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Brushless DC (BLDC) motor drives are extensively preferred in the automotive industry for their high torque‐to‐weight ratio. In conventional BLDC drives, the torque ripples are high due to 120° conduction mode operation and nonideal back EMF. To minimize the torque ripple in BLDC drives, a flux trajectory is employed based on the extension Park transformation d‐q axis frame. This article introduces a control scheme for BLDC drives that integrate maximum torque per ampere (MTPA) with pseudo–field‐oriented control (FOC). This approach gets an optimal reference d‐axis current (Ids∗) that optimize the current magnitude Is2) and minimize copper loss. Furthermore, the 180o conduction mode is used to minimize the torque ripple and provide high dynamic performance of drives. The efficacy of the proposed MTPA‐based pseudo‐FOC strategy is experimentally validated by employing a 0.75‐kW BLDC motor drive using real‐time interface controller dSPACE 1202 and Danfoss VLT 302 converter. [ABSTRACT FROM AUTHOR]

DC microgrids are getting more attention because majority of the renewable energy sources generate DC output voltage and also modern gadgets require DC voltage for its operation. In this work, high gain SEPIC (HGSC) topology is derived from switched inductor voltage boosting cell (SIVBC). The HGSC converter provides continuous source current due to SIVBC and high conversion ratio and achieves maximum efficiency of 97.88% when compared with the existing SEPIC topology. The operating modes, conversion ratio expression, power loss distribution, voltage drop, current stress of the semiconductor devices, and efficiency are also analysed. In DC microgrids, the HGSC intends to track the peak power from solar PV array. An incremental conductance algorithm is employed to track the peak power of the solar PV modules. The power flow in the microgrid system is analysed by employing synchronous reference frame theory‐based current controller. In order to validate the theoretical concepts of the HGSC converter, the hardware model is developed for the load rating of 1,000 W/380 V output voltage. [ABSTRACT FROM AUTHOR]

The manuscript proposes the design and implementation of a photovoltaic-assisted dynamic voltage restorer with fuzzy-logic control (FLC)-based Improved Second-order Generalized Integrator (I-SOGI) scheme to improve power quality (PQ) in the power system by using an enhanced approach. The proposed method utilizes Firebug Swarm Optimization (FSO) which is motivated by the reproductive swarming behavior of Firebugs. The swarming behavior of FSO is enhanced by using two operators called mutation and crossover. Hence, it is termed as Enhanced FSO (En-FSO) method. Power quality issues considered include harmonic distortions and voltage sag/swell. The Z-source inverter (ZSI) by compensating the dynamic voltage restorer (DVR's), fed voltage into the grid through a series transformer. Long-term balanced voltage sags, swells, unbalanced sags, swells, and interruptions of voltage are all offset by the ZSI-DVR. Using the suggested control method, the total harmonic distortion (THD) of load voltage is kept below 5%. The FLC-based I-SOGI control method for generating the reference signals for voltage compensation enhances a considerable parameter tweaking. The entire analysis demonstrates that the proposed FLC-based I-SOGI control technique effectively mitigates voltage sag, swell, and harmonics while supplying solar photovoltaic (SPV) power to the grid. The simulation and experimental verification of the proposed control algorithm successfully maintains pure sinusoidal load voltage in a variety of test conditions. The results are compared with existing methods to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a new double mover configuration of a linear switched reluctance motor (LSRM). The proposed design is established for optimization of the motional forces and to ensure a high-grade electromechanical energy conversion process. The major drawback of the traditional linear machine is that its force densities within and throughout its area is produced in its radial direction of the yoke and does not contribute to its motion or twisting force of the rotor. If these normal forces happen to be in the direction of motion, a larger motional force profile for SRM is yielded. Based on these guidelines, a new LSRM is developed. In order to compare the energy conversion efficiency of LSRM with that of the conventional SRM, a finite element model is constructed. The proposed system is simulated using the FEM software and tested under four conditions. Further, an experimental prototype of the proposed machine is also developed and tested in the laboratory. The results obtained from the prototype indicate that the proposed geometry offers superior performance in terms of high-power density and higher percentage of the national forces. [ABSTRACT FROM AUTHOR]

The accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid. The proposed system must ensure dependable, safeguarded, and stable operating conditions amidst diverse power quality issues. This paper presents an approach to classifying power quality disturbances using a deep learning model that synergizes deep convolutional neural networks (DCNN) and Bidirectional Long Short‐Term Memory (BiLSTM). This amalgamation effectively extracts and classifies disturbance signals in real time, grounded on noise levels. The initial feature extraction from the signal is accomplished through a time‐frequency matrix. Subsequently, secondary extraction employs the BiLSTM layer to intricately and significantly classify disturbances in the power signal. This aids in transforming high‐dimensional matrices into a reduced set for enhanced performance. The detailed classification is facilitated by the softmax layer. The simulation results support the power quality evaluations under varied constraints and underscore the substantial classification of power quality disturbances through the DCNN‐BiLSTM algorithm, in comparison to alternative classification algorithms in terms of computational speed and accuracy. [ABSTRACT FROM AUTHOR]