Surface‐mounted permanent magnet (SPM) machines are highly capable for wind power generation due to their high output power, simple structural design, and effective thermal management. However, their heavy reliance on rare‐earth magnets significantly increases the cost per unit power, limiting large‐scale deployment. To address these challenges, this study proposes a cost‐effective SPM wind generator featuring a hybrid NdFeB‐ferrite Halbach PM array and flux barriers. The structural parameters are optimized using a genetic algorithm (GA) with sensitivity‐based constraints. Finite element analysis (FEA) demonstrates that the proposed design reduces material cost by 18.2% and weight by 14% compared to conventional SPM generators, while delivering higher output power. Furthermore, cogging torque and torque ripple are reduced by 54% and 79%, respectively, enhancing its suitability for low‐speed wind applications and ensuring smoother operation. Improved loss characteristics and efficiency further highlight the generator’s superior performance. These results position the proposed design as a cost‐effective and practical solution for wind power generation.

This paper introduces a cost-effective hybrid-magnet multi-flux barrier interior permanent magnet motor for electric vehicle applications. The design combines partial ferrite PM substitution with a multi-flux barrier rotor to compensate for the magnetic flux reduction that occurs when reducing rare-earth permanent magnet (REPM) usage, while keeping the total magnet volume unchanged. Rotor design variables are optimized using a multi-objective genetic algorithm with sensitivity-based constraints. Compared with the benchmark design, the optimized motor maintains back-electromotive force and electromagnetic torque levels, with a slight reduction in torque ripple. An approximately 11% reduction in REPM usage, together with a 7.14% reduction in total material cost, yields a corresponding 7.43% improvement in torque per cost performance, reflecting the cost advantage enabled by the hybrid-magnet multi-flux barrier configuration. Furthermore, efficiency map evaluation shows that the proposed motor sustains efficiency comparable to the benchmark. Mechanical stress analysis confirms that rotor stress remains below the elastic limit, and demagnetization analysis verifies the magnetic robustness of both REPM and ferrite PM segments. The findings highlight the feasibility of combining hybrid-magnet and multi-flux barrier configurations for further advancements in cost-effective IPM motors.

Surface-mounted permanent magnet synchronous generators (SPMSGs) are well suited for wind power applications mainly because of their high power density, low cogging torque, and effective thermal management. This study proposes an eccentric Halbach PM array pole shape to enhance the power generation capability of SPMSGs specifically designed for low-speed wind power generation. The topology of the proposed eccentric Halbach PM arrangement is optimized using a genetic algorithm. Two-dimensional finite element simulations indicate that the eccentric Halbach configuration significantly improves flux focusing and magnetic field distribution. Compared to the conventional design, the proposed structure exhibits a substantial increase in electromotive force with reduced total harmonic distortion. Cogging torque is reduced by 48.6%, supporting improved starting and low-speed operation. Under on-load, the proposed design delivers higher average torque with reduced ripple, contributing to smoother operation. At a rated speed, the output power increases by 25%, with consistently higher power generation capability across a wide range of load conditions. Additionally, the proposed generator achieves higher efficiency across all operating speeds. These findings confirm the effectiveness of the eccentric Halbach array configuration in improving the power generation capability of SPMSG, thereby reinforcing its applicability to low-speed wind energy systems aligned with long-term sustainability objectives.

Interior permanent magnet (IPM) synchronous motors have gained widespread adoption in electric vehicles (EVs) owing to their durable rotor configurations, expansive operational speed range, and superior efficiency. Nonetheless, typical IPM motor designs frequently exhibit high torque ripple and constrained torque density. To address these issues, a torque enhancement method is introduced by applying both filleting and notching techniques to the stator core. These techniques help reshape the magnetic field directly at the stator, allowing for more precise control of torque production and torque ripple reduction while keeping the rotor structure unchanged. Design variables of the stator in a 12-slot/8-pole fractional-slot V-shaped IPM motor are optimized using a multi-objective genetic algorithm based on a sensitivity constraint for unidirectional operation. The electromagnetic performance of the motor is analyzed through 2D finite element simulations for both no-load and loaded scenarios. The proposed motor increases average torque by 2.45% and significantly reduces torque ripple by 47.73% compared to the conventional motor. These reflect a significant advancement in torque capability. Furthermore, the efficiency of the proposed motor reaches 93.8%. The findings suggest the potential of the proposed filleting and notching techniques for torque capability improvement in EV applications.

Accurate fault diagnosis of electrical machines is essential for operational reliability and safety. Handcrafted features remain attractive in practice because of their interpretability and computational efficiency. However, this traditional approach relies on individual feature vectors and is limited in preserving inherent temporal dynamics. To address this issue, a label-consistent input structuring based on the second stage sliding window (SSSW) method is proposed. This approach retains the benefits of handcrafted features and arranges feature vectors into temporally coherent sequences while ensuring label consistency. A hyperparameter optimization scheme is incorporated with a long short-term memory classifier to reduce manual tuning. Performance evaluations demonstrate the robustness of the proposed SSSW method across diverse operating conditions and input signal configurations. These include single-phase currents, multi-phase currents, vibration, and fused current-vibration signals. Notably, the proposed approach achieves classification accuracy of up to 100% and demonstrates stable learning behavior. Experimental verification on a laboratory scale permanent magnet synchronous motor testbed further validates the proposed method under realistic measurement noise and interference conditions. Finally, combining handcrafted features with the proposed SSSW input structuring method provides a practical, scalable solution for reliable electrical machine fault diagnosis.