IJEER Vol no. 12, Issue 3


Design Wien Bridge Oscillator for VLF to VHF Using Practical Op – Amp

An electronic oscillator is generally a major part of electrical, electronic and communications circuits and systems and it is can be divided into linear and nonlinear families. Wien Bridge is a type of RC phase shift oscillators mostly used for around 1MHz and its design adopts positive feedback technology. In this research, novel look the reasons for the inability to achieve high frequencies was understanding and the ambiguity was removing from the determinants of obtaining a high frequency signal for this type of oscillators, also, new results were obtained with a unique presentation. The output formula for the oscillation resonant frequency was deriving based on the oscillator’s theory.

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A New Electric EEL Foraging Optimization Technique for Multi-Objective PV Unit Allocation in the Context of PHEV Charging Demand

The existing electric distribution system is under tremendous stress due to reasons like power efficacy & voltage profile, load growth, radial structure etc. Additionally, electric load demand due to PHEVs worsens the existing distribution system performance. Planning of DGs in distribution system is one of the potential solutions for improving existing distribution system performance without changing its infrastructure. Therefore, the primary objective of this research is to determine the optimal way to allocate photovoltaic (PV) based distributed generators (DGs) inside radial distribution networks while taking into account the load demands of both conventional and PHEVs. In the study, three key technical metrics of the distribution network are improved via optimal planning of PV units: maximizing the voltage stability index, minimizing total voltage variation, and minimizing energy loss. Mathematically, weighted objective function is formulated for dealing the above-citied technical metrics.

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Advanced Energy Management System for Hybrid AC/DC Microgrids with Electric Vehicles Using Hybridized Solution

The rapid expansion of the automotive sector promising this technology is going forward and deeply ingrained in human society. Without a doubt, the unpredictable and erratic charging demands of these devices would have an impact on the power grid's scheduling and optimal performance, which may be seen as a new issue. This research introduces an efficient energy management system for hybrid renewable energy in AC/DC microgrids, including electric vehicle (EV) renewable microgrids, utilizing sources such as solar and wind energy. These systems offer promising solutions for enhancing security, reliability, and efficiency in power systems, with the added benefit of reducing greenhouse gas emissions. The proposed optimization approach utilizes Honey Badger Algorithm (HBA) Golden Jackal Optimization (GJO) called Advanced HBA (AHBA) for voltage and power control in hybrid AC/DC microgrids with EVs.

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Speed Control of Sensorless Induction motor based on Grey Wolf Optimizer Fractional Order Controller using MRAS based Speed Estimation

Traditional induction motor control methods typically require feedback from sensors like encoders or resolvers to determine the motor's rotor position and speed accurately. The speed control of a sensorless induction motor is critical, so this study provides a novel method that combines the Model Reference Adaptive System (MRAS) for speed estimate with the Fractional Order PID controller for speed control. This controller's parameters are optimized using the Grey Wolf Optimizer Algorithm. After being implemented in the MATLAB/Simulink environment, the suggested approach's performance is compared to that of a standard PI controller. From the findings, it is clear that the proposed method effectively maintaining the specified speed as compared with PI controller. The proposed controller performance is also validated through experimental results.

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A New Quazi Z-Source Seven-Level inverter for Photovoltaic Applications

This work presents a Quazi Z-Source Seven-Level inverter (qZS7LI) for Photovoltaic (PV) applications. The presented topology partakes the benefit of having a lesser switch count (8 switches) compared to current 7-Level qZ source topologies. The presented qZS7LI consists of three quasi-Z-source based impedance network, 02 bidirectional power switches, and one H-bridge inverter. The presented qZS7LI topology is studied by using pulse-width modulation (PWM) technique. The bidirectional switches and one leg of the H-bridge inverter are employed to insert shot through and generate levels, operating at a high frequency.

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A Novel Open-Circuit Fault-Tolerant MMC with Multi-carrier PWM Techniques for Solar PV Applications

Modular multilevel converters (MMC) have attracted much interest from researchers worldwide. It is a desirable solution due to important features such as modularity, low harmonic content at high output voltages, avoiding large capacitors and separate DC sources, easy scaling to any voltage level, and reduced voltage stress on switches, as well as being suitable for high and medium power applications, such as HVDC and motor drives. High-voltage applications require a cascade of hundreds of sub-modules. Depending on the type of sub-module selected for the application, the sub-module of the MMC may contain several switches. Depending on the MMC-based application, the converter typically needs to operate for a long period, two or three years, without interruption. MMCs can experience many electrical problems, including single line-to-ground faults, DC-bus short circuit faults, switch open circuit faults, and short circuits. A malfunction like this can damage the MMC and cause the system voltage to drop.

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Cost-Benefit Evaluation of the Implementation of a Photovoltaic Solar Generation System in the Archipelago of San Andrés, Providencia, and Santa Catalina

The study highlights the importance of photovoltaic solar energy as a viable alternative for the Archipelago of San Andrés, Providencia, and Santa Catalina in response to the current 99% dependence on fossil sources, especially diesel. There is a need to diversify the energy matrix and reduce associated costs, including significant subsidies. The implementation of renewable sources is considered crucial to achieve sustainable development goals and improve energy autonomy, in addition to mitigating environmental impacts. The study proposes a detailed cost-benefit analysis of implementing a solar photovoltaic system, considering the return on investment and the savings in monthly energy payments for different user segments, classified into four clusters: strata 1 and 2, strata 6, commercial, and official.

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MS-CFFS: Multistage Coarse and Fine Feature Selection for Advanced Anomaly Detection in IoT Security Networks

In recent years, the concept of Internet-of-Things (IoT) has increased in popularity, leading to a massive increase in both the number of connected devices and the volume of data they handle. With IoT devices constantly collecting and sharing large quantities of sensitive data, securing this data is of major concern, especially with the increase in network anomalies. A network-based anomaly detection system serves as a crucial safeguard for IoT networks, aiming to identify irregularities in the network entry point by continuously monitoring traffic. However, the research community has contributed more to this field, the security system still faces several challenges with detecting these anomalies, often resulting in a high rate of false alarms and missed detections when it comes to classifying network traffic and computational complexity. Seeing this, we propose a novel method to increase the capabilities of Anomaly Detection in IoT.

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Enhancing Facial Recognition Accuracy through KNN Classification with Principal Component Analysis and Local Binary Pattern

Recent developments in deep learning techniques have led to remarkable progress in facial recognition. As a component of biometric verification, human face recognition has become widely used in a variety of applications, including surveillance systems, home entry access, mobile face unlocking, and network security. Conventional facial recognition techniques are especially useful when dealing with low-resolution photos or difficult lighting situations. The K-nearest neighbor (KNN) classifier has been used in this paper. KNN is a non-parametric, instance-based learning algorithm that is commonly used for classification tasks. Principal Component Analysis (PCA) and local binary pattern (LBP) are used in this study to develop face identification.

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An Intelligent Approach for MPPT Extraction in Hybrid Renewable Energy Sources

A multi-source power system that integrates sustainable energy sources for power generation. MPPT, or Maximum Power Point Tracking, is a method employed to optimise the power generation of sources, such as solar panels or wind turbines. Since the efficiency of these sources can vary due to environmental conditions (like sunlight intensity or wind speed), MPPT algorithms optimize the electrical operational parameters of the modules to guarantee they are functioning at their highest efficiency. In the context of MPPT, fuzzy logic is used to handle the uncertainties and nonlinearities in the behaviour of these sources. It allows for a more adaptive and resilient control strategy, which can be particularly effective in fluctuating environmental conditions. When fuzzy logic is applied to MPPT in a hybrid power system, the goal is to intelligently manage and optimize the power output from various sources.

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Advancements in Mathematical Modelling for Estimation of Lifetime of Wireless Mobile Ad Hoc Networks

This article proposes a comprehensive mathematical model along with reviews on various mathematical models used by the researchers to estimate the lifetime of Mobile Ad-hoc Networks (MANETs). Network lifetime is a crucial quality-of-service (QoS) metric, and researchers have defined it in multiple ways, including the time from network establishment to its failure or the average time until the network dies. Many models utilize the First Order Radio Model (FORM) to calculate energy consumption during data transmission and reception. There are different factors which influence network lifetime which include, energy consumption during data transmission and reception, battery capacity and discharge rate, energy consumption by the microcontroller, transceiver, and sensors in various modes (active, sleep, idle, etc.). The article also discusses existing mathematical models that are based on some of these factors.

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Enhancing Data Security through Hybrid Error Detection:Combining Cyclic Redundancy Check (CRC) and Checksum Techniques

Error detection is a critical aspect of ensuring the accuracy of data transmission in communication systems. In this study, the performance of two error detection techniques has been investigated when combined to achieve a Bit Error Rate of 10^(-5)for single and multiple error detection ability. The two techniques studied were Cyclic Redundancy Check and Checksum with a new combination process. This proposed method showed that when CRC and Checksum were combined, the overall error detection performance significantly improved compared to using either technique alone. Specifically, the combined technique was able to achieve a BER of 10^(-5) for 6 given examples with higher accuracy and lower false positive rates. These findings demonstrate the potential benefits of combining error detection techniques to enhance the reliability of data transmission systems.

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Studying Relative Merits of FOC and DTC for 1-∅ Synchronous Induction Motor Powered by Solar Cell

The paper studies the merits of a 1-∅ induction motor powered by a Trina Solar's module TSM-250PA05.08 that employs the perturbs and observes (P&O) technique, utilizing two types of controllers, FOC and DTC. The merits of the two types of controllers were studied according to the results of the electromagnetic torque (Te), the currents of the main winding Ia and auxiliary winding Ib, and the Speed of a rotor. The results showed that both controllers' electromagnetic torque and speed rotor are very close. The results also showed distortions in the main and auxiliary winding currents when using the DTC controller. In contrast, when utilizing FOC, the results demonstrate smoother waveforms for main and auxiliary winding curves.

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Implementation of Realtime Image Fusion for Biomedical Applications Using ICA And Discrete Wavelet Transform

Image fusion is an extensively used technique in various areas like computer visualization, enhanced diagnostic imaging, radio therapy, automatic object recognition, image analysis, and remote sensing. The main aim of image fusion is to combine several input images into one image containing more information than the individual images. This type of image fusion results in a new image that is easier for computers and humans to see, making it possible for additional image processing operations like object detection, segmentation, and feature extraction. This paper examines the potential application of customized wavelet transform for image fusion.

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Design of a Multi-loop PI Controller for Minimum Phase System Level Regulation in a Quadruple Tank: A Method for Constraint Optimization

A nonlinear optimization based decentralized PI controller for Two Input Two Output (TITO) is presented in this paper. Modelling of Quadruple tank minimum phase system with time delay is introduced here. The basic principles of nonlinear optimization are utilized to design the proposed PI controller in which the overshoot is bounded with constraints on the maximum closed-loop amplitude ratio, maximum closed loop width, gain and angle bounds. Besides, the control algorithm is designed for decoupled systems to reduce the loop interactions. Further, the first order plus dead time (FOPDT) model is derived for each of the decoupled subsystems to design the control law.

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Dynamic Optimization in 5G Network Slices: A Comparative Study of Whale Optimization, Particle Swarm Optimization, and Genetic Algorithm

This study presents a comprehensive framework for optimizing 5G network slices using metaheuristic algorithms, focusing on Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine Type Communications (mMTC) scenarios. The initial setup involves a MATLAB-based 5G New Radio (NR) Physical Downlink Shared Channel (PDSCH) simulation and OpenAir-Interface (OAI) 5G network testbed, utilizing Ubuntu 22.04 Long Term Support (LTS), MicroStack, Open-Source MANO (OSM), and k3OS to create a versatile testing environment. Key network parameters are identified for optimization, including power control settings, signal-to-noise ratio targets, and resource block allocation, to address the unique requirements of different 5G use cases. Metaheuristic algorithms, specifically the Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), are employed to optimize these parameters.

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Multi-Source Data Integration for Navigation in GPS-Denied Autonomous Driving Environments

Autonomous driving is making rapid advances, and the future of driverless cars is close to fruition. The biggest hurdle for autonomous driving currently is the reliability and dependability of navigation systems. Navigation systems are predominantly based on GPS signals and despite it being highly available there are scenarios where GPS is either not present or unavailable such as in tunnels, indoor environments, and urban areas with high signal interference. This paper proposes an adaptive decision-making algorithm that leverages multi source data source integration for navigation in GPS-denied environments. The algorithm enables seamless switching between the different data sources such as LTE or 5G for autonomous driving systems to maintain accurate navigation even when GPS signals are unavailable.

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Advanced Artificial Neural Network for Steering and Braking Control of Autonomous Electric Vehicle

Sensors are necessary for an autonomous electric vehicle (AEV) system to identify its environment and take appropriate action, such avoiding obstacles and crashes. Despite their limitations about color, light, and non-metallic items, cameras, radar, and lidar are widely employed to detect objects surrounding a vehicle. Ultrasonic sensors are weather and light-resistant. Thus, the goal of this work was to create object detectors by combining multiple long-range ultrasonic sensors into a multi-sensor circuit. The Arduino processor incorporates an artificial neural network that uses the advanced artificial neural network as a novel approach to control the sensors. There are two steps to this method: offline training and implementation test. The most ideal neural network weights are found offline using the adaptive back propagation algorithm, and the best fixed weight is then embedded into the neural network software on Arduino for implementation test.

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Real Power Losses Reduced by Network Reconfiguration the Distribution Systems using Modified BAT Algorithm

This research paper is proposed to achieving the minimum power losses in all the branches, minimum number of switching operations, maximizing the power flow through the placing the DG sources, minimizing the voltage deviations with satisfying all the constraints using the modified BAT algorithm. The effect of the offered method is tested on standard systems like IEEE 33, 69 buses and Indian standard 62 bus distribution systems. The mBAT effect is estimated with the recent algorithm including Shuffled Frog, Stud krill, Dingo, Grey Wolf, and Antlion algorithms. MATLAB results are proved that the total power active power losses and branch voltages and number of switches, capacity of DG sources and cost of the DG sources are drastically reduced. The results are compared with many techniques are tabulated.

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Field Analysis and Equivalent Circuit Parameters of Linear Induction Motor with Eddy Current Secondary, Taking End Effect into Consideration

The field analysis of a single-sided linear induction motor (LIM) taking end effect into consideration is carried out. A suitable model is used to establish the two components of secondary current density and the air gap field intensity, these components, beside the force density are carried out through the three regions model. The drive force acting on a conducting secondary sheet is calculated and plotted as functions of the speed, taking the length of secondary ends as parameter. The motor speed can be controlled by changing the displaced angle φ of the electric loading wave in the second stator phase.

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Creating a Logic Divider Based on BCD and Utilizing the Vedic Direct Flag Method

Reversible logic has potential for a variety of applications demanding low energy usage since it prevents information loss and energy waste. The purpose of this work is to design a new Vedic divider circuit with reversible gates. Efficiency in quantum and ASIC parameters is demonstrated by the Reversible Direct Flag Vedic Division Method (RDFVDM), which has been devised. Block-level reversible gates are used in the RDFVDM to provide benefits including lower quantum costs and less trash outputs. The performance of Cadence EDA Tool is validated by simulation trials. Based on a comparative examination utilizing current methodologies, RDFVDM performs better than comparable designs.

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Implementation of Massive Multiple-Input Multiple-Output (MIMO) 5G Communication System using Modified Least-Mean-Square (LMS) Adaptive Filters Algorithm

The massive MIMO systems are the more popular field in the present era for the 5G wireless communication system. The MIMO system is a demanding research topic for the last four decades. This topic is under implementation and observation from the last few years. These systems have many advantages and many research sub-areas but this paper investigates the modified model of the massive MIMO receiver system. The traditional receiver system model of massive MIMO system reduces the channel noise using a linear filter in the receive combiner bank (RCB) but the proposed model removes the channel noise before the RCB using an adaptive filter bank (AFB). The AFB is the combination of LMS adaptive filters.

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Experimental implementation of Speed Stabilizer Based Field Oriented Control of Brushless DC Motor for Scooter Applications

An electric scooter, as one type of Lightweight vehicles technology, is a motorized vehicle designed for short-distance travel and recreational purposes. It is powered by an electric motor and typically has a rechargeable battery that provides sufficient power to operate the vehicle. Electric scooters are similar to traditional scooters but are much quieter, eco-friendly, and more energy-efficient. They are commonly used as an alternative mode of transportation for commuting, sightseeing, and recreational activities. This work presents an experimental implementation of speed stabilizer of electric scooter. In fact, a constant speed function might be required in a specific case in the operation of the scooter. A Field Oriented Control (FOC) method was chosen to control the speed of 3-phase Brushless DC motor of the scooter using a PIC16F873A Microcontroller through a Driver circuit.

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Economic Load Dispatch of Thermal-Solar-Wind System using Modified Grey Wolf Optimization Technique

The growing demand for electrical energy, coupled with the uneven distribution of natural resources, necessitates the integration of power plants. Coordinating the operation of interconnected generating units is crucial to meet the fluctuating load demand efficiently. This research focuses on the Economic Load Dispatch (ELD) problem in hybrid power systems that incorporate solar thermal and wind energy. Renewable energy resources, such as wind and solar thermal energy, depend on atmospheric conditions like wind speed, solar radiation, and temperature. This study addresses the ELD problem using a Modified Grey Wolf Optimization (MGWO) approach to obtain the most optimal solution for generator fuel costs. The Grey Wolf Optimization (GWO) approach, inspired by natural processes, is utilized but may exhibit both exploratory and exploitative behavior.

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Deep Learning based DWT- Bi-LSTM Classifier for Enhanced Cardiovascular Arrhythmia Classification

Nowadays heart diseases and their diagnosis have emerged as a prominent subject in health care systems, given that the heart performs a crucial role in the human body. Several computational techniques have been explored for the recognition and classification of cardiac diseases using Electrocardiogram (ECG) signals. Deep Learning (DL) is a present focus in healthcare solicitations, particularly in the classification of heartbeats in ECG signals. Many studies have utilized dissimilar DL models, including RNN (Recurrent Neural Networks), GRU (Gated Recurrent Unit), and CNN (Convolutional Neural Networks), to classify heartbeats using the MIT-BIH arrhythmia dataset.

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A Classical Approach for MPPT Extraction in Hybrid Energy Systems

A novel approach for Maximum Power Point Tracking (MPPT) extraction using the Hill Climbing method in hybrid solar and wind energy systems. MPPT is essential for optimizing the energy harvesting efficiency of sustainable energy sources, the integration of multiple sources poses unique challenges. The proposed Hill Climbing algorithm is applied to both solar photovoltaic (PV) panels and wind turbines, enabling efficient tracking of the Maximum Power Points (MPPs) under varying environmental circumstances. This article investigates the performance of the Hill Climbing MPPT method through simulation and experimental validation in a hybrid energy system.

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An Enhanced Dynamic Collector Voltage and Current Clamping Method for Semiconductor in Electric Vehicles

The electric drive and the batteries are the two primary parts of an electric vehicle (EV). In order to increase the availability and dependability of the semiconductors used in traction converters, this research focuses on a new approach of semiconductor protection. The IGBT overshoot in voltage during a short circuit situation was successfully reduced by a newly created active voltage and current clamping circuit. This innovative method restricts IGBT’s collector-emitter voltage during the turn-off event. As soon as the collector-emitter voltage of the IGBT crosses a predetermined threshold, the IGBT is partially turned on.

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Improvement of Solar Farm Performance based on Photovoltaic Modules Selection

The emissions of greenhouse gases from conventional power plants are currently a significant cause for worry. In China, about 75% of total domestic energy is dependent on coal-fire power, which emits 50% of total SO2 and has a significant impact on the human respiratory system. Therefore, solar power plants are a viable option that can mitigate this problem. Furthermore, the efficiency of solar modules exhibits a progressive upward trend, while their price per watt experiences a corresponding decline, making it a promising source for future energy. This article examines the performance and effectiveness of several photovoltaic (PV) modules in designing solar plants on a certain land area measuring 10000 m2 (100 m * 100 m). The PV plant performance was evaluated by comparing occupation ratio (OR), PV power capacity, net energy production, performance ratio (PR) via PVsyst software, and lastly financial analysis.

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Millimeter Wave Massive MIMO systems using Hybrid Beamforming

The goal of next-generation wireless systems is to support many users by achieving higher data rates and reduced latency. Multiple input and multiple output systems (MIMO) are utilized in order to achieve high data rates. Multiple antennas are employed by Massive MIMO systems in both the transmitter along with the receiver. A signal processing method known as beamforming is used on several transmitting and receiving stations in order to deliver and receive multiple messages at once. To increase spectral efficiency, hybrid beamforming using a uniform rectangular antenna array is used in this research work. The results of hybrid beamforming using different numbers of antennas are compared with those of fully digital beamforming.

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An Efficient Image Encryption Scheme for Medical Image Security

In the contemporary landscape of digital healthcare, the confidentiality and integrity of medical images have become paramount concerns, necessitating the development of robust security measures. This research endeavors to address these concerns by proposing an innovative image encryption scheme tailored specifically for enhancing medical image security. The proposed scheme integrates a sophisticated blend of symmetric and asymmetric encryption techniques, complemented by a novel key management system, to fortify the protection of medical image data against unauthorized access and malicious tampering. The proposed DNA-based encryption algorithm leverages the unique properties of DNA encoding to securely scramble image data, providing an added layer of protection. By utilizing DNA sequences in the encryption and decryption processes, the scheme achieves a high level of data confusion and diffusion, significantly enhancing security.

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Improving Robustness and Dynamic Performance of Sensor less Vector-Controlled IM Drives with ANFIS-Enhanced MRAS

The Model Reference Adaptive System (MRAS) enables effective speed control of sensorless Induction Motor (IM) drives at zero and very low speeds. This study aims to enhance the resilience and dynamic performance of MRAS by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller into sensorless vector-controlled IM drives. To address issues related to parameter uncertainties, load variations, and disturbances, the combination of MRAS and ANFIS is investigated. The ANFIS controller improves the dynamic performance by adapting its parameters based on the error between estimated and measured rotor speeds. This allows for better tracking of the reference speed and smoother drive operation.

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Conversion of An Existing 3 – Phase BLDC Hub motor to A 6 – Phase BLDC Hub Motor and their Performance Analysis for EV Application

In this research work a six–phase BLDC Hub motor designed, developed by using an existing 3–phase BLDC Hub motor material. This conversion orients towards design modifications in the stator winding layout, the number of phases, the number of hall-sensors, placement of hall-sensors and stator winding commutation. Also, a new controller is designed to commutate the six-phase stator winding. The rotor is kept the same without any modification in geometry and number of magnets. The existing 3–phase BLDC Hub motor, which is used in two – wheel EV application, has 48 slots and 52 magnets and a concentrated double layer winding layout is observed in it. This paper presents a novel direct approach for the conversion of the existing 3–phase BLDC Hub motor to a six–phase BLDC Hub motor. Using this proposed approach, a six-phase winding layout is designed and developed for the existing 48 slots stator of the BLDC Hub motor.

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A Comparative Investigation of Hybrid MPPT Methods for Enhancing Solar Power Generation in Renewable Energy Systems

Photovoltaic (PV) systems are among the types of renewable energy that are frequently employed. Since the characteristics of the solar cell depend on the amount of insolation and temperature, it is necessary to use MPPT “Maximum Power Point Tracking” to move the operating voltage close to the maximum power point under changing weather conditions. This article aims to design a photovoltaic energy system based on boost converter control to obtain maximum power using a hybrid algorithm based on artificial neurons (ANN). Additionally included is a proportional-integral (PI) controller, which improves the performance of the ANN-MPPT controller; this method is quick and precise for tracking the maximum power point (MPP) in the face of variations in temperature and solar radiation.

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Optimal Reactive Power Dispatch Using Artificial Gorilla Troops Optimizer Considering Voltage Stability

The power system has been expanded to supply and fulfil the consumers’ requirements for reliability, affordability, and power quality. Power loss reduction and voltage stability enhancement are important points and have been considered interesting subjects for researchers and utilities. Furthermore, reactive power plays an important role in power system stability, security, and voltage improvement, and it is known as reactive power dispatch (RPD). In this paper, a newly developed meta-heuristic optimization technique that inspired the gorilla troop’s social intelligence in nature is applied. It is named Artificial Gorilla Troop Optimization (GTO). In addition, GTO is utilized to solve the optimal reactive power dispatch (ORPD) problem, whose real active power and voltage deviation reduction are the objective functions of this study. Generator voltage, transformer tap-changers, and reactive power compensators are the controlled variables that are optimized for achieving the minimum real power loss and bus voltage deviation.

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Effects of Number of Filters and Frequency Cutoff in Continuous Interleaved Sampling and Frequency Amplitude Modulation Encoding Schemes in Cochlear Implant

Cochlear implants are devices designed to transform sound into electrical signals perceived by the brain, making them vital prostheses for deaf individuals. This study examines two schemes used in cochlear implants, namely Continuous Interleaved Sampling (CIS) and Frequency Amplitude Modulation Encoding (FAME), to compare their performance while varying the number of bandpass filters and cutoff frequencies used. Both schemes were simulated using 8 and 5 bandpass filters, and cutoff frequencies of 2000 Hz and 200 Hz. Results show that the CIS scheme can maintain signal intelligibility despite the loss of some frequency components when the number of bandpass filters is lowered.

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Comparative Analysis and Performance Evaluation of Dual Active Bridge Converter Using Different Modulation Techniques

The development in the renewable energy systems and the necessity of the enhanced grid that includes the conventional energy sources and the renewable energy sources and storage systems have realized the importance of power electronics conversion systems. These interfaces are crucial for enhancing the efficiency as well as the control in bi-directional power flow. The use of HEVs as a means of preserving the future supply of fossil fuels, as well as the need for improving the efficiency of the power electronics interfaces required for efficient power management between the two energy sources of the vehicle, is discussed. Furthermore, the improvements in the Uninterruptible Power Supply (UPS) systems and regenerative power systems also require sophisticated power electronic conversion systems.

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Experimental analysis of an Interleaved Boost Converter for Electric Vehicle Applications

With its potential to improve fuel efficiency and contribute to a more sustainable energy future, electric cars will be an integral part of the transportation system of the future. For the time being, this industry is using traditional boost converters. A proposal for an interleaved boost converter for EVs is made in this article. When contrasted with the Classic boost converter, the suggested one produces higher-quality results. In this proposed work, we use a two-phase boost converter to lower the output waveform's ripple current, which is often rather significant in boost converters. It is suggested to use an Interleaved Boost Converter with MPPT to maintain a steady DC output voltage from PV systems. Electric vehicle propulsion performance and system current ripple are both enhanced by the suggested integrated circuit.

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Comparative Study of Photovoltaic Thermal Performance with Water and Aloe Vera Heat Extracting Fluids

Crystalline solar panels are widely used in households and for public road lighting. Mono-crystalline panels are well-known for their higher efficiency and long service life. However, their efficiency decreases as the module temperature increases under consistent solar radiation conditions. To enhance module power generation and efficiency, effective temperature reduction techniques are necessary. This study investigates the use of water and aloe vera fluid as cooling agents for a mono-crystalline photovoltaic thermal (PVT) system. The system was designed with a circulating mass flow rate of 0.016 kg/s or 1 LPM (liter per minute) and tested under the climate conditions of Phnom Penh city, Cambodia.

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Performance Analysis of Hybrid Relay Selection in Cooperative NOMA Systems

Cooperative NOMA is a method to improve the system performance, this could be done by enabling a user to relay the signal to other users or a dedicated relay. Several dedicated hybrid relays are distributed to forward the source’s signal to the destinations to further enhance the system and serve the users at the cell edge. However, operating several relays simultaneously reduces the spectral efficiency because they need orthogonal channels. Thus, hybrid relay selection is proposed and investigated as a cooperative technique to improve NOMA performance.

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Improved PID based Adaptive Controllers for Denoising Biomedical Signals

Biomedical signal processing is one of the most popular research domains. Very fine features in biomedical signals carry important information regarding patient’s health. So, it is necessary to have noise free biomedical signals for the correct diagnosis. The major trouble for biomedical equipment is Power Line Interference (PLI) which impairs the signals. An adaptive filter can be one of the possible solutions for the removal of non-stationary noise, but maintaining the system stability along with a high convergence rate is a critical issue. The adaptive algorithm works on the principle of minimization of error for optimized coefficients updating while PID controller attempts to minimize the error over time by adjusting the control variables.

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Stabilizing Voltage and Managing Power Loss in Medium Voltage Distribution Systems Through Strategic Maneuvers

Repairs and maintenance on the electrical power system often require power outages. To ensure safety and security during maintenance, limiting the extent of these outages is essential. This study aims to analyze the impact of network maneuvers on voltage drop and power loss in feeders in Medan, Indonesia. The research involves simulating the feeder circuit under various maneuver scenarios using ETAP 19.0.1 software to analyze voltage drop and power loss in the 20 kV distribution network. The end voltage on the backup feeder decreased from 20.31 kV before maneuvering to 20.10 kV after maneuvering.

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A Novel Hybrid FSO-mm Wave System for Enhanced Mobile Network Capacity and Reliability

The ever-growing demand for high-bandwidth communication in mobile networks necessitates innovative solutions that provide exceptional capacity and reliability. This paper introduces a novel dual hop hybrid system model that leverages the complementary strengths of free space optics (FSO) and millimeter-wave (mm W) communication, while incorporating a direct mm W link for added robustness. This paper analyzes the comprehensive performance of the proposed hybrid system using a single threshold selection combining. We consider a scenario where the channel state for the FSO link adheres to a Log-Normal distribution under conditions of weak turbulence. For the millimeter wave link, we assume it follows a Nakagami-m distribution, which encompasses a broad range of commonly encountered radio frequency (RF) fading distributions.

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PSbBO-Net: A Hybrid Particle Swarm and Bayesian Optimization-based DenseNet for Lung Cancer Detection using Histopathological and CT Images

Lung cancer remains a substantial global fatality; early detection is imperative for successful intervention and treatment. Deep learning (DL) models have shown promise in predicting lung cancer from medical images, but optimizing their parameters remains a challenging task. To improve prediction capability, this study introduces an approach by merging Particle Swarm Optimization and Bayesian Optimization (PSbBO) to optimize deep learning parameters. PSO provides an effective way for exploring the hyperparameter space, while Bayesian optimization provides a probabilistic framework for the effective evaluation and refining of a DL network.

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Comparative Analysis of Congestion Management Methods with Integrated Renewable Energy Generation

Inclusion of renewable energy in power grid at micro and macro level has imposed numerous challenges in the recent years. Occurrence and managing congestion in the power transmission line due to unpredictable and stochastic nature of Renewable Energy Source (RES) integration has become a challenging task to the grid operators. Transmission lines operate at bottlenecks during a congestion episode adding to the extra congestion cost and risk in grid stability which becomes burden to the generation as well as end users. Different methodologies are applied to detect and manage the congestion to eliminate the congestion cost factor and maintain grid stability.

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Brain Tumor Detection using Improved Binomial Thresholding Segmentation and Sparse Bayesian Extreme Learning Machine Classification

People are dying these days from numerous deadliest diseases. One such illness is brain tumour, in which the unusual cells within the tumour quickly begin to damage the brain's healthy cells. Owing to this rapid growth, a person may pass away before the disease receives a correct diagnosis. Early disease detection is essential for any disease to help save the patient by providing them with better care. In a similar vein, a patient's life depends on early brain tumour detection. Brain tumour detection is an extremely challenging procedure that we would like to simplify in order to save time. The proposed model facilitates the quicker and more accurate identification of abnormal brain cells, leading to the early detection of brain tumours. In this work, an improved binomial thresholding-based segmentation (IBTBS) is introduced for segmentation purpose. From this segmented image, information theoretic based, wavelet transform (WT) based, and wavelet scattering transform (WST) based features are extracted.

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Optimal Control Strategy for Power Management Control of an Independent Photovoltaic, Wind Turbine, Battery System with Diesel Generator

The need for a greater supply of energy from sustainable sources is growing because of increasing energy prices, concerns about nuclear power, climate change, and power grid disruptions. This research offers a method for the balance of power management of a combination of multi-source DC and AC supplier systems that enables sources of clean energy based on an independent grid to function economically and with the highest levels of system predictability and stability possible. The DC microgrid's hybrid generation source consists of a diesel power source, wind, photovoltaic (PV) power, and a battery bank. The energy system can fulfill the load demand for electricity at any moment by connecting various renewable sources. It can function both off and on the grid. The microgrid may occasionally not be able to provide sufficient electricity, while every green energy source's electricity contribution is based on how its supply varies and how much power is needed to meet demand. As a result, a diesel generator is required as additional backup power, particularly while operating off-grid.

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State Estimation of Radial Distribution Systems Based on Multiple Legendre Neural Networks

The conventional weighted least square (WLS) method is the most effective technique used in the state estimation of high voltage transmission system. Unfortunately, the application of WLS in radial distribution network encounter difficulties due to the inherent characteristics of these systems, such as the low measurement redundancy and high r/x ratio of the distribution systems. Given the structure of bulky systems that require a bulky number of measurements, the use of artificial neural networks is considered an effective alternative to estimate these values using a lesser number of measurements than conventional techniques. Due to state estimation based on ANN technique, the time-consuming gain matrix manipulation and pseudo measurements required in the conventional WLS method are no longer necessary.

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