Multi-Phase Permanent Magnet Generator with Halbach Array for Direct Driven Wind Turbine: A Hybrid Technique

ABSTRACT

In this paper, a hybrid approach is proposed for Multiphase Halbach Array Permanent Magnet Generator (MHAPMG) in the direct driven wind turbine. The proposed approach is the consolidation of Archimedes optimization algorithm (AOA) and Radial Basis Function Neural Network (RBFNN) named AOA-RBFNN approach. The proposed approach achieves higher efficiency at low speed and it is utilized to improve the lesser Cogging torque with torque ripple and fault tolerance. The proposed generator is compared with Multiphase Surface Mount conventional Permanent Magnet Generator (MSMPMG) for its improved outputs. The proposed generator is used to chose the turbine design and to estimate the output of 1000 W with a speed of 440 RPM and the wind speed of 9 m/s. The proposed approach optimizes the electromagnetic performance of Cogging and back electromotive force. The performance of the proposed Halbach array is analyzed with the regular sequence with multiphase topology based on the induced electromotive force, Cogging Torque, Air gap Flux density, and Harmonic Components. The proposed generator is investigated by finite element analysis (FEA). By then, the performance of the proposed system is activated on MATLAB/Simulink platform. The efficiency of WT using proposed technique, HS, TS, and PSO becomes 98%, 80%, 77%, and 82%, respectively.

KEYWORDS:

• Permanent magnet generator
• multiphase technology
• Halbach
• cogging
• finite element analysis
• wind turbine

Nomenclature

$\theta$	=	Rotor angular position
$\beta$	=	Position along Rotor Circumference
L	=	Stack Length
$h_{sb}$	=	Stator Back Iron Height
${\mu }_r$	=	Relative permeability of Magnet
S	=	No of Stator Slots
P	=	No of Rotor Poles
$D_{SO}$	=	Stator Outer Diameter
$B_{g0}$	=	No load Air Gap Flux Density
$B_t$	=	Flux Density of Stator Tooth
$k_f$	=	Filling Factor
$S_{ca}$	=	Stator Conductor Area
MHAPMG	=	Multiphase Halbach Array Permanent Magnet Generator
AOA	=	Archimedes optimization algorithm
FEA	=	Finite Element Analysis
$\alpha$	=	Position along Stator Circumference
$h_g$	=	Air Gap Length
$h_m$	=	Height of the Magnet
$W_s$	=	Stator Tooth Width
${\mu }_0$	=	Permeability of Air
$B_r$	=	Magnetic remanence
$D_{SI}$	=	Stator Inner Diameter
$B_g$	=	Air Gap Flux Density
$B_y$	=	Flux Density of Stator Yoke
$k_s$	=	Stacking Factor
$H_{nth}$	=	nth Harmonic order
$N_T$	=	Number of Turns
RBFNN	=	Radial Basis Function Neural Network
MSMPMG	=	Multiphase Surface Mount conventional Permanent Magnet Generator
CLF	=	control Lyapunov function

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing is not appropriate to this article as no novel data were generated or analyzed in this study.