Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

The rotor permanent magnet flux-switching (RPM-FS) machine is a promising candidate for electric vehicle (EV) and hybrid electric vehicle (HEV) applications. In this paper, we propose the magnetic flux barrier design to improve the torque capability of the RPM-FS machine. The response surface optimization method was used to design and optimize the topology of flux barriers. The 2D finite element analysis shows that the proposed RPM-FS machine has a higher electromotive force than the conventional structure, with only a slight increase in cogging torque. Notably, an insertion of flux barriers could yield a reduction of magnetic flux leakage, an improvement of magnetic saturation capability, and an enhancement of working harmonics of the air-gap flux density. As a result, a significant improvement in torque capability, eddy current losses, and efficiency was obtained. Hence, the RPM-FS machine proposed in this work is capable of being used in EV and HEV applications.

Power quality disturbance (PQD) is an influential situation that significantly declines the reliability of electrical distribution systems. Therefore, PQD classification is an important process for preventing system reliability degradation. This paper introduces a novel algorithm called “adaptive salp swarm algorithm (SSA)” as an optimal feature selection algorithm for PQD classification. Feature extraction and classifier of the proposed classification system were based on the discrete wavelet and the probabilistic neural network, respectively. The classification was focused on the 13 types of power quality signals. The optimal number of selected features for the proposed classification system was firstly determined. Then, it demonstrated that the optimally selected features resulted in the highest classification accuracy of 98.77%. High performance of the proposed classification system in the noisy environment, as well as based on the real dataset was also verified. Furthermore, the proposed SSA indicates a very high convergence rate compared to other well-known algorithms. A comparison of the proposed classification system’s performance to existing works was also carried out, revealing that the proposed system’s accuracy is on a high-range scale. Hence, the adaptive SSA becomes another efficient optimal feature selection algorithm for PQD classification.

Flux-switching permanent magnet (FSPM) machines have attracted wide attention in many rotating applications that require high-power density. In this research, we propose for the first time a novel six-phase FSPM generator with a stator featuring a V-shaped flux-focusing magnet arrangement. The design is targeted for low-speed wind power generation. To achieve the design objectives as a wind generator, the highly comprehensive structural parameters, including the number of rotor poles, split ratio, and rotor pole width, are designed and optimized using 2D finite-element analysis. From findings, the optimal stator/rotor pole combination is discovered to be 12/19 for the considered power and speed requirements. When compared to the initial structure, the optimized structure of the V-shaped FSPM generator is found to produce a significant improvement in EMF, cogging torque, electromagnetic torque, power, and efficiency. The power-generating performance of the proposed FSPM generator is found to be outstanding when compared to the radial-flux PM generators described in the literature. Therefore, the proposed V-shaped FSPM generator is capable of being used for low-speed wind power generation. The machine configuration adjustment approach presented in this work can also be utilized for the design of permanent magnet wind generators.

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.