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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.

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