Doubly Fed Induction Generator Control Using Artificial Neural Network for Wind Generation System

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dc.contributor.advisorPeng, Wei
dc.contributor.authorSilva de Siqueira, Luanna Maria
dc.contributor.committeememberMayorga, Rene
dc.contributor.committeememberDai, Liming
dc.contributor.committeememberWu, Peng
dc.contributor.externalexaminerWang, Zhanle
dc.date.accessioned2022-08-05T16:37:46Z
dc.date.available2022-08-05T16:37:46Z
dc.date.issued2021-08
dc.descriptionA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Industrial Systems Engineering, University of Regina. xvi, 105 p.en_US
dc.description.abstractDue to the electricity demand and environmental concerns, alternative clean forms of energy have been widely researched in recent years, in which wind energy is in evidence. To convert the mechanical energy generated by the wind turbines, the wind generation system needs generators. The doubly fed induction generator is the most commonly used due to its high energy transfer efficiency, reliability, and low maintenance cost. The control of DFIG parameters is crucial to the dynamic performance of the wind generation system. This thesis aims to develop an artificial neural network (ANN) controller for the doubly fed induction generator in wind generation system. It was proposed a data acquisition method using the system outcome with the conventional PI controller in order to train and test the ANN controller. The ANN controller was then designed and implemented in the system to control the machine speed, DC link voltage, and dq rotor and stator currents. In order to simulate the complete wind generation system, the models of DFIG, wind turbine, and converter were developed. The simulation results via Matlab Simulink indicate that it is possible to control the DFIG using an ANN controller. The results also show that the proposed ANN controller outperformed the conventional PI controller performances during the transient response, decreasing the overshoot for both machine speed and DC link voltage control. Outcomes found in this thesis combined with future works could potentially incentive the use of ANN for control purposes in wind generation systems. Furthermore, this research provides a guide on how to collect data for the training and testing of ANN controller for this kind of system.en_US
dc.description.authorstatusStudenten
dc.description.peerreviewyesen
dc.identifier.tcnumberTC-SRU-14984
dc.identifier.thesisurlhttps://ourspace.uregina.ca/bitstream/handle/10294/14984/Silva_de_Siqueira_Luanna_MASC_ISE_Spring2022.pdf
dc.identifier.urihttps://hdl.handle.net/10294/14984
dc.language.isoenen_US
dc.publisherFaculty of Graduate Studies and Research, University of Reginaen_US
dc.titleDoubly Fed Induction Generator Control Using Artificial Neural Network for Wind Generation Systemen_US
dc.typeThesisen_US
thesis.degree.departmentFaculty of Engineering and Applied Scienceen_US
thesis.degree.disciplineEngineering - Industrial Systemsen_US
thesis.degree.grantorUniversity of Reginaen
thesis.degree.levelMaster'sen
thesis.degree.nameMaster of Applied Science (MASc)en_US
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