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.

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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.

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