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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