With the wide applications of dual-rotor flux-modulation machines for the growing wind power generations, research activities for the control of dual-rotor flux-modulation machines are intensified in recent years. Most of the existing control schemes are based on indirect measurements of the d-axis inductance, the q-axis inductance and the stator resistance to achieve high torque density and low torque ripple for the dual-rotor flux-modulation machines. However, conventional measurements of the d-axis inductance, the q-axis inductance and the stator resistance may suffer from (i) low accuracy and (ii) additional sensor costs. To this end, an adaptive differential evolution algorithm is proposed to identify the machine parameters by considering the magnetic saturation and cross-coupling issue at low rotational speed of dual-rotor flux-modulation machines. Finite element analysis is adopted in simulation to preliminarily monitor the actual machine parameter values based on the length and cross section area of the conductor and inductance matrix computation. Both simulation and experimental results reveal that the adopted adaptive differential evolution algorithm can identify the three parameters more steadily and accurately than the conventional genetic algorithm.
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment