Nonlinear and Sampled Data Control of Wind Turbine

This chapter aims to investigate the effectiveness of the nonlinear control-based model and the sampled-data design through the power system application. In particular, the study will focus on a model of a wind turbine system fed by a doubly fed induction generator (DFIG). First of all, a detail dynamical model of a DFIG-based wind-turbine grid-connected system is presented in the direct and quadratic synchronous reference frame. Afterward, mathematical modeling is performed for the nonlinear and sampled data systems. The nonlinear control will ensure the reproduction of the rotor direct and quadratic current that converge to the reference signal generated from. The proposed sampled-data system is built upon the nonlinear model and is introduced as an alternative of the classical discrete control which is known as emulation design. The simulation’s results will show that implementing the approximate feedback will yield better results than the one obtained from the mere emulation

Development of a Self-healing and Rejuvenating Mechanisms for Asphalt Mixtures Containing Recycled Asphalt Shingle

The objective of this study was to test the hypothesis that hollow-fibers encapsulating a rejuvenator product could improve both self-healing, rejuvenation, and mechanical properties of asphalt mixtures. Hollow-fibers containing a rejuvenating product were synthesized via a wet spinning procedure with sodium-alginate polymer as the encapsulating material. An optimization of the production parameters for the synthesis of fibers was performed to develop fibers suitable for high-temperature and shear stress environment typical of asphalt mixture production. A self-healing experiment was conducted to evaluate the healing/rejuvenation capabilities of sodium-alginate fibers in asphalt mixtures with varying types of binders and recycled materials. Based on the self-healing experiment, a 5% fiber content was determined to be the optimum fiber content to enhance the self-healing ability of asphalt mixtures. In addition, the effect of different fiber contents on binder blends and asphalt mixtures was evaluated by performing the Multiple Stress Creep Recovery (MSCR) and Semi-Circular Bending (SCB) tests. Results of the self-healing experiment showed that the enhancement in the healing recovery depends on the breakage of the fibers. When the fibers break, the rejuvenator is released resulting in softening of the binder. In contrast, when the fibers do not break, they act as a reinforcement for the mix. Loaded Wheel Tester (LWT) test results showed a performance improvement against permanent deformation for asphalt mixtures containing recycled materials with sodium-alginate fibers compared to conventional asphalt mixtures. Furthermore, SCB test results showed that the addition of sodium-alginate fibers enhanced the fracture properties of asphalt mixtures with Recycled Asphalt Shingle (RAS) at intermediate temperatures. Moreover, the addition of fibers in mixtures with recycled materials resulted in an improved performance against low-temperature cracking as the mixtures resisted higher stresses before failure

Nonlinear and sampled data control with application to power systems

Sampled data systems have come into practical importance for a variety of reasons. The earliest of these had primarily to do with economy of design. A more recent surge of interest was due to increase utilization of digital computers as controllers in feedback systems. This thesis contributes some control design for a class of nonlinear system exhibition linear output. The solution of several nonlinear control problems required the cancellation of some intrinsic dynamics (so-called zero dynamics) of the plant under feedback. It results that the so-dened control will ensure stability in closed-loop if and only if the dynamics to cancel are stable. What if those dynamics are unstable? Classical control strategies through inversion might solve the problem while making the closed loop system unstable. This thesis aims to introduce a solution for such a problem. The main idea behind our work is to stabilize the nonminimum phase system in continuous- time and undersampling using zero dynamics concept. The overall work in this thesis is divided into two parts. In Part I, we introduce a feedback control designs for the input-output stabilization and the Disturbance Decoupling problems of Single Input Single Output nonlinear systems. A case study is presented, to illustrate an engineering application of results. Part II illustrates the results obtained based on the Articial Intelligent Systems in power system machines. We note that even though the use of some of the AI techniques such as Fuzzy Logic and Neural Network does not require the computation of the model of the application, but it will still suer from some drawbacks especially regarding the implementation in practical applications. An alternative used approach is to use control techniques such as PID in the approximated linear model. This design is very well known to be used, but it does not take into account the non-linearity of the model. In fact, it seems that control design that is based on nonlinear control provide better performances

Maintenance and Restriping Strategies for Pavement Markings on Asphalt Pavements in Louisiana

In Louisiana, most districts restripe their roadways using waterborne paints every other year; this strategy is questionable in terms of efficiency and economy. Meanwhile, previous studies showed substantial variability in the paint service life throughout the United States ranging between 0.25 and 6.2 years. Shortcomings in modeling the retroreflectivity of waterborne paints appear to significantly contribute to these variations as several studies predicted these values using degradation curves with a coefficient of determination (R2) as low as 0.1. Therefore, the objective of this study was to (i) develop new cost-effective restriping strategies using 4-inch (15-mil thickness) and 6-inch (25-mil thickness) wide waterborne paints when applied on asphalt pavements in hot and humid climates, and (ii) employ an advanced machinelearning algorithm to develop performance prediction models for waterborne paints considering the variables that are believed to affect their performance. To achieve these objectives, National Transportation Product Evaluation Program (NTPEP) data were collected and analyzed to evaluate the field performance of waterborne paints commonly used in Southern United States. Results indicated that 4-inch wide standard paints exhibited service life up to four years depending on the line color, traffic and initial retroreflectivity, while 4-inch wide high-build paints had a service life of at least three years. Based on a life-cycle cost analysis, it was concluded that LaDOTD could restripe their district roads every three years instead of the current two-year period using the same product (4-inch or 6-inch wide) saving about $20 or$2 million, respectively, every year when restriping a 5,000-mile network. Additionally two machine-learning models were developed with an acceptable level of accuracy, and that can predict the skip and wheel retroreflectivity of waterborne paints for up to three years using only the initial measured retroreflectivity and the anticipated project conditions over the intended prediction horizon, such as line color, traffic, air temperature, etc. These models could be used by transportation agencies throughout the United States to (1) compare between different products and select the best product for a specific project, and (2) determine the expected service life of a specific product based on a specified threshold retroreflectivity to plan for future restriping activities

Quantifying heteroskedasticity metrics

This study proposes a quantification measure for heteroskedasticity in the time series. Two methods are introduced for quantifying heteroskedasticity: Slope of Local Variance Index (SoLVI) and a statistical divergence method using Bhattacharys coefficient. Both measures show reliability in measuring and quantifying heteroskedasticity in comparison to numerical and hypothesis heteroskedasticity tests

Development of Decision Trees for the Selection of Pavement Maintenance and Rehabilitation Activities in South-Central United States

Over time, new pavements deteriorate under the combined effects of traffic loading and the environment, no matter how well-designed or constructed. In general, maintenance and rehabilitation activities are employed to slow down or reset the rate of pavement deterioration. Cement-Stabilized Full Depth Reclamation (CSFDR) is a common rehabilitation treatment used by transportation agencies, specifically in Louisiana. Likewise, Ultra-Thin overlay (UTO) is a pavement maintenance treatment that has increased in popularity in recent years in Region 6. Yet, several gaps exist in the literature regarding the long-term field performance and cost-effectiveness of these two treatments especially in hot and humid climates. Therefore, the key objectives of this study were to assess the immediate benefits and long-term field performance as well as the cost-effectiveness of these two treatments in Louisiana. To achieve these objectives, numerous CSFDR and UTO projects were identified from the Louisiana Department of Transportation (LaDOTD) Pavement Management System (PMS) database and analyzed in terms of alligator cracks, rutting, random cracks, and roughness over a monitoring period of up to 15 years. Results indicated that the performance of CSFDR is significantly affected by the pre-treatment pavement conditions, applied overlay thickness, and traffic. Results also indicated that CSFDR projects would usually fail due to the development of random cracks. This could be attributed to the development of shrinkage cracks, which is a common problem with cement stabilization in Louisiana. A regression model was developed to predict the service life of CSFDR based on project conditions. Results also showed that UTO considerably extended the Pavement Service Life (PSL) for all the distress indices. This extension varied based on the pre-treatment pavement conditions and traffic level. As such, a predictive model was developed, with reasonable accuracy, to predict the extension in PSL of UTO based on project conditions. The developed models in this project for CSFDR and UTO will help state agencies make effective decisions for the maintenance and rehabilitation of their pavements