Stator Flux Based Model Reference Adaptive Observers for Sensorless Vector Control and Direct Voltage Control of Doubly-Fed Induction Generator
A position sensorless operation of a doubly-fed induction generator (DFIG) is desired from considerations of cost, maintenance, cabling, and reliability. This article proposes two stator flux based model reference adaptive observers (SF-MRAOs) for the estimation of the rotor speed and position in a...
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Veröffentlicht in: | IEEE transactions on industry applications 2020-07, Vol.56 (4), p.3776-3789 |
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Sprache: | eng |
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Zusammenfassung: | A position sensorless operation of a doubly-fed induction generator (DFIG) is desired from considerations of cost, maintenance, cabling, and reliability. This article proposes two stator flux based model reference adaptive observers (SF-MRAOs) for the estimation of the rotor speed and position in a stand-alone DFIG. The cross product of the reference and estimated stator flux linkage vectors is fed as controller input in the existing method, thereby, making the observer model nonlinear. The first proposed method, which is based on notch filtering, offers a performance comparable to that of the existing method while reducing the number of voltage and current sensors required. The second proposed method linearizes the error input to the controller, making the controller design straight forward, and also leading to superior performance than the existing methods. The linearized SF-MRAO is shown to work successfully with good dynamic performance in vector control of a stand-alone DFIG. This article also shows that the proposed linearized SF-MRAO can be employed for obtaining the rotor current angle in direct voltage control of a stand-alone DFIG. Simulation and experimental results are presented from a laboratory prototype of a 10-hp DFIG coupled to a squirrel-cage induction motor. The sensitivity of the performance of the proposed linearized SF-MRAO to variations in machine parameters is also studied theoretically and experimentally. |
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ISSN: | 0093-9994 1939-9367 |
DOI: | 10.1109/TIA.2020.2988426 |