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dc.contributor.authorHui, Joanne Chung Yanen
dc.date2014-11-26 14:18:08.825
dc.date.accessioned2014-11-27T21:46:53Z
dc.date.available2014-11-27T21:46:53Z
dc.date.issued2014-11-27
dc.identifier.urihttp://hdl.handle.net/1974/12627
dc.descriptionThesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2014-11-26 14:18:08.825en
dc.description.abstractDue to its high energy generation capability and minimal environmental impact, wind energy is an elegant solution to the growing global energy demand. Though wind energy systems can yield large amounts of energy, frequent atmospheric changes make it difficult to effectively harness the energy in the wind because maximum power extraction occurs at a different operating point for each wind condition. Therefore, maximum power point tracking (MPPT) control algorithms are used in wind energy systems to increase its power extraction efficiency. Basic MPPT algorithms use pre-determined mathematical relationships that represent a specific wind system’s power/torque characteristics for maximum power capture. Due to system aging, the efficiency of these basic algorithms deteriorates over time since it cannot adapt to any system changes. As a result, perturb and observe (P&O) parameter-independent algorithms were developed to actively search for the system’s optimal operating points by perturbing the system and observing the resultant behavior. However, P&O algorithms are susceptible to logical errors when subjected to frequent atmospheric variations and ignore the effects of air density. This thesis proposes a parameter independent intelligent power management controller that consists of a slope-assisted MPPT and a power limit search (PLS) algorithm for small wind energy systems. The proposed MPPT actively searches for MPPs and establishes a maximum power curve (MPC) relationship that characterizes the behavior of the given wind turbine to assist in future MPP searches. The algorithm is robust to changes in wind speed and/or air density conditions by monitoring internal system behavior. The slope assist MPPT uses an internally derived slope parameter to ‘remember and revert’ to the system’s last operating state before the atmospheric change to increase MPP search efficiency. The controller’s PLS is able to minimize the production of surplus energy to minimize the heat dissipation requirements of the energy release mechanism. The PLS seeks the operating points that result in the desired power rather than the maximum power. Finally, the performance of the proposed algorithm was compared to that of the conventional P&O algorithms and the results have shown that the proposed MPPT can extract at least 10% more power.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectWind Energyen
dc.subjectPower Managementen
dc.subjectMaximum Power Point Trackingen
dc.titleAdaptive Slope-Assist Maximum Power Point Tracking and Power Limit Search for Intelligent Power Management of Small Wind Energy Systemsen
dc.typethesisen
dc.description.degreePhDen
dc.contributor.supervisorBakhshai, Alirezaen
dc.contributor.supervisorJain, Praveen K.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.degree.grantorQueen's University at Kingstonen


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