This paper reviews the performances of some newly developed reluctance machines with different winding configurations,excitation methods,stator and rotor structures,and slot/pole number combinations.Both the double la...This paper reviews the performances of some newly developed reluctance machines with different winding configurations,excitation methods,stator and rotor structures,and slot/pole number combinations.Both the double layer conventional(DLC-),double layer mutually-coupled(DLMC),single layer conventional(SLC-),and single layer mutually-coupled(SLMC-),as well as fully-pitched(FP)winding configurations have been considered for both rectangular wave and sinewave excitations.Different conduction angles such as unipolar120°elec.,unipolar/bipolar180°elec.,bipolar240°elec.and bipolar360°elec.have been adopted and the most appropriate conduction angles have been obtained for the SRMs with different winding configurations.In addition,with appropriate conduction angles,the 12-slot/14-pole SRMs with modular stator structure is found to produce similar average torque,but lower torque ripple and iron loss when compared to non-modular 12-slot/8-pole SRMs.With sinewave excitation,the doubly salient synchronous reluctance machines with the DLMC winding can produce the highest average torque at high currents and achieve the highest peak efficiency as well.In order to compare with the conventional synchronous reluctance machines(SynRMs)having flux barriers inside the rotor,the appropriate rotor topologies to obtain the maximum average torque have been investigated for different winding configurations and slot/pole number combinations.Furthermore,some prototypes have been built with different winding configurations,stator structures,and slot/pole combinations to validate the predictions.展开更多
This paper addresses the bearingless motor with a single set of multiphase windings. The interaction between M and M±1 pole-pair magnetic fields produces radial force. Based on this principle,a bearingless machin...This paper addresses the bearingless motor with a single set of multiphase windings. The interaction between M and M±1 pole-pair magnetic fields produces radial force. Based on this principle,a bearingless machine is obtained. Conventional bearingless machine has dual windings,levitation windings and torque windings,which produce the two magnetic fields. In the proposed bearingless motor,the two needed magnetic fields are produced by feeding two groups of currents to a single set of multiphase windings. Taking a 5-phase induction motor as example,the inductance matrices,considering air gap eccentricity,are calculated with the modified winding function method. The radial force analytical model is deduced by virtual displacement,and its results are validated by FEA. The mathematical model of the new bearingless machine is set up,and the simulation results verified the feasibility of this novel bearingless motor.展开更多
Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions.Although there are several intelligent techniques in the litera...Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions.Although there are several intelligent techniques in the literature for wind speed prediction,their accuracies are not yet very reliable.Therefore,in this paper,a new hybrid intelligent technique named the deep mixed kernel random vector functional-link network auto-encoder(AE)is proposed for wind speed prediction.The proposed method eliminates manual tuning of hidden nodes with random weights and biases,providing prediction model generalization and representation learning.This reduces reconstruction error due to the exact inversion of the kernel matrix,unlike the pseudo-inverse in a random vector functional-link network,and short-ens the execution time.Furthermore,the presence of a direct link from the input to the output reduces the complexity of the prediction model and improves the prediction accuracy.The kernel parameters and coefficients of the mixed kernel system are optimized using a new chaotic sine–cosine Levy flight optimization technique.The lowest errors in terms of mean absolute error(0.4139),mean absolute percentage error(4.0081),root mean square error(0.4843),standard deviation error(1.1431)and index of agreement(0.9733)prove the efficiency of the proposed model in comparison with other deep learning models such as deep AEs,deep kernel extreme learning ma-chine AEs,deep kernel random vector functional-link network AEs,benchmark models such as least square support vector machine,autoregressive integrated moving average,extreme learning machines and their hybrid models along with different state-of-the-art methods.展开更多
文摘This paper reviews the performances of some newly developed reluctance machines with different winding configurations,excitation methods,stator and rotor structures,and slot/pole number combinations.Both the double layer conventional(DLC-),double layer mutually-coupled(DLMC),single layer conventional(SLC-),and single layer mutually-coupled(SLMC-),as well as fully-pitched(FP)winding configurations have been considered for both rectangular wave and sinewave excitations.Different conduction angles such as unipolar120°elec.,unipolar/bipolar180°elec.,bipolar240°elec.and bipolar360°elec.have been adopted and the most appropriate conduction angles have been obtained for the SRMs with different winding configurations.In addition,with appropriate conduction angles,the 12-slot/14-pole SRMs with modular stator structure is found to produce similar average torque,but lower torque ripple and iron loss when compared to non-modular 12-slot/8-pole SRMs.With sinewave excitation,the doubly salient synchronous reluctance machines with the DLMC winding can produce the highest average torque at high currents and achieve the highest peak efficiency as well.In order to compare with the conventional synchronous reluctance machines(SynRMs)having flux barriers inside the rotor,the appropriate rotor topologies to obtain the maximum average torque have been investigated for different winding configurations and slot/pole number combinations.Furthermore,some prototypes have been built with different winding configurations,stator structures,and slot/pole combinations to validate the predictions.
基金Project (No. 50677060) supported by the National Natural ScienceFoundation of China
文摘This paper addresses the bearingless motor with a single set of multiphase windings. The interaction between M and M±1 pole-pair magnetic fields produces radial force. Based on this principle,a bearingless machine is obtained. Conventional bearingless machine has dual windings,levitation windings and torque windings,which produce the two magnetic fields. In the proposed bearingless motor,the two needed magnetic fields are produced by feeding two groups of currents to a single set of multiphase windings. Taking a 5-phase induction motor as example,the inductance matrices,considering air gap eccentricity,are calculated with the modified winding function method. The radial force analytical model is deduced by virtual displacement,and its results are validated by FEA. The mathematical model of the new bearingless machine is set up,and the simulation results verified the feasibility of this novel bearingless motor.
文摘Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions.Although there are several intelligent techniques in the literature for wind speed prediction,their accuracies are not yet very reliable.Therefore,in this paper,a new hybrid intelligent technique named the deep mixed kernel random vector functional-link network auto-encoder(AE)is proposed for wind speed prediction.The proposed method eliminates manual tuning of hidden nodes with random weights and biases,providing prediction model generalization and representation learning.This reduces reconstruction error due to the exact inversion of the kernel matrix,unlike the pseudo-inverse in a random vector functional-link network,and short-ens the execution time.Furthermore,the presence of a direct link from the input to the output reduces the complexity of the prediction model and improves the prediction accuracy.The kernel parameters and coefficients of the mixed kernel system are optimized using a new chaotic sine–cosine Levy flight optimization technique.The lowest errors in terms of mean absolute error(0.4139),mean absolute percentage error(4.0081),root mean square error(0.4843),standard deviation error(1.1431)and index of agreement(0.9733)prove the efficiency of the proposed model in comparison with other deep learning models such as deep AEs,deep kernel extreme learning ma-chine AEs,deep kernel random vector functional-link network AEs,benchmark models such as least square support vector machine,autoregressive integrated moving average,extreme learning machines and their hybrid models along with different state-of-the-art methods.
