This article discusses the speed-sensorless vector-controlled SRM-drive. The use of position/speed sensors have few concerns related to it in terms of cost, reliability, and compactness, a few to mention. Therefore, it is necessary to have some estimation strategy which estimates the speed/position. Since the control of the machine is done in dq0-reference frame, so by utilising these dq0-variables a novel speed/position estimation technique i.e. 'P n-MRAS (Net-input-Power-based Model-Reference-Adaptive-System) speed-estimator' is developed for the vector-controlled SRM-drive. The speed-estimator implemented is simple, no look-up table is required and no external circuitry is required for injection of the diagnostic signal. Also, the estimator is free from differentiator and integrator terms. The proposed speed-estimator is stable in all the four quadrants of operation which is proved by performing the stability analysis. The estimator is dependent on two machine parameters (stator-resistance and amplitude of AC-component of self-inductance). The estimator is made robust to changes in stator-resistance by estimating the resistance online by proposed V 0-MRAS resistance-estimator. The drive has been extensively simulated in MATLAB/SIMULINK and results are also verified experimentally through a dSPACE-1104-based laboratory prototype.

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This study aims to improve the quality of operation parameters of the stand-alone hybrid microgrids (HMGs). The proposed module for the AC microgrid (ACMG) is a modulated-unified power quality conditioner (M-UPQC). Furthermore, the suggested component for the DC microgrid (DCMG) is a switched-inductor boost converter module (S-IBCM). The M-UPQC control method is based on inverter modules and the system resonant features. The aim of S-IBCM applied is to improve DC microgrid (DCMG) efficiency. In this paper, the research challenge consists of two sections: first, adjusting the control parameters of M-UPQC by the black hole optimization (BHO), Harris hawk optimization (HHO), and grasshopper optimization algorithm (GOA) techniques, respectively; second, presenting a new design of the BC module called S-IBCM to increase DCMG efficiency. The programmed multi-objective functions (MOFs) for M-UPQC contain the harmonic parameters. Finally, according to output results, the performance conditions for ACMG and DCMG divisions achieve significantly improved by the proposed modules adopted. Furthermore, the performance of the M-UPQC operating under static and dynamic disturbances is tested through an experimental setup.

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