Open AccessModelling of brushless doubly fed reluctance machines based on reluctance network model
Author(s) -
Kenmoe Fankem Eric Duckler,
Kendeg Onla Clement Junior,
Xiaoyan Wang,
Dountio Tchioffo Alix,
Effa Joseph Yves
Publication year - 2021
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 97
eISSN - 1751-8679
pISSN - 1751-8660
DOI - 10.1049/elp2.12106
Subject(s) - magnetic reluctance , finite element method , stator , electromagnetic coil , computation , rotor (electric) , control theory (sociology) , computer science , reluctance motor , switched reluctance motor , computational model , control engineering , magnetic flux , engineering , magnet , magnetic field , algorithm , mechanical engineering , physics , control (management) , artificial intelligence , electrical engineering , structural engineering , quantum mechanics
The modelling and analysis of Brushless Doubly Fed Reluctance Machines (BDFRMs), taking into account magnetic saturation and rotor movement, by conventional modelling techniques are very difficult, if not impossible, because the two stator windings have different number of poles leading to a complex flux pattern. To overcome this drawback, Finite Element Analysis (FEA) is generally used for modelling and analysing BDFRMs. But it requires a considerable computational time compared with semi‐analytical methods. This article, therefore, steps forward by proposing a new approach to dynamical modelling of BDFRM based on the Reluctance Network Method (RNM), which can enable accurate calculation of the electromagnetic parameters and performances of BDFRMs. Indeed, the reluctance network method offers an interesting compromise between precision and computation time compared to finite element analysis. To validate the proposed model, simulations are carried out and comparison are made with FEA. It is observed that the greatest error between the values of the proposed model and those from FEA is close to 1%. The accuracy in the calculation of electromagnetic parameters, as well as the computational time leads us to the conclusion that the proposed model could be suitable for optimisation and control purposes.