Through comprehensive study for the power converters that have emerged previously for switched reluctance machine (SRM) drive system, this paper proposes an evaluation approach to determine the suitability of these converters for using in SRM-based electric vehicle (EV) drive system. This evaluation reflects the relative differences of these converters in terms of their cost as well as their capability to offer best technical performance for the drive system of EVs. From technical viewpoint, four factors are considered as follow: the converter efficiency, the converter reliability, the capability of the converter to reduce torque ripple, and to increase power productivity. A benchmark consists of number of converter characteristics is identified. The operation of the converters is analyzed to determine, in quantitative way, their fulfillments to these characteristics. Expressions for the evaluation factors are proposed in terms of the converters’ characteristics. Accordingly, the converters are relatively scored to reflect their differences in cost as well as in technical impact on the SRM drive system. The results provide useful indication for trade-off between gaining some of advantages and losing others for EV applications. In addition, they identify of potentials for optimization based on the cost and the technical performance of different SRM converters.

This paper proposes an Adaptive Neural Fuzzy Inference System (ANFIS) model for diagnosis of combined Inter Turn Short Circuit (ITSC) and Broken Rotor Bar (BRB) faults in a Squirrel Cage Induction Motor (SC-IM). The signal of the stator current is obtained from a really healthy and faulty SC-IM. Experimental tests have been set up using a 1.5 Hp/380 V three-phase SC-IM with different combined ITSC and BRB faults under different loading conditions. Before entering the model, the Discrete Wavelet Transform (DWT) pre-processes the stator current signal. The DWT generates data sets in order to evaluate the proposed technique. ANFIS based on DWT is used successfully to diagnose the most relevant faults very effectively. In addition, ANFIS based on the DWT method has been compared to ANFIS and ANFIS based on an auto-regressive model, finding that the proposed method achieves higher efficiency than the previous one. The proposed ANFIS based on the DWT model classifies entirely different states of combined ITSC and BRB faults with high accuracy.

This paper has adopted the new bio-inspired Manta-Ray Foraging Optimization (MRFO) algorithm for optimal allocation of multiple Distributed Generation (DG) units attached to Radial Distribution Systems (RDSs) in order to reduce the total energy loss of the studied system. The DG units are optimized to work with a unity power factor (UPF) and optimal power factor (OPF) during a 24-h time-varying demand. The MRFO algorithm optimized single, two, and three DG units. The total energy loss and energy-saving during the time-varying demand are calculated and compared with the original case. The MRFO algorithm behavior is compared to the Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO) algorithms regarding energy loss and energy-saving values. The standard 69-bus RDS is used as a test system. Considerable improvements in energy saving, loss reduction, and voltage profile are achieved after installing DG units, mainly when operating with optimal power factors. The MRFO algorithm achieves energy losses of 817.91, 751.08, and 730.25 kWh with 1, 2, and 3 DG units with UPF allocations, respectively. On the other hand, when the DG units are optimized to work with OPF, the MRFO achieves energy losses of 233.24, 142.08, and 106.79 kWh with the same number of DG units, respectively. Furthermore, the MRFO algorithm has efficient behavior compared with the PSO, ASO, and other algorithms for different operations and conditions.

Switched reluctance machines (SRMs) have received increasing attention for their many potential uses, such as for wind power and electric vehicle (EV) drive systems. The Quasi-Z-source Integrated Multiport Converter (QZIMPC) was recently introduced to improve the reliability of the SRM driver through small capacitance values. It is not possible, however, to simultaneously energize and deenergize two SRM phases in QZIMPC. This phenomenon can significantly increase the commutation period which, in turn, degrades the performance of SRM; in addition, this causes high-voltage ripples on the converter’s capacitors. Two switching algorithms are introduced and applied in this paper, and their performance with SRM is investigated in terms of torque ripple and peak phase current. The algorithms are based on prioritizing the control command in the on-going and off-going phases to fulfill the required load torque, as well as to accelerate the commutation process where possible. This is achieved without the interference of high-level controllers, which include speed controllers and/or torque ripple minimization. Through the simulation results, a comparison between the two switching algorithms is presented to determine their potential to improve the SRM drive system’s performance.