Control of Wind Power Systems for Energy Efficiency and Reliability

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dc.contributor.advisorRotea, Mario A.
dc.contributor.advisorLi, Yaoyu
dc.contributor.advisorFahimi, Babak
dc.creatorXiao, Yan
dc.date.accessioned2019-04-28T23:21:06Z
dc.date.available2019-04-28T23:21:06Z
dc.date.created2018-12
dc.date.issued2018-12
dc.date.submittedDecember 2018
dc.date.updated2019-04-28T23:23:16Z
dc.description.abstractWith decades of development of wind energy technology, the cost of electricity production from wind has decreased significantly. To stimulate higher penetration of wind energy in the electric grid, further research and development are needed to reduce the Levelized Cost of Energy (LCOE) of wind power systems. The LCOE of wind energy may be reduced by: 1) increasing the Annual Energy Production (AEP); 2) reducing the operation and maintenance (O&M) costs; 3) reducing the capital expenditures. On the aeromechanics side, increasing the AEP is achieved by increasing the turbine aeromechanical efficiency (the power coefficient CP of a wind turbine), while reducing the O&M costs could be attained by reducing the aeromechanical forces and moments on the wind turbine rotor and structure. On the electrical side, the AEP is increased by increasing the efficiency of the electricity generation, conversion and transmission, while the capital expenditures may be reduced by lowering the costs of electric components. Conventional wind turbine control schemes are mostly model based, and may rely on wind speed measurements for some cases. However, accurate wind turbine models and wind speed measurements may be difficult and costly to acquire. Extremum Seeking Control (ESC) is a nearly model-free optimization approach suitable for automatically finding the optimal torque gain and blade pitch angle that results in maximized wind turbine aeromechanical efficiency. Previous studies on ESC based wind turbine control are all simulation based. To further evaluate the effectiveness and potential of ESC for wind energy applications, it is necessary to implement the ESC controller on a commercial scale wind turbine, and evaluate the performance through field test. This dissertation presents the results of a field test of an ESC based controller on the NREL’s (National Renewable Energy Laboratory) 600 KW CART3 wind turbine. Also, to reduce the wind turbine aeromechanical loads, while increasing the power coefficient, a multi-objective ESC wind turbine control scheme is proposed. The effectiveness of this multi-objective ESC is evaluated using computer simulations. The second major part of this dissertation is dedicated to investigating two control challenges of the DFIG-DC (DFIG: doubly-fed induction generator; DC: direct current) framework. One of the most critical issues is the torque ripple caused by uncontrollable rectification. In this dissertation, a torque ripple mitigation scheme based on the Multiple Reference Frame (MRF) method is proposed. The effectiveness of the proposed strategy is evaluated through both simulations and experiments. Another control challenge of the DFIG-DC framework is associated with stator frequency control. This dissertation presents computer simulations and experiments indicating that the efficiency of the DFIG-DC system is a unimodal function of the stator frequency. It is also shown that the optimal stator frequency, attaining the highest efficiency, varies with the generator rotor speed. Since it is difficult to obtain an accurate efficiency model of the DFIG-DC system, ESC is implemented to find this optimal frequency in real time. The effectiveness and performance of the proposed ESC based optimal stator frequency control is evaluated with both simulations and experiments.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10735.1/6409
dc.language.isoen
dc.subjectWind power
dc.subjectEnergy consumption
dc.subjectWind turbines—Aerodynamics
dc.subjectReliability (Engineering)
dc.titleControl of Wind Power Systems for Energy Efficiency and Reliability
dc.typeDissertation
dc.type.materialtext
thesis.degree.departmentElectrical Engineering
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.levelDoctoral
thesis.degree.namePHD

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