Ryan F. Schkoda

Integration of mechanical and electrical hardware for testing full scale wind turbine nacelles

This paper describes in detail the 7.5 and 15 megawatt mechanical test benches located at the Wind Turbine Drivetrain Testing Facility (WTDTF) at Clemson Universitys Restoration Institute in North Charleston, SC, USA, including their ability to couple with a 15 megawatt, hardware-in-the-loop, electric grid simulator at the Duke Energy eGRID Center located at the same site. System topology of both the mechanical and electrical systems are discussed in detail and special attention is paid to how the two systems may be used simultaneously to test full scale wind turbine nacelles. Additionally, the results of two model development surveys are presented. These surveys were completed by the WTDTFs internal engineering team, suppliers, and customers, and are used to shape the model development strategy for the mechanical test benches. Finally, a control system application for the developed models is described.

System level dynamic modeling framework being developed at Clemson University's Wind Turbine Drivetrain Testing Facility

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.© 2013 ASME

Hardware-in-the-Loop Testing of Utility-Scale Wind Turbine Generators

Historically, wind turbine prototypes were tested in the field, which was--and continues to be--a slow and expensive process. As a result, wind turbine dynamometer facilities were developed to provide a more cost-effective alternative to field testing. New turbine designs were tested and the design models were validated using dynamometers to drive the turbines in a controlled environment. Over the years, both wind turbine dynamometer testing and computer technology have matured and improved, and the two are now being joined to provide hardware-in-the-loop (HIL) testing. This type of testing uses a computer to simulate the items that are missing from a dynamometer test, such as grid stiffness, voltage, frequency, rotor, and hub. Furthermore, wind input and changing electric grid conditions can now be simulated in real time. This recent advance has greatly increased the utility of dynamometer testing for the development of wind turbine systems.

An Improved Undergraduate Mechanical Engineering Laboratory Structure and Curriculum: Design and Assessment

The mechanical engineering department at Clemson University reevaluated its undergraduate laboratory experience and focused on improving various aspects of the three required laboratory courses. The faculty believe that these laboratory courses are a defining feature of the bachelor of science degree, as many graduates accept entry-level manufacturing positions or pursue graduate studies. The mechanical engineering laboratory courses at Clemson are stand-alone offerings in the undergraduate program, in contrast those schools which attach the labs to select courses. This structure allows a variety of experiments to be offered during each course, which can encompass various scientific and engineering topics. This paper reviews various changes to the laboratories, describes their implementation and presents evidence of their effectiveness. Examples of the improvements were: the development of printed manuals for both students and teaching assistants, the development of a unified training program for the teaching assistants, the introduction of new laboratory equipment and experiments, and revision of the current laboratory documentation. To evaluate the effectiveness of the implemented changes, survey results were analyzed. Overall, student satisfaction with the course improved significantly, as evidenced by the survey results.

Clustering of Cyclostationary Signals with Applications to Climate Station Sitings, Eliminations, and Merges

This paper considers methods to classify and discriminate the multidimensional cyclostationary climatological time series. The methods take into account both the seasonal mean cycles and random behavior in the series, simultaneously considering means and all component-by-component autocovariances. This improves classical Hotelling T2 statistics that classify through mean changes only, and constant-mean speech methods that classify exclusively through sample autocovariance differences. Here, two series are compared by assuming that both follow the same time series model; from this, a test statistic representing a distance between the two series is developed from linear prediction theory. This construction generates a level- α test statistic for Gaussian data that can be used to assess how different the two series are. The derived distances can be used in a clustering algorithm, e.g., to group series with similar behavior. Such information is useful to eliminate or merge the climate stations whose data are redundant to another station or to optimally locate a collection of stations. The techniques are first tested on simulated series with known structures, and then applied to 11 two-dimensional series in the National Oceanic and Atmospheric Administrations data buoy catalog. Natural clusters emerge which are geographically realistic. Specifically, the methods were able to perfectly group stations in the Gulf of Mexico, the Carolina Coast, the Pacific Ocean, and offshore New England.

Geartrain Reduction for Real-Time Simulation of a Multibody Wind Turbine Gearbox Model

This paper presents a reduction strategy for a multibody model of a 7.5 MW gearbox located at Clemson University’s Wind Turbine Drivetrain Testing Facility in North Charleston, SC. A model reduction is needed because of a high frequency dynamic associated with the input shaft of the gearbox which prevents the model from being executed in real-time. This particular gearbox has an atypical input stage configuration whereby the load path is split into two parallel shafts before being recombined at the sun shaft. The strategy includes removing model elements associated with high frequency vibration, merging model elements, and locating model elements where needed. The presented strategy successfully removes the problematic eigenvalues, maintains basic dynamic character, and results in a multibody gearbox model suitable for real-time simulation.Copyright

Hydraulic spool valve modeling for system level analysis

This paper presents a parameterized, dynamic model of Moog D671 hydraulic servovalve. The valve is a 4/3, center closed configuration. The model is developed based on first principles and is compared to a dynamic model in the form of a Simulink S-function made available by Moog Inc. The model is developed as part of a system level modeling, simulation, and analysis activity at Clemson Universitys Wind Turbine Drivetrain Testing Facility. The valve model is valuable because the valve is used to control a primary piece of test equipment at the facility and the model will be used for subsequent control system development activities. Results show that the derived model adequately reproduces the behavior of the supplied S-function while offering greatly improved functionality. Additionally, a discussion section address the observed differences between the derived model and the supplied S-function and offers a critique of the S-functions performance.

Characterizing the Influence of Abstraction in Full-Scale Wind Turbine Nacelle Testing

In recent years, there has been a growing interest in full-scale wind turbine nacelle testing to complement individual component testing. As a result, several wind turbine nacelle test benches have been built to perform such testing with the intent of loading the integrated components as they are in the field. However, when mounted on a test bench the nacelle is not on the top of a tower and does not have blades attached to it--this is a form of abstraction. This paper aims to quantify the influence of such an abstraction on the dynamic response of the nacelle through a series of simulation case studies. The responses of several nacelle components are studied including the main bearing, main shaft, gearbox supports, generator, and yaw bearing interface. Results are presented to highlight the differences in the dynamic response of the nacelle caused by the abstraction. Additionally, the authors provide recommendations for mitigating the effects of the abstraction.

Static uncertainty analysis of a wind turbine test bench's load application unit

Modern wind turbine test benches include devices for applying non-torque loads to the driveline of the device under test. The device studied in this paper works by using hydraulic actuators to apply forces to a large disk that is part of and rotates with the driveline. The applied actuator loads result in a net linear force and net moment at the nacelles control point (point along the driveline that corresponds to what would be the center of the blade hub). Direct measurement of the resulting control point loads is difficult due to their magnitudes (up to 2 million Newtons of linear force and 10 million Newton-meters of bending moment). The current strategy is to use actuator pressure measurements to estimate the loading at the control point. This paper investigates the sources and effects of uncertainty on static, control point load estimation.

A comparison of multivariate signal discrimination techniques

This paper investigates several techniques to discriminate two multivariate stationary signals. The methods considered include Gaussian likelihood ratio tests for variance equality, a chi-squared time-domain test, and a spectral-based test. The latter two tests assess equality of the multivariate autocovariance function of the two signals over many different lags. The Gaussian likelihood ratio test is perhaps best viewed as principal component analyses (PCA) without dimension reduction aspects; it can be modified to consider covariance features other than variances via dimension augmentation tactics. A simulation study is constructed that shows how one can make inappropriate conclusions with PCA tests, even when dimension augmentation techniques are used to incorporate non-zero lag autocovariances into the analysis. The various discrimination methods are first discussed. A simulation study then illuminates the various properties of the methods. In this pursuit, calculations are needed to identify several multivariate time series models with specific autocovariance properties. To demonstrate the applicability of the methods, nine US and Canadian weather stations from three distinct regions are clustered. Here, the spectral clustering perfectly identified distinct regions, the chi-squared test performed marginally, and the PCA/likelihood ratio method did not perform well.