Data Analytics Methods in Wind Turbine Design and Operations
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This dissertation develops sophisticated data analytic methods to analyze structural loads on, and power generation of, wind turbines. Wind turbines, which convert the kinetic energy in wind into electrical power, are operated within stochastic environments. To account for the influence of environmental factors, we employ a conditional approach by modeling the expectation or distribution of response of interest, be it the structural load or power output, conditional on a set of environmental factors. Because of the different nature associated with the two types of responses, our methods also come in different forms, conducted through two studies. The first study presents a Bayesian parametric model for the purpose of estimating the extreme load on a wind turbine. The extreme load is the highest stress level that the turbine structure would experience during its service lifetime. A wind turbine should be designed to resist such a high load to avoid catastrophic structural failures. To assess the extreme load, turbine structural responses are evaluated by conducting field measurement campaigns or performing aeroelastic simulation studies. In general, data obtained in either case are not sufficient to represent various loading responses under all possible weather conditions. An appropriate extrapolation is necessary to characterize the structural loads in a turbine’s service life. This study devises a Bayesian spline method for this extrapolation purpose and applies the method to three sets of load response data to estimate the corresponding extreme loads at the roots of the turbine blades. In the second study, we propose an additive multivariate kernel method as a new power curve model, which is able to incorporate a variety of environmental factors in addition to merely the wind speed. In the wind industry, a power curve refers to the functional relationship between the power output generated by a wind turbine and the wind speed at the time of power generation. Power curves are used in practice for a number of important tasks including predicting wind power production and assessing a turbine’s energy production efficiency. Nevertheless, actual wind power data indicate that the power output is affected by more than just wind speed. Several other environmental factors, such as wind direction, air density, humidity, turbulence intensity, and wind shears, have potential impact. Yet, in industry practice, as well as in the literature, current power curve models primarily consider wind speed and, with comparatively less frequency, wind speed and direction. Our model provides, conditional on a given environmental condition, both the point estimation and density estimation of the power output. It is able to capture the nonlinear relationships between environmental factors and wind power output, as well as the high-order inter- action effects among some of the environmental factors. To illustrate the application of the new power curve model, we conduct case studies that demonstrate how the new method can help with quantifying the benefit of vortex generator installation, advising pitch control adjustment, and facilitating the diagnosis of faults.
Lee, Giwhyun (2013). Data Analytics Methods in Wind Turbine Design and Operations. Doctoral dissertation, Texas A & M University. Available electronically from