TY - GEN
T1 - Parametrically robust dynamic speed estimation based control for Doubly Fed Induction Generator
AU - Bhattarai, Rojan
AU - Gurung, Niroj
AU - Ghosh, Sudipta
AU - Kamalasadan, Sukumar
N1 - Publisher Copyright:
© IEEE.
PY - 2017/11/8
Y1 - 2017/11/8
N2 - This paper presents a speed estimation based vector control architecture for Doubly Fed Induction Generator (DFIG). The main advantage of the proposed architecture is that with this methodology the generator can be operated without a speed sensor and position encoder. The method calculates the machine parameters online using a recursive least square (RLS) technique based on identifying the transfer function relating to rotor speed and position error. A minimum variance regulator ensures that the position error is minimum by proper estimation of the speed. For illustration, first, the small signal model of the machine and the regulator design is discussed. Then, a methodology is proposed, in which machine's mutual inductance is estimated online, so that the estimation approach is robust to changes in the machine parameters. Second, the control architecture with the speed estimation technique is discussed. The proposed approach is validated using a real-time simulation platform for a GE 1.5 MW wind turbine and with hardware-in-the-loop experimental set up for a 2kW Doubly Fed Induction Machine (DFIM).
AB - This paper presents a speed estimation based vector control architecture for Doubly Fed Induction Generator (DFIG). The main advantage of the proposed architecture is that with this methodology the generator can be operated without a speed sensor and position encoder. The method calculates the machine parameters online using a recursive least square (RLS) technique based on identifying the transfer function relating to rotor speed and position error. A minimum variance regulator ensures that the position error is minimum by proper estimation of the speed. For illustration, first, the small signal model of the machine and the regulator design is discussed. Then, a methodology is proposed, in which machine's mutual inductance is estimated online, so that the estimation approach is robust to changes in the machine parameters. Second, the control architecture with the speed estimation technique is discussed. The proposed approach is validated using a real-time simulation platform for a GE 1.5 MW wind turbine and with hardware-in-the-loop experimental set up for a 2kW Doubly Fed Induction Machine (DFIM).
KW - Doubly Fed Induction Generator (DFIG)
KW - Dynamic speed estimation
KW - Robust control
KW - Sensorless maximum power point tracking (MPPT)
UR - https://www.scopus.com/pages/publications/85044201106
U2 - 10.1109/IAS.2017.8101779
DO - 10.1109/IAS.2017.8101779
M3 - Conference contribution
AN - SCOPUS:85044201106
T3 - 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017
SP - 1
EP - 8
BT - 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017
Y2 - 1 October 2017 through 5 October 2017
ER -