Articles published in IJEER


Exploiting PV System Performance: A Combined Approach Using MPPT, IoT, Cleaning, Cooling, and Neural Networks

This paper investigates the effects of three foremost methodologies—Maximum Power Point Tracking (MPPT), Internet of Things (IoT)-driven cleaning and cooling, and Neural Network Training (NNT)—on improving the efficacy of solar photovoltaic (PV) systems. Solar photovoltaic systems have considerable complications in sustaining maximum performance due to environmental conditions such as dust collection, temperature variations, and an insufficient energy management. A new control method is presented to challenge these difficulties, including MPPT, IoT-based cleaning and cooling, and NNT for the real-time optimization of PV systems. The simulation findings indicate a substantial increase in power production when both technologies are used together.

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An Optimal DC Microgrid for Hybrid Consumer Loads and Electric Vehicle Integration

This paper investigates the effects of three foremost methodologies—Maximum Power Point Tracking (MPPT), Internet of Things (IoT)-driven cleaning and cooling, and Neural Network Training (NNT)—on improving the efficacy of solar photovoltaic (PV) systems. Solar photovoltaic systems have considerable complications in sustaining maximum performance due to environmental conditions such as dust collection, temperature variations, and an insufficient energy management. A new control method is presented to challenge these difficulties, including MPPT, IoT-based cleaning and cooling, and NNT for the real-time optimization of PV systems. The simulation findings indicate a substantial increase in power production when both technologies are used together.

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Effective Energy Management System in Microgrid Employing Model Predictive Controller

The primary focus of this study is to develop an energy management system that regulates the energy transfers between the hybrid microgrid system and the loads connected to it, and the grid via MATLAB/Simulink so as to model the flow of energy. The secondary aim is to make recommendations aimed at the charging and discharging of what is referred to as the hybrid energy storage system (HESS). The results indicate that the proposed algorithm successfully carried out the required task of bridging the HESS charging to discharging ratio in relation to the different operating conditions as well as power management between the microgrid and the network. In this application, a stronger charging power might be employed on the HESS. It has been seen that the HESS is more likely to complete charging within a short time than the greater charging power.

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Fast Charging System of Electric Vehicle Using Optimized Isolated Multi-Port DC-DC Converter based on Modified Coati Optimization Algorithm

Once clean, renewable energy sources are used to charge the batteries in electric vehicles (EVs), the vehicles can produce zero gas emissions, greatly improving the environment. EVs and other distributed energy storage devices can be used in a smart microgrid to deliver energy to the loads throughout highest times, reducing the impact of load shading and improving the quality of the electricity. To achieve these goals of energy balance between EVs, the grid, and renewable energy sources, an isolated hybrid multiport converter is required. This paper develops an optimized isolated multi-port DC-DC converter for controlling power flow in multiple directions in an EV. This converter contains a dc-dc unidirectional converter, a bidirectional dc-dc converter, a triple active bridge (TAB), and a multi-port dual active bridge converter.

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Optimizing Real-Time Scheduling for Post Islanding Energy Management Using African Vulture Optimization Algorithm on Hybrid Microgrids Environment

Microgrids (MG) are small-scale energy systems that use distributed energy storage and sources. Hybrid microgrids are transforming energy management by incorporating various energy resources like wind, solar, and battery storage. Effective scheduling of this resource is vital to minimize the costs and maximize energy autonomy. Advanced scheduling algorithm optimizes the operation of hybrid microgrids, which dynamically adjusts the energy consumption and generation to satisfy the demand while ensuring power balancing. This scheduling strategy has been instrumental in improving the sustainability and resilience of MGS, which paves the way for an environmentally friendly and more reliable energy future. They can operate on islanded or grid-connected modes. The optimization of hybrid MG scheduling is paramount in the field of post-island management to ensure effective energy sustainability and distribution. Using metaheuristic approaches like simulated annealing or genetic algorithms allows the finetuning of scheduling parameters to increase energy utilization while reducing environmental impact and costs.

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EEG-Based Early Detection of Autism Using Convolutional Neural Networks

This Timely intervention and improved management of autism spectrum disorder (ASD) are contingent upon early detection. In this paper a novel method for early autism identification using EEG signals and convolutional neural networks (CNNs) is proposed. Preprocessing, wavelet transform, Discrete Cosine Transform (DCT) , energy and entropy function feature extraction, and CNN classifier classification are some of the phases in the suggested approach. EEG signals are first pre-processed to get rid of artifacts and noise. Then, to extract pertinent characteristics from the EEG signals, wavelet transform and DCT are used. For feature extraction, energy and entropy calculations are used to identify unique patterns suggestive of ASD. After then, a CNN classifier receives these features and divides them into two categories: Autism identified or normal identified.

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Optimized Energy Management for PV Hybrid Power Systems with DC Bus Voltage Control

The growing popularity of direct current (DC) power sources, energy storage systems, and DC loads has recently shifted the focus away from alternating current (AC) microgrids and towards DC-only systems. However, smart and energy-efficient building integration and effective microgrid administration are prerequisites. Direct current microgrids, which include solar modules as their principal power source, an energy storage device (battery), and an essential DC load, may have their energy consumption managed with the help of our study. Within the microgrid (MG) architecture, the DC-DC boost converter enables the PV module to operate in many modes, one of which is Maximum Power Point Tracking (MPPT). In order to link the battery and supercapacitor to the DC bus, the system also makes use of a DC-DC bidirectional converter.

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An Optimized Stationary Wavelet Fusion Technique for Image de-hazing

Nowadays, the amount of smoke and dust in the air is increasing significantly due to industrialization. The smoke and dust particles accumulate in the relatively dry air and cause haze in the surrounding area, impairs visibility. This haze also affects photography, which reduces the images' quality and looks unnatural. The hazy atmosphere affects even pictures taken with a cell phone in everyday life. There are many methods to remove this haze content from the image, but they have not yielded great results. The long-time and short-time shots constantly differed while attempting to eliminate atmospheric haze from the images. To solve this problem, a fusion rule was proposed to fuse the luminance and dark channel prior (DCP) methods. The transmission estimated with the DCP method contributes mainly to the foreground regions, while the luminance model deals with the celestial regions.

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Load modeling of electric bus charging station from data obtained through Phasor Measurement Units

While the increased adoption of electric vehicles (EVs) is a promising alternative to reduce CO2 emissions, it creates new challenges for the power grid due to increased energy demand and power quality (PQ) issues. These impacts vary depending on several factors such as the level of EV adoption, charging technology, network voltage level, charging patterns, charging station location, battery condition, and driving habits. Analyzing these impacts and developing solutions, such as characterizing the demand curve for charging stations and understanding EV charging patterns, is crucial to ensure a sustainable transition to an electrified EV future. A study using the ZIP load model that represents voltage dependence by combining constant impedance (“Z”), constant current (“I”), constant power (“P”) components, and phasor measurement units (PMUs) demonstrates the effectiveness of EV demand characterization. The importance of this aspect for grid stability and charging management is highlighted.

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Implementation of PI Controller of Hybrid DC Microgrid Energy Management

This paper presents energy management in DC Microgrid. Microgrids are a growing power generating source in remote areas than the utility grid. It can be operated as a standalone & grid-connected to serve the entities. This paper concentrates on the DC Microgrid and manages the energy within the system when the grid suffers from an outage of service, and unbalanced load conditions in the grid. During grid conditions, the buses connected to it should synchronize with the grid. The voltage in the buses is same, that is verified in this paper work through controllers to the converter are also discussed.

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Vehicle-to-Grid Power Transfer Method for Electric Vehicles using off-board charger

This article explores a power transfer technique from vehicle to grid (V2G) via the construction of an off-board charger for electric cars (EVs). The charger accommodates several charging modes, such as grid-to-vehicle (G2V), vehicle-to-vehicle (V2V), and vehicle-to-grid (V2G), facilitating efficient and adaptable energy management. In G2V mode, the charger utilizes grid power to recharge electric vehicle batteries, whilst V2V mode enables direct energy transfer between electric vehicles, circumventing the grid. The novel integration of G2V and V2V modes enables the concurrent use of grid electricity and energy from other electric vehicles, therefore diminishing grid reliance and enhancing power efficiency. The system has a three-phase pulse width modulation (PWM) rectifier that sustains a constant DC link voltage and attains a unity power factor on the grid side, therefore adhering to the IEEE 519 standard for total harmonic distortion (THD).

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Designing RNS-based FIR filter with optimal area, delay, and power via the use of swift adders and swift multipliers

Based on the Residue Number System (RNS), Finite Impulse Response filters have gained prominence in digital signal processing due to their efficiency in handling complex computations. This work presents a comprehensive analysis on optimizing area, delay, and power in the FIR filter by exploring different adders and multipliers. Three prominent adders, namely Ripple Carry Adder, Kogge Stone Adder, and Proposed Adder, are evaluated for their impact on area and delay. The choice of adder influences the overall performance of the FIR filter, and a careful selection is made based on the trade-offs between area and delay. Furthermore, various multipliers, including Booth, Baugh-Wooley, Braun, and Array, are compared in terms of their efficiency in power consumption. Multipliers contribute significantly to the overall power consumption, and the analysis involves selecting the most suitable multiplier for achieving the desired power optimization.

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Application of Walrus Optimizer for Power Quality Improvement in Radial Distribution System

To increase power quality (PQ) in radial distribution systems (RDS) by utilizing active power filters (APFs), this research discusses the application of walrus optimization algorithms (WaOA). The main problem with the PQ is harmonics. The harmonics are added to the RDS by nonlinear loads (NLs). In this instance, together with NL at two end nodes, nonlinear distributed generation (NLDG) is additionally considered. APFs are used to decrease the harmonics to specified limits. In this instance, APFs are positioned correctly to reduce harmonics and improve PQ. WaOA is utilized to maximize the APF's size at the ideal bus location. The WaOA is inspired by natural processes and contains features that are well-balanced for both exploration and exploitation. Within limitations on inequality, optimization seeks to minimize APF's current. On the IEEE-69 bus RDS, a simulation is run to assess the WaOA's performance.

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An Optimal Load Frequency Control in a Two-Area Power System Using a Fractional Order Proportional- Integral-Derivative-Based Zebra Optimization Algorithm

Load frequency control systems are crucial for maintaining the stability and reliability of power grids. They help ensure that the power supply matches the demand, preventing fluctuations in grid frequency. However, Load frequency control systems have inherent limitations, such as the potential for instability and oscillation if not properly controlled. In this work, A fractional order proportional integral derivative controller is proposed to address this issue, which exhibits strong capabilities in managing parameter uncertainties, rejecting disturbances, and handling non-linear systems controllers. The novel approach in this search is the application of the zebra optimization algorithm to fine-tune controller parameters, a technique not previously used in load frequency control systems. In this Study the two-area power system with a reheat turbine is used a test case for fractional order proportional integral derivative controller, and the system is simulated using MATLAB/SIMULINK. The objective function integral time of square error is used that acts as a bridge of communication between the system's behavior and the control strategy. The performance of this controller is evaluated under disturbance 0.04.

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Minimization of Harmonic in Stator current for Grid Connected Five Phase Squirrel Cage Induction Generator with Predictive Torque Control Technique

Wind energy systems have become a highly practical kind of renewable energy, which requires the development of more advanced control mechanisms to enhance efficiency and dependability. Five-phase machines provide several advantages compared to standard three-phase systems in this particular situation. These advantages include reduced torque variation, improved ability to handle faults, and increased capacity to handle power. The Direct Torque Control technology has attracted considerable interest for controlling five-phase squirrel cage induction generators employed in wind energy conversion systems. The main goal of DTC is to achieve efficient energy extraction from the wind by regulating torque and flux without the need for complex transformations and decoupling mechanisms, as required in field-oriented control. DTC is known for its simplicity and fast response, enabling high dynamic performance. This work introduces the notion of predictive torque control as a sophisticated control method for five-phase asynchronous generators in wind energy systems.

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MRI brain tumor classification using HOG features selected via impurity-based importances measure

MRI is considered the primary method for confirming the diagnosis of brain tumors and choosing the appropriate treatment. Automating the process of detecting brain tumors in MRI images using deep models has become a popular trend in the scientific research community. However, deep neural networks require a large volume of data to avoid overfitting, which is not ideally available. This is where handcrafted features come in handy. In this paper, we present an efficient approach for brain tumor classification that can outperform deep CNN models. In the proposed system, the histogram of oriented gradients algorithm is used to extract feature descriptors from brain MRI images.

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Comparison of SoC in Ni-MH and Lithium-Ion Battery for E-Vehicle

The current energy side of the battery is indicating in a percentage level is called state of charge (SOC). Nickel-metal hydride and lithium-ion batteries are a type of rechargeable battery. The chemical response at the positive electrode in nickel-metal hydride (NiMH) batteries is like that in nickel-cadmium (NiCd) batteries, as both use nickel oxide hydroxide (NiOOH). However, while NiCd cells use cadmium, NiMH batteries feature a hydrogen-absorbing alloy in their negative electrodes. NiMH batteries provide two to three times the capacity and a significantly higher energy density compared to NiCd batteries of the same size. The research was mainly focused on the aspect of SoC of Ni-MH and Lion batteries with operation of an electric vehicle with total weights of 600 kg was investigated using mat lab Simulink.

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Optimizing Solar PV Array Reconfiguration for Maximum Power Extraction Using Hippopotamus Algorithm under Partial Shading

This research paper presents an innovative approach to maximizing power extraction from solar photovoltaic (PV) arrays under partial shading conditions by employing the Hippopotamus Optimization Algorithm (HOA). Partial shading is a common issue that significantly reduces the efficiency of PV systems by creating multiple local maxima on the P-V curve, thereby challenging conventional Maximum Power Point Tracking (MPPT) methods. To address this, we propose an adaptive reconfiguration strategy for the PV array, optimized using HOA, which successfully moderates the impacts of shading and enhances overall energy yield. The Hippopotamus Optimization Algorithm, inspired by the foraging behavior of hippopotamuses, is utilized for its robust global search capabilities and fast convergence. The algorithm dynamically adjusts the arrangement of the PV module to locate and maintain operation at the global maximum power point.

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Design and Optimization of a Half-Circular Ultra-Wideband Patch Antenna Using Genetic Algorithm

This research introduces an optimization design of an ultra-wideband (UWB) half-circular planar antenna using genetic algorithm. The optimization process is conducted using an Application Programming Interface (API) links two softwares; MATLAB environment and ANSYS HFSS software. The UWB antenna design includes a semi-circular patch element, the UWB behavior is obtained using truncated ground plane incorporating a rectangular slot cut out of the ground. Genetic algorithm is exploited to optimize the length of partial ground plane and the size and position of the rectangular slot. The overall size of the antenna is 28×29×1.6 mm3.

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Wide Bandwidth Tri-Band MIMO Antenna Design for 5G Communication

Wireless communication research is currently focused on 5G. On an FR4 substrate, a building-like structure with two slot-based planar multiple input multiple output (MIMO) antenna has been designed. 4.3 is the dielectric constant, and the substrate thickness is 1.6mm. The proposed antenna is designed and simulated for 5G applications using CST microwave studio and HFSS at 24.25GHz, 29.25GHz, and 32.40GHz. According to the simulated results, the VSWR is lower than 3:1 and the return loss at both ports is -43.27dB, -60.55dB, and –42.61dB at 29.25GHz, 34.20GHz, and 34.075GHz resonating frequencies respectively. Isolation between both ports is better. At 24.25GHz, 29.25GHz, and 32.40GHz the proposed design achieves a gain of 5.1dBi, 5.5dBi, and 3.5dBi respectively.

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Power Quality Improvement of PV fed Grid Connected System using ANN Controlled Shunt Active Power Filter

Harmonics are common in integrated power systems, especially with the increasing use of nonlinear loads (NLL), such as those found in photo voltaic (PV) systems connected to the grid. Traditional LC filters Shunt active power filters (SAPF) have been developed to effectively correct harmonics and improve power quality performance. This study presents a three-phase voltage-fed SAPF implementation to mitigate harmonics using an artificial neural network (ANN) controller. The SAPF control system focuses on generating reference source currents to counterbalance the harmonic effects caused by NLL. The model's effectiveness is validated using experimental data gathered from a nonlinear load through MATLAB/Simulink simulations.

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System Performance Evaluation of Indoor Visible Light Communication

The objective of the paper is to exhibit the significance of the LiFi-based data transmission using light. LiFi is deployed in numerous applications such as security, augmented reality, intelligent transport system etc as typical indoor localization is essential and is being done with mobile robots. This research article proposes a short range, indoor design, light fidelity model and discusses the simulations conducted for the Lifi model and the model is analyzed for various aspects that uses different LED light sources with Line Of Sight (LOS) and without Non Light Of Sight (NLOS), different room sizes, different modulation formats, and simulation is also performed with and without noise models. The LiFi proposed model is designed to transfer data wirelessly with a data rate of 10 Gbps.

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Hybrid Learning Approach for Automated Identification and Categorization of Cardiovascular Disorders

Cardio Vascular Diseases (CVDs) pose an important global health challenge, contributing substantially to mortality rates worldwide. Electrocardiography (ECG) is a necessary diagnostic tool in the detection of CVDs. Manual analysis by medical experts, for ECG interpretation, is laborious and subject to interobserver variability. To overcome these limitations, automated categorization technique has gained prominence, enabling efficient CVDs classification. The major focus of this work is to utilize deep learning (DL) approach for the identification of CVDs using ECG signals. The presented work incorporates two hybrid models: one-dimensional convolutional neural network (1D-CNN) with Recurrent Hopfield Neural Network (1DCNN-RHNN) and with Residual Network (1D-CNN-ResNet), to obtain important features from raw data and categorize them into different groups that correlate to CVD situation.

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Symbol Interference Cancellation in MIMO-GFDM Using Singular Value Decomposition Technique for 5G

The Physical layer (PHY) is implemented with the new Generalized Frequency Division Multiplexing (GFDM) scheme to increase the throughput of wireless systems in the present communication era. The GFDM system is combined with Multi-Input and Multi Output antenna system (MIMO GFDM) to achieve higher channel capacity and low out-of-coverage probability. Therefore, the MIMO-GFDM system is suffering from internal Inter-Carrier-Interference (ICI) and external Inter-Symbol-Interference (ISI) at the receiver. This makes the receiver design more complex and challenging.

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Performance Analysis of Various Nonlinear Elements in Frequency Multiplying Circuits for Wireless Applications

This paper presents the performance comparison of various nonlinear elements in frequency multiplying circuits for low power wireless applications. An analysis and design are derived. Passive frequency multipliers take advantage of the nonlinear behavior of passive circuit components. Passive frequency multipliers take advantage of the nonlinearity of passive circuit components. In this category, Schottky diodes and varactor diodes are used for frequency multiplication. We analyzed passive devices like resistive diodes (Schottky-barrier diodes) and nonlinear capacitors (varactors). The performance between frequency doubling System using Schottky diode and frequency doubling System using varactor diode is compared.

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Highly Directive and High Gain Multiple Beam Reconfigurable Antenna for Base Stations

A directional pattern reconfigurable array with high gain is proposed in this paper in which each antenna element is an array of driven and parasitic arc dipoles. The elements can be selectively excited using RF switches and power dividers to produce high gain patterns in desired single or multiple directions. By providing optimum spacing to array elements via stacking, gain can be further improved by exciting multiple elements simultaneously. The array resonates at 5.8 GHz, which is an ISM frequency. The directivity and realized gain of the unit element are 12 dB and 10.2 dB respectively.

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Design and Development of an Enhanced Modular Multilevel Converter Using Advanced Predictive Control for Motor Drive Systems

This paper presents the design and implementation of a Modular Multilevel Converter (MMC) integrated with a motor drive system. The MMC topology offers numerous advantages such as improved voltage waveform quality, reduced harmonics, and enhanced reliability, making it an attractive choice for high-power motor drive applications. The proposed system architecture is modular in nature, facilitating scalability and flexibility to meet varying power requirements. Design considerations including sub module configuration, control strategies, and modulation techniques are discussed in detail.

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Leveraging Hybrid Precoding for Enhanced Terahertz Communication System

Terahertz (THz) transmission is a promising strategy for future 6G networks, offering ultra-wide bandwidth. Effective channel modeling and precoding techniques are essential for achieving required coverage and addressing significant path loss in THz communications. In this paper, we comprehensively examine the major THz precoding algorithms for future 6G networks, focusing on their significant challenges and prospects. We discriminate between millimeter-wave and THz channels and uncover issues with THz precoding, such as distance-dependent direction loss, beam split impact, and excessive power usage.

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Impact of Extreme Weather Parameters on Optimum Sizing of Solar Photovoltaic-Battery Energy Storage Systems: A Case Study

The performance of an off-grid solar Photovoltaic (PV) system with Battery Energy Storage (BES) depends on the system's location. Incorporating climatic variables such as solar irradiance, ambient and cell temperatures into the modelling of PV systems helps predict the system's appropriate behaviour. The paper discusses the impact of variations in seasonal irradiance, temperature, and system location on the optimum sizing of a standalone solar PV-BES system for minimum total annual cost using Improved Particle Swarm Optimization. Three locations: Pune (Maharashtra), Ladakh (Jammu and Kashmir), India, and Rafsanjan (Iran) with extreme weather conditions (winter and summer) are identified to analyze the effect for optimum sizing of PV-battery energy systems. In addition, the optimum system sizing is analyzed for lead acid and lithium-ion batteries for both seasons.

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MCBIR: Deep Learning based Framework for Efficient Content Based Image Retrieval System of Medical Images

Content-Based Image Retrieval (CBIR) in computer vision applications, enables retrieval of images reflecting user intent. Traditionally CBIR is based on image processing techniques. With the emergence of Artificial Intelligence (AI), it is now possible to realize CBIR using learning-based approaches. Particularly deep learning techniques such as Convolutional Neural Network (CNN) are efficient for image analysis. In this paper, we proposed a framework known Medical Content Based Image Retrieval System (MCBIRS), which exploits pre-trained CNN variants for retrieving medical images based on image input. The framework has an offline phase for extracting visual features from training data and an online phase for processing given user queries. The descriptors obtained by CNN variants in the offline phase are persisted in a database. These are later used in the online phase to compute the distance between persisted descriptors and input image descriptor. A set of closely matching images are returned against the query image based on similarity.

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Analysis of SEPIC Converter at Different Switching Frequencies

Single Ended Primary Inductance Converter (SEPIC) which is commonly devoted as a switched power supply in many applications is presented in this work to conclude the effect of switching frequency on the output voltage time response, elements values, spectral analysis of output voltage and efficiency. For this goal, three circuits of SEPIC converters were designed at switching frequencies of 100KHZ, 400KHZ and 600KHZ. The operating conditions of the three circuits which include input voltage range, output voltage value, output voltage ripple and inductor ripple current are kept the same while the inductors and capacitors of the converters are calculated according to each switching frequency.

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Secure Routing E-voting Protocol based on Wireless Sensor Network Platform with Block chain

Many have long aimed to create a safe electronic voting system that maintains the confidentiality and integrity of traditional voting methods while using the convenience and openness of modern technology. Ballot paper or electronic voting machines are the current voting schemes in every nation, and democratic voting is a significant event in every country. Problems with these procedures abound, including lack of openness, poor voter turnout, vote manipulation, mistrust of the election body, forgery of unique identification (voter ID card), delays in disseminating results, and, most importantly, security breaches. Prioritizing the security of digital voting is of utmost importance when contemplating implementing a digital voting system.

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Enhanced Maximum Power Point Tracking for Hybrid Solar and Wind Systems Using Fractional-Order Constant Voltage Topology: A Novel Approach in Power Electronics

The growing demand for renewable energy has led to the integration of solar and wind systems as sustainable power sources. Efficient energy harvesting from these systems requires precise tracking of the maximum power point (MPP) under varying environmental conditions. The integration of solar and wind energy systems is vital for achieving sustainable power generation. The objective of this research is to develop an efficient Maximum Power Point Tracking (MPPT) technique that addresses the variability of environmental conditions in hybrid solar and wind systems. This study introduces a novel approach using Fractional-Order Constant Voltage (FO-CV) topology for MPPT.

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Enhancing Brain Tumor Classification through Feature Selection with Beetle-Swarm Optimization

The selection of features is a crucial part of machine learning and data mining. The feature sets that are used for classification are always prone to having redundant and correlated features that can affect the performance. The goal of this study is to remove redundant and irrelevant features from the system and retain only relevant ones. This study presents Beetle-Swarm optimization process which involves selecting the features from a segmented image with a Random Forest classifier. The process is performed through a series of steps such as pre-processing, feature extraction, and feature classification. Two objective functions are used to perform the process: image entropy and accuracy function.

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An IoT Prototype for Water Quality Monitoring System with Case Study in NPIC Campus

Water is one of the natural resources that may be found in the environment. It is the most essential element of life. All living things, including humans, depend on water to survive, as do other species. Regretfully, water contamination has become a significant worldwide issue because of industrialization and irresponsible consumption. Moreover, low-quality water is poisonous to entire ecosystems due to its hazardous chemical and microbial contents. The issue is made much more dangerous by the lack of accessible tools for monitoring water quality other than costly laboratory tests.

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Linearization, EM Simulation, and Realization of a 40 DBM Class-AB Gan Power Amplifier

This research article presents the comprehensive design and analysis of a 2.45 GHz RF power amplifier operating at 40 dBm using GaN technology. The amplifier is built around the CGH40010F transistor and employs a Linearization with Nonlinear Components Method (LINC) for enhanced linearity. The study outlines the design methodology, including the selection of the CGH40010F transistor and the application of the LINC technique. It investigates the amplifier's performance characteristics, including power output, linearity, and efficiency at the 2.45 GHz frequency.

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IoT-Based Energy Meter for Remote Monitoring and Managements of Power Consumption

Real-time monitoring of several parameters such as the current and the rated power is provided by this design. Users become more conscious of their consumption with specifications based on recorded data using this design. Additionally, it enables users to quickly identify abnormal issues such as spikes that result in device failure. Also, this design offers remote access to received data by cloud platforms with mobile applications. This study evaluates the performance of an Energy Monitoring distributed among 17 electrical devices in three houses over a period of 30 days. The design demonstrated high accuracy and minimal error in its readings for the related devices with effectively capturing detailed energy consumption values.

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Voltage and Reactive Power Control Approach to Address Over-Voltage Issues in High PV Penetration Networks

High energy penetration through photovoltaic (PV) panels poses a significant challenge due to over-voltage problems. This article presents a new approach for controlling the rise of voltage in low voltage Micro-grids (LVMg) with relatively high PV penetration (HPVP) named voltage/reactive power control (VRPC). The control is set via an inverter that uses the point of common coupling (PCC) voltage and returns it to its reference voltage via controlling of reactive power. Furthermore, the network in which the proposed method is considered have a high X/R ratio to emulate the LVMg.

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Design a Merging Technique Circuit to Error Detection and Correction Based on Hamming Code and Checksum Using VHDL

Particularly when considering the Internet, recent developments in communication networks have resulted in considerable rises in both the amount of information transmitted and the user base. Errors frequently occur during transmission and reception of this spike in data transfer, which includes phone, video, and message connections. This work proposes a novel approach to error repair and detection through the combination of two well-known techniques: checksum and Hamming code. This combined strategy minimizes the drawbacks of each technique while leveraging its advantages. Even with single-bit errors, Checksum techniques can cause delays and decreased bandwidth efficiency because they require retransmitting data.

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Short Term Load Prediction based on LSTM Network for Iraqi Thermal Power Plant

Electricity generation must satisfy the demand for electric loads in order to optimize the functioning of the power system. Load prediction can assist power companies in safely and efficiently operating their electrical systems. Load prediction is a process employed by power providers to predict the quantity of power or energy always demanded for balancing supply and demand. Short-term load prediction (STLP) with high accuracy is crucial to the seamless operation of the power system and the improvement of economic benefits. An approach for predicting short-term electrical demand utilizing long short-term memory (LSTM) based on actual data collected from Wasit Thermal Power Plant in Iraq is proposed in this paper.

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Active Channel Selection by Sensors using Artificial Neural Networks

Efficient channel selection is essential for optimizing resource utilization in wireless communication. Traditional static allocation often results in underutilization and wastage of channels. This research addresses these inefficiencies by using cognitive sensors, known as secondary users (SUs), to dynamically identify and utilize available channels, thereby minimizing channel wastage and deficits. The proposed strategy configures sensors cognitively to detect real-time channel availability. Secondary users (SUs) identify free channels initially allocated to primary users (PUs) and list these channels based on parameters such as capacity, transmission range, data load, and distance.

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Performance Analysis of RIS-Assisted Index Modulation for Terahertz Communications

The significance of Terahertz (THz) communication for the upcoming sixth generation (6G) communications, which offers abundant bandwidth for high-speed data transmission. However, THz frequencies face challenges such as propagation attenuation and molecular absorption that limit their coverage range. To address this issue, the use of reconfigurable intelligent surfaces (RIS) and index modulation (IM) has been proposed to extend coverage and reduce bit error rates (BER) at low transmitted power. This paper introduces a novel RIS-assisted IM system for THz communication, employing semi-definite relaxation (SDR) optimization techniques for passive beam forming at the detector. The proposed system, named PRIS-IM, demonstrates superior performance compared to conventional RIS-IM (CRIS-IM) and conventional IM (CIM) without RIS in THz frequencies.

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Design of Microstrip Filtering Antenna for 5G band and LTE Applications

A filtering antenna is a type of antenna that not only receives or transmits electromagnetic signals but also filters out unwanted frequencies or interference. It combines the functions of an antenna and a filter, typically a bandpass filter, to only allow specific frequency ranges to pass through while rejecting others. The goal of the current work is to design a Snow flake-Koch fractal microstrip filtering antenna to operate at 2.1GHz and 4.98GHz for 2.1GHz band 5Gn1 LTE Applications and Wi-Fi applications. Better impedance bandwidth is by optimizing the shape of the patch using snow flake fractal up to 2nd iteration.

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Potentials of Genetic Algorithm in the Performance of Load Frequency Control using FACT Devices, AVR and SMES for Hybrid Power System in Deregulated Environment

In this article, the interconnection of two areas of a multi-unit hybrid interconnected power system is studied under a deregulated environment. For emergency requirements and energy-efficient building systems, the hybrid power system is studied which consists of several co-generating units thermal, hydro, nuclear, diesel, and gas power. To control the active power of the interconnected system, the Automatic Generation Controller (AGC) control loop is investigated, whereas, to control voltage, a reactive power control loop named automatic voltage regulator is applied. Effective governance requires the use of additional reliable fast energy exchange devices named superconducting magnetic energy storage (SMES) in both areas. The FACT devices considered are thyristor control series compensator (TCSC), Thyristor controlled phase shifter (TCPS), Unified power flow controller (UPFC), and Static synchronous series compensator (SSSC).

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Optimal DG and Capacitor Allocation Along with Network Reconfiguration Using Grey Wolf Optimizer Algorithm

This study introduces a multi-criteria optimization approach using the Grey Wolf Optimizer (GWO) algorithm to determine the optimal capacity, locations of DG units and capacitor banks, and network reconfiguration in distribution systems. The objective function incorporates six key performance metrics: real losses, imaginary losses, voltage variation, voltage stability index, section load ability, and current balancing index. The proposed GWO technique was evaluated on IEEE 33- and 69-bus systems and benchmarked against Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

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A Robust Deep Learning-Based Speaker Identification System Using Hybrid Model on KUI Dataset

Background: Speaker identification, detecting human voices using speech characteristics and acoustics, is essential in security, biometrics, IoT, and human-computer interaction (HCI). As technology advances, more innovative software and robust hardware enhance these applications. This study evaluates feature extraction, pre-processing, and deep learning methods for speaker identification in natural settings. Methods: We compared deep learning algorithms, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a proposed Hybrid model. Audio files were processed using different feature extraction and pre-processing techniques. Results: The proposed Hybrid model achieved the highest accuracy at 95%, surpassing other models. LSTM followed with an accuracy of 93%.

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16-Elements Ring Shaped MIMO Antenna Design for 5G Wireless Communication

The most popular area of study in wireless communication is Fifth Generation (5G). In this paper, a 5G MIMO antenna with a wide bandwidth, large gain, and compact structure is designed. The suggested design has two input and two output ports, with eight octagonal randomly shaped patches designed with Rogers RT5880 substrate in a building from the front and stripped from the bottom sort of arrangement. On CST-Microwave Studio, the entire designing and simulation process is carried out. Several characteristic parameters have been determined in this research using CST software. Broadband from 32GHz to 35 GHz and -43.62 return losses achieved at 33.56 GHz frequency with the proposed design.

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Block-chain Enabled Strategies for Efficient Power Loss Management in Distribution Networks

This study proposes a novel methodology for optimizing capacitor placement in distribution networks, employing the War Strategy Optimization (WSO) algorithm integrated with adaptive parameter tuning and block-chain technology. The WSO algorithm, inspired by military strategies, strategically positions fixed kVAR capacitors at variable locations to minimize power losses and enhance voltage profiles. The adaptive parameter tuning dynamically adjusts algorithm parameters to improve optimization efficiency, while the incorporation of block-chain ensures secure and verifiable optimization results. The methodology is tested on the IEEE 33-bus test system under different loading conditions (80%, 100%, and 120%), representing light, normal, and heavy load scenarios.

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Harmonic Analysis of Stator Current for Grid Connected Five Phase Open End Winding Squirrel Cage Induction Generator Using Indirect Rotor Field Oriented Control Technique with Three Level Rectifier

The rising need for renewable energy sources requires the optimization of power generation systems, especially in wind energy applications. The five-phase open end winding induction generator offers benefits such as less torque ripple and improved fault tolerance relative to conventional three-phase systems. The Indirect Rotor Field Oriented Control Technique for five phase open end winding squirrel cage induction generator separates the control of electromagnetic torque and rotor flux, allowing for accurate regulation of generator output in fluctuating wind conditions. The control technique utilizes a three-level rectifier to enhance power conversion efficiency, resulting in a more stable voltage output during various wind speeds and diminished harmonic distortion.

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