linearization by Data Dependent Systems

1977 ◽  
Vol 99 (4) ◽  
pp. 221-226 ◽  
Author(s):  
S. M. Pandit
Keyword(s):  
Furnace Operation ◽  
System Response ◽  
Ticket Sales ◽  
Response Data ◽  
Machine Tool Chatter ◽  
Chatter Vibrations ◽  
Stochastic Linearization ◽  
Airline Passenger ◽  
Prediction And Control ◽  
And Control

The paper presents and illustrates a method of stochastic linearization of nonlinear systems. The system response to white noise excitation is modeled by a differential equation, which provides the necessary transfer function. The linearization is optimal in the mean squared sense within the statistical limits imposed by the response. Since the linearization is accomplished purely from the response data, governing equations of the system need not be known. An application to machine tool chatter vibrations illustrates stability assessment and modal analysis. The ease with which optimal prediction and control equations can be derived and implemented is shown by an application to blast furnace operation. Detection and verification of limit cycles are illustrated by a model for airline passenger ticket sales data.

Design and Control Framework for Selecting Wind Turbine Gear Ratios Based on Optimal Power Generation and Blade Stress

2016 ◽  
Author(s):  
Amrita Lall ◽  
Hamid Khakpour Nejadkhaki ◽  
John Hall
Keyword(s):  
Wind Turbine ◽  
Energy Production ◽  
Control Algorithm ◽  
Power Production ◽  
Wind Data ◽  
Data Set ◽  
Blade Root ◽  
Partial Load ◽  
And Control ◽  
Root Stress

A variable ratio gearbox (VRG) can enable small wind turbines to operate at discrete variable rotor speeds. This reliable, low-cost, alternative does not require power conversion equipment, as is the case with conventional variable speed. Previous work conducted by the author has demonstrated that a VRG can increase the power production for a fixed-speed system with passive blades. The current study characterizes the performance of a wind turbine equipped with a VRG and active blades. The contribution of this work is an integrative framework that optimizes power production with blade root stress. It works by defining a set of control rules that specify the VRG gear ratio and pitch angle that will be used in relation to wind speed. Three ratios are selected through the proposed procedure. A case study based on the simulation of a 300-kW wind turbine model is performed to demonstrate the proposed technique. The model is constructed with aerodynamic, mechanical, and electrical submodels. These drivetrain components work together to simulate the conversion of moving air to electrical power. The blade element momentum (BEM) technique is used here to compute the blade loading. The resulting torque and rotor speed are reduced and increased, respectively, through the mechanical system gearbox. The output from this is then applied to the electrical generator. The BEM technique is also used here to determine the bending and thrust and loads that are applied to the blade. The stress in the root of the blade is then determined based on these loads, and that caused by centrifugal force and gravity. The proposed method devises a VRG design and control algorithm based on the unique wind conditions at a given installation site. Two case studies are conducted using wind data sets provided by the National Renewable Energy Laboratory (NREL). Low and high-speed data set are selected as inputs to demonstrate the versatility of the proposed method. Dynamic programming is used to reduce the computational expense. This enables the simulation of an exhaustive set of potential VRG combinations over each set of recorded wind data. Each possible combination is evaluated in terms of the total energy production and blade-root stress produced over the simulation period. A set of weights is applied to a multi-objective function that computes the cost associated with each combination. A Pareto analysis is then used to identify the VRG combination and establish the control algorithm for both systems. The results suggest that the VRG can improve energy production in the partial-load region by roughly 10% in both cases. Although stress increases in Region 2, it decreases in Region 3, and overall, through the optimal selection of gear combinations.

A Method for Analyzing Wind Turbine Dynamic Response Test Data

1988 ◽  
Vol 110 (4) ◽  
pp. 335-339 ◽  
Author(s):  
A. C. Hansen
Keyword(s):  
Time Series ◽  
Dynamic Response ◽  
Wind Turbine ◽  
Test Data ◽  
Fourier Coefficients ◽  
Structural Response ◽  
Poor Correlation ◽  
Data Set ◽  
Response Data ◽  
Original Time

Correlation of wind turbine structural response with ambient wind conditions is an essential but expensive and difficult task. The numbers of variables involved in a typical atmospheric test, the poor correlation between measured instantaneous winds and the actual wind across a rotor disc, and the range of input and response time scales involved all make the correlation task formidable. This paper describes a method which has proven effective for analyzing test data and gaining insight into wind turbine behavior. The method basically consists of representing the dynamic response data in terms of its Fourier Series. A time-series of Fourier coefficients is then created to replace the original time-series raw data. The entire data set, consisting of thousands of rotor revolutions is subdivided into hundreds of sets, each consisting of the azimuth average of (typically) two to ten revolutions. One set of Fourier coefficients (magnitudes and phases of response) is calculated for each azimuth average. The resulting reduced data has a greatly compressed volume with virtually no loss of information. The result is greater insight and a manageable data set size. This new technique is demonstrated for two different wind turbines, an ESI-80 and a Hamilton Standard WTS-4.

scholarly journals Numerical and Experimental Methods for the Assessment of Wind Turbine Control Upgrades

2018 ◽  
Vol 8 (12) ◽  
pp. 2639 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Francesco Berno ◽  
Ludovico Terzi
Keyword(s):  
Control System ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Power Curve ◽  
Data Set ◽  
Wind Turbine Control ◽  
The Impact ◽  
Operational Data

Megawatt-scale wind turbine technology is nowadays mature and, therefore, several technical improvements in order to optimize the efficiency of wind power conversion have been recently spreading in the industry. Due to the nonstationary conditions to which wind turbines are subjected because of the stochastic nature of the source, the quantification of the impact of wind turbine power curve upgrades is a complex task and in general, it has been observed that the efficiency of the upgrades can vary considerably depending on the wind flow conditions at the microscale level. In this work, a test case of wind turbine control system improvement was studied numerically and through operational data. The wind turbine is multi-megawatt; it is part of a wind farm sited in a complex terrain in Italy, featuring 17 wind turbines. The analyzed control upgrade is an optimization of the revolutions per minute (rpm) management. The impact of this upgrade was quantified through a method based on operational data: It consists of the study, before and after the upgrade, of the residuals between the measured power output of the wind turbine of interest and an appropriate model of the power output itself. The input variables for the model were selected to be some operational parameters of the nearby wind turbines: They were selected from the data set at disposal with a stepwise regression algorithm. This work also includes a numerical characterization of the problem, by means of aeroelastic simulations performed with the FAST software: By mimicking the pre- and post-upgrade generator rpm–generator torque curve, it is subsequently possible to estimate how the wind turbine power curve changes. The main result of this work is that the two estimates of production improvement have the same order of magnitude (1.0% of the production below rated power). In general, this study sheds light on the perspective of employing not only operational data, but also a sort of digital replica of the wind turbine of interest, in order to reliably quantify the impact of control system upgrades.

An insight into Case Management and Challenges in Veteran Suicide Prevention

2020 ◽  
pp. 14-25
Author(s):  
Tamara Green
Keyword(s):  
Case Management ◽  
Suicide Prevention ◽  
Suicide Risk ◽  
Digital Transformation ◽  
Prevention Science ◽  
Historical Records ◽  
Military Members ◽  
Prediction And Control ◽  
And Control ◽  
Insight Into

Much of the literature, policies, programs, and investment has been made on mental health, case management, and suicide prevention of veterans. The Australian “veteran community is facing a suicide epidemic for the reasons that are extremely complex and beyond the scope of those currently dealing with them.” (Menz, D: 2019). Only limited work has considered the digital transformation of loosely and manual-based historical records and no enablement of Artificial Intelligence (A.I) and machine learning to suicide risk prediction and control for serving military members and veterans to date. This paper presents issues and challenges in suicide prevention and management of veterans, from the standing of policymakers to stakeholders, campaigners of veteran suicide prevention, science and big data, and an opportunity for the digital transformation of case management.

A case study of wear-type rail corrugation prediction and control using speed variation

2009 ◽  
Vol 325 (1-2) ◽  
pp. 85-105 ◽  
Author(s):  
P.A. Meehan ◽  
P.A. Bellette ◽  
R.D. Batten ◽  
W.J.T. Daniel ◽  
R.J. Horwood
Keyword(s):  
Rail Corrugation ◽  
Speed Variation ◽  
Prediction And Control ◽  
And Control
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