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This paper presents a novel non-isolated capacitor-assisted quasi-Z-source (CA-QZS) multiport DC-DC boost converter, which operates as a dual-input single-output high gain boost converter. It consists of a bidirectional port for the smooth operation of the energy storage system, and a unidirectional port for the solar/fuel cell to deliver power to the battery and the load. The availability of common ground between input and output ports, high voltage conversion ratio, and continuous current of the unidirectional port are the main features of this novel design, making it suitable for a wider range of applications. A typical switching strategy is followed to regulate the output voltage as well as control input power from the sources. As a battery-incorporated system, the converter's performance is determined by its selection between three operating modes. Therefore, the CA-QZS converter offers a viable interface for the management of battery energy storage systems. A comparative study is performed with other multi-port converters to highlight the advantages. It is observed that the voltage conversion ratio of 10, can be achieved at a low duty ratio of 0.3. To evaluate the effectiveness of the proposed topology, a prototype is developed with a nominal output power rating of 200W and an output voltage of 200 V. [ABSTRACT FROM AUTHOR]

In this paper, a single-switch hybrid dual diode-capacitor (HDDC) boost converter with less stress over all devices for high voltage gain applications is proposed. It combines a voltage boost cell with two back-to-back diode-capacitor cells for providing high voltage gain. The current spikes across the switching devices, occurring due to the diode-capacitor circuit, are effectively truncated by an inductor that is used at the input side. With a single inductor and a single MOSFET, the proposed HDDC converter provides continuous input current, a common ground (C.g) structure and keeps the device voltage stress (Vstress\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$_\textrm{stress}$$\end{document}) and current stress under check. This allows the use of lower-rating devices and is helpful in restricting switching losses, thus improving the comprehensive efficiency of the converter. For integrating RES with micro-grid, the proposed HDDC converter provides all the desirable features. A MATLAB/Simulink model is employed for testing purposes of the proposed HDDC. Additionally, a hardware prototype of the HDDC, with a power rating of 280 W and voltage output of 200 V, is subjected to laboratory testing at a frequency of 33 kHz. The findings from both the simulation and hardware testing are then compared to validate the performance of the proposed HDDC. At near-rated load, the converter operates at an efficiency of around 95.4%. [ABSTRACT FROM AUTHOR]

There is a requirement for a suitable DC-DC converter which can exhibit satisfactory performance in the case of OFF-Grid PV systems. This paper proposes a PV-Battery stand-alone system with an enhanced DC-DC boost converter and an efficient voltage balancing technique. The converter is designed to achieve the desired voltage gain at a lower duty-ratio as compared to the existing converter topology used in conventional stand-alone systems, thereby reducing the voltage stress across the switching devices. For extracting maximum power from the solar panel at every given point of operation, the Incremental Conductance Technique is used. A non-complex voltage balancing technique maintains the DC bus voltage constant with fast dynamic response. The system is highly reliable in case of converter fault and exhibits less initial transient time each time it is put to test. The performance of the proposed system is tested under varying input and varying load conditions with maximum overall system efficiency of around 93%. The stability of the system is investigated using a small signal model of the converter. The proposed stand-alone PV/Battery system is designed to handle a 1 KW load at 180 V DC voltage fed by 1.5 KW solar panel and 100Ah/24 V Li-ion Battery. The dynamic performance of the proposed system is verified through OPAL-RT 4510 platform. [ABSTRACT FROM AUTHOR]

In this study, three different Switched-Capacitor Enhanced boost quasi Z source (SCEq-Zs) DC–DC converters are presented, out of which the converter having a switched capacitor network positioned in series with the enhanced boost q-Zs network (SCEq-Zs-3) provides the common ground path and less output capacitor stress. This converter has a high voltage conversion ratio with low voltage stress on active and passive devices like capacitors, diodes, and the power semiconductor switch in comparison to other converters. This unidirectional hybrid switched-capacitor with a quasi Z source combination provides continuous and ripple-free source current mostly suitable for fuel cell applications. This leads to reduced voltage fluctuations at the input, providing a steady source of power to the converter. The converter is comprehensively analyzed and the operating principle along with waveforms is presented in this paper. A systematic comparison with other DC–DC boost converters is brought to view. A scaled-down 200V/200W prototype for the suggested converter is developed to verify the results of the theoretical study. A maximum efficiency of 95.85 % is measured. To demonstrate the effectiveness of the suggested converter, detailed experimental findings are presented. [ABSTRACT FROM AUTHOR]

Switched Reluctance Motor (SRM) drives have gained a lot of attention in the drive domain because of their built-in benefits, which include ease of use, longevity, and compatibility with high-speed applications. Precise speed control is essential to minimize torque variations and maximize SRM performance. Traditionally, the proportional–integral controller is used for speed control. The nonlinearity and uncertain complexities of SRM have shifted the focus to sophisticated control techniques from conventional. In this work, the Model Predictive Controller (MPC) is applied for SRM speed control, and its performance is compared with that of conventional control methods. MPC improves speed control precision and disturbance rejection by taking into account input restrictions and system dynamics through optimization across a constrained prediction horizon, a strong fault tolerance capability. Furthermore, three different models are developed to control the speed of the SRM using a PI controller, a lookup table-based controller, and an adaptive neuro-fuzzy inference system controller to compare the results of MPC. Through extensive simulations, the study seeks to show how successful MPC is in terms of speed tracking accuracy, transient responsiveness, and stability when compared to standard control techniques, especially under variable operating circumstances and load disturbances. The practicality of the suggested control approach is illustrated by comparing it with other conventional control methods, and its performances are evaluated by using real-time simulator OPAL-RT 4510 under various situations. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a human eye disease in which the eye's retina is damaged in diabetics. Diabetic retinopathy can be diagnosed by manually interpreting retinal fundus images, even though that takes longer to diagnose. Among these, the most challenging task in diagnosing the DR disease is edge detection in retinal fundus images to identify the region of infection and its severity. This paper aims to use the adaptive neural network-based Laplacian of Gaussian (AnLoG) classification algorithm on features extracted from diverse retinal fundus images to improve DR disease diagnostic accuracy and reduce training time. Based on the retinal fundus image in the Messidor dataset, the consequence of the proposed AnLoG classification algorithm for detecting diabetic retinopathy is compared to traditional supervised BPN machine learning algorithms and other contemporary techniques. AnLoG has proved its supremacy in terms of accuracy (97.29%), recall (94.64%), precision (93.13%), and F-Score (93.80%). Simulation results show that the proposed technique performs well compared to the existing approach. [ABSTRACT FROM AUTHOR]