Open AccessModel reference adaptive system based sensorless speed estimation of brushless doubly‐fed reluctance generator for wind power application
Author(s) -
Kumar Mukesh,
Das Sukanta
Publication year - 2018
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2018.5344
Subject(s) - mras , control theory (sociology) , magnetic reluctance , robustness (evolution) , estimator , computer science , wind power , wind speed , ac power , induction generator , electric power system , matlab , control engineering , power (physics) , engineering , vector control , magnet , mathematics , voltage , induction motor , artificial intelligence , chemistry , operating system , biochemistry , control (management) , quantum mechanics , statistics , physics , meteorology , electrical engineering , gene , mechanical engineering
Brushless doubly‐fed reluctance generator (BDFRG) is drawing a growing attention of research over the traditional doubly‐fed induction generator in variable speed application like wind power generation nowadays, due to its brushless structure. In this work, the applicability of different model reference adaptive system (MRAS) based sensorless speed estimation techniques for BDFRG in wind power application is explored. The MRAS estimators developed, in this context, are based on active power, reactive power and fictitious quantity. All these schemes are implemented under primary field orientation using machine secondary winding quantities as the functional variables. Matlab/Simulink study and hardware test results under variable wind speed (between cut‐in and rated speed) are presented for the aforementioned speed estimation techniques. Furthermore, to ensure the robustness of the drive system, stability study using Popov's criteria for hyperstability and sensitivity analyses is presented.