IJEER Vol no. 12, Issue 2


Improved Power Sharing Strategy for Parallel Connected Inverters in Standalone Micro-grid

The primary goal of integrating alternative energy systems such as solar and wind turbines into the power grid using power electronic devices is to meet the growing energy demands. Connecting inverters in parallel effectively enhance power capacity, reliability, and overall system efficiency. However, an uneven power distribution among the inverters is a significant limitation in these parallel connected inverters (PCI). This study focuses on a distributed generation (DG) unit comprising a solar photovoltaic system (SPV) and a battery energy storage system (BESS) connected to voltage source inverters (VSI) 1 and 2. The proposed approach aims to achieve uniform load/power distribution among the inverters with power management, maintaining a constant DC link voltage despite variations in solar irradiation and temperature. Additionally, the strategy targets the reduction of total harmonic distortion (THD) in the load current.

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Optimizing Capacitor Placement in Distribution Systems Under Variable Loading Conditions with Golden Jack Optimization (GJO)

In modern society, the demand for electricity is ever-growing, making the minimization of power losses in distribution systems paramount. One significant aspect contributing to these losses is the strategic placement of capacitors within the distribution network. Efficient capacitor placement not only reduces power losses but also enhances the overall performance and reliability of the system. In today's world, where electricity is indispensable, minimizing power losses in the distribution system holds significant importance. This research introduces the Golden Jack Optimization (GJO) algorithm as a novel approach to address the challenge of capacitor placement in distribution systems. GJO, inspired by the foraging behavior of jackals, exhibits unique characteristics such as adaptability and efficiency in finding optimal solutions this paper proposes an innovative algorithm specifically designed for this purpose.

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Green Power Ev Charging Station Design and Analysis for Electric Vehicles

The primary goal of this research is to design on electric vehicle charging station with less emission in Chennai due to an increase in electric vehicles. The wind and solar are common renewable energy sources which produces green power. These renewable sources can also be implemented with diesel generator and grid connection to run the Electric Vehicle (EV) charging station. This research also focuses on the cost of energy and the total cost of the system for different sources to operate EV charging station. The sources to operate an EV charging station in various period of time to charge the vehicle are analyzed. The sensitivity analysis like derating of solar also done to examine the status of different parameters in entire system with low cost. The design of low-cost system for Electric Vehicle charging station will be a useful implementation to Chennai city for charge various EV vehicles.

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DFIG in Wind Energy Applications with High Order Sliding Mode Observer-based Fault-Tolerant Control Scheme using Sea Gull Optimization

This paper describes a new method for maximizing power extraction from a wind energy conversion system (WECS) by using a doubly fed induction generator (DFIG) that operates below nominal wind speed. To maximize the collected power of a wind turbine (WTG) exposed to actuator failure, a fault-tolerant high-order sliding mode observer (HOSMO) and Seagull Optimization Algorithm with a model predictive controller (MPC) technique is proposed. Evaluate both the real state and the sensor error simultaneously using a higher-order sliding-mode observer. Active fault tolerant controllers are designed to regulate wind turbine rotor speed and power in the presence of actuator defects and uncertainty. With the growing interest in employing wind turbines (WTGs) as the primary generators of electrical energy, fault tolerance has been seen as essential to improving efficiency and reliability.

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Resource Optimization in H-CRN with Supervised Learning Based Spectrum Prediction Technique

Cognitive radio network shows potential means of granting intensifying demand for wireless applications. In this model, an efficient resource optimization scheme with Priority Pricing Technique (PPT) is proposed with supervised learning-based SVM to tackle limited spectrum availability and underutilization in Hybrid-Cognitive Radio Networks (H-CRN). H-CRN works under the principle of detection of PUs states (active/inactive). If spectrum sensing is made in favor of active PUs, then the CSI (Channel State Information) is estimated and works in underlay principle. If it is made in favor of inactive PUs, then the transmission is performed in overlay manner. In the proposed PPT the PUs and SUs with highest channel gain have the highest priority to use the spectral resources. SVM is used as an effective technique of spectrum sensing to provide higher probability of detection of PUs as soon as possible.

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Empowering Smart City IoT Network Intrusion Detection with Advanced Ensemble Learning-based Feature Selection

This study presents an advanced methodology tailored for enhancing the performance of Intrusion Detection Systems (IDS) deployed in Internet of Things (IoT) networks within smart city environments. Through the integration of advanced techniques in data preprocessing, feature selection, and ensemble classification, the proposed approach addresses the unique challenges associated with securing IoT networks in urban settings. Leveraging techniques such as SelectKBest, Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA), combined with the Gradient-Based One Side Sampling (GOSS) technique for model training, the methodology achieves high accuracy, precision, recall, and F1 score across various evaluation scenarios.

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Rate 5/6 TCM Code Having 64 States with 64 QAM for Fading Channel

Trellis Coded Modulation (TCM) is powerful technique employed in digital communication systems to improve the reliability and efficiency of data transmission. TCM combines error control coding and modulation schemes to achieve superior performance in challenging channel conditions. In TCM, information bits are encoded using a trellis encoder, which generates a sequence of encoded symbols. These symbols are then mapped onto a modulation scheme, such as Quadrature Amplitude Modulation (QAM) or Phase Shift Keying (PSK), to create the modulated signal. At the receiver, the received signal is demodulated and decoded using a trellis decoder, which employs maximum likelihood decoding to recover the original information bits. The trellis structure allows for efficient error correction and makes TCM particularly suitable for channels with fading, noise, and other impairments.

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IoT Security Framework Optimized Evaluation for Smart Grid

Modern systems' needs may be satisfied by smart grid technologies. Since we frequently struggle to effectively manage security, the smart grid's capacity is frequently underutilized. Despite the fact that a variety of solutions have been offered for securing the smart grid, the problem still exists that no single solution can entirely protect the environment. We provide a protection architecture for the IoT-connected smart grid. The proposed framework to secure IoT devices for the smart grid includes three complementary approaches. By conducting a rigorous comparative analysis of our proposed solution alongside four existing models, we contribute to the ongoing discourse on bolstering the security infrastructure of the smart grid IoT environment.

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Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

One powerful technique that can offer a thorough examination of the body's internal structure is magnetic resonance imaging (MRI). MRI's lengthy acquisition times, however, may restrict its clinical usefulness, particularly in situations where time is of the essence. Compressed sensing (CS) has emerged as a potentially useful method for cutting down on MRI acquisition times; nevertheless, the effectiveness of CS-MRI is dependent on the selection of the sparsity-promoting algorithm and sampling scheme. This research paper presents a novel method based on adaptive multi-extreme particle swarm optimization (AMEPSO) and dual tree complex wavelet transform (DTCWT) for fast image acquisition in magnetic resonance. The method uses AMEPSO in order to maximize the sampling pattern and minimize reconstruction error, while also exploiting the sparsity of MR images in the DTCWT domain to improve directional selectivity and shift invariance.

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Design and Development of Mathematical and Thermal Load Modelling for Induction Heating Systems

Complex systems can be modelled and their performance can be anticipated with the help of current computers and software tools that are applicable in real-world situations. The IH system is capable of being modelled and put through analysis in two distinct domains. The modelling is compatible with the research and applications in their entirety, including the particular control circuits and converter systems under consideration. The use of electrical modelling makes it possible to analyse the features of the IH load and to examine the variations in the parameters in order to determine the impact of load variations. In order to gain an understanding of the load's flow and thermal distribution, thermal modelling is a crucial component of this study.

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A Study of High Gain DC-DC Boost Converters for Renewable Energy Sources

This paper presents a comprehensive investigation into the various topologies of DC-DC boost converters which are designed for optimal integration with RES like photovoltaic (PV) systems. Photovoltaic applications demand efficient energy harvesting and management to maximize the conversion of solar energy into electrical power. The DC-DC topologies include switched coupled inductor, basic coupled inductor, coupled capacitor with coupled inductor with a snubber circuit, active clamp, high step-up and three-winding dual switches are considered for study. Each topology is analyzed in terms of its suitability for PV applications, considering factors such as efficiency, voltage gain, and reliability.

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Improved Mayfly Algorithm for Optimizing Power Flow with Integrated Solar and Wind Energy

Across the globe, the transition towards sustainable energy systems necessitates seamless implementation of Renewable Energy Sources (RES) into traditional power grids. Such RESs include solar and wind power. The current research work intends to overcome the challenges associated with Optimal Power Flow (OPF) problem in power systems in which the traditional operation parameters ought to be optimized for effective and trustworthy integration of the RESs. The current study proposes an innovative nature-inspired approach by enhancing the Mayfly algorithm on the basis of mating behaviour of mayflies. The aim of this approach is to tackle the complexities introduced by dynamic and discontinuous nature of solar and wind power. The improved Mayfly algorithm aims at minimizing power losses, emission, optimize voltage profiles, and ensure reliable integration of solar and wind power.

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Enhancing Performance of Power Allocation for VLC Networks by Non-Orthogonal Multiple Access-MIMO

Visible light communication, or VLC, networks emerged as a viable option for data access, particularly indoors. Their cost-effectiveness, protection to radio frequency (RF) intrusion, and extraordinarily high data speeds make them a desirable option for the upcoming generation of indoor networking technologies. In this paper, we propose the Exponential Gain Ratio Power Allocation (EGRPA), an effective and low-complex power splitting method, to increase the attainable sum amount in Multiple Input Multiple Output (MIMO) VLC downlink networks. Using numerical simulations, we assess the enactment of an indoor 2x2 MIMO VLC downlink model for several users.

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Modelling of IPFC with multifunctional VSC for low-frequency oscillations damping and system stability improvement

The unified power flow controller (UPFC) approach maximizes active power transfer with the least amount of losses by independently controlling both reactive and active power flow. This makes it possible to use individual transmission lines more effectively. The interline power flow controller (IPFC) utilizes the concept of UPFC for economic operation and control, management of multiline transmission systems. In its most basic form, the IPFC consists of many DC to AC converters such as voltage source inverters (VSCs), each of which performs the same purpose as the UPFC: providing series compensation for every line in multiline transmission system. A novel idea for the efficient power flow control management and compensation in multiline transmission system is the IPFC. This research proposed a backup controller for an effective modelling of IPFC in order to reduce low-frequency oscillations using four different damping controller options.

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Exact Computing Multiplier Design using 5-to-3 Counters for Image Processing

This work presents a novel approach to improve the area and energy efficiency of 5:3 counter, a key element used in digital arithmetic. To provide an effective substitute for addition operations, mostly in the partial product reduction stage of larger multipliers, this study suggests a new 5:3 counter. The Input Shuffling Unit (ISU) is employed within the proposed 5:3 counter to minimize gate-level implementation and path delay during partial product reduction in 16-bit and larger multipliers, thereby enhancing area and energy efficiency. Consequently, there are 84% fewer choices of input-output combinations, thereby decreasing the circuit complexity with respect to area and energy usage.

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Reliability Improvement of Grid Connected PV Inverter Considering Monofacial and Bifacial Panels Using Hybrid IGBT

The development of bifacial photovoltaics has led to significant advancements in solar energy. Unlike traditional solar panels, which only generate electricity from the front side, these panels capture the energy from the rear and front surfaces. Bifacial photovoltaics utilize a dual-sided absorption to capture the sunlight that falls on nearby structures and the ground. This technology helps boost their efficiency and makes them an economical and sustainable choice. Furthermore, the increased energy production from the rear side of bifacial panels may lead to higher voltage fluctuations, which affects the thermal stability of PV inverter. Nevertheless, PV inverter is regarded as critical component which affects the reliability performance. Hence in this paper reliability improvement methodology with hybrid IGBT is proposed for the PV inverter.

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Risk Assessment of Radial Distribution Systems using Modified Jelly Fish Search Algorithm to analyse the Performance Indices

One of the essential techniques for figuring out Power Distribution System performance is reliability evaluation. With time, the range of methods for assessing reliability has grown, and the distribution system's evolution has also become more intricate. The likelihood of a network failing grows with time once it begins to function, especially if it is used for an extended period. Reliability indices have been evaluated using different algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and various modified versions of algorithms. The Jelly Fish Search Algorithm has been used in various power system applications such as to determine the most cost-effective way to dispatch generating units' loads, integrate Distributed Generation (DG) units, track the maximum power of photovoltaic systems, and determine optimal power flow solutions, among other uses.

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A Switched Z-Source and Switched Capacitor Multi-Level Inverter Integrated Low Voltage Renewable Source for Grid Connected Application

Most of the renewable sources generate power at lower voltage levels in the range of 20-50V which cannot be utilized by the loads. Therefore, stacking multiple modules in series increases the voltage level or using conventional boost converter or QZSis helpful. However, due to series stacking and boost converter or QZS there is a great power loss and also have reliability issues.The QZS inverter has very less boosting gain in the range of 2times. Theconventional boost converter or QZSis replaced with SZSC for voltage boosting and inverter operation. The SZSC boosts the voltage 4-5 times to the input voltage level. For further mitigation of harmonics, the conventional 6-switch inverter is replaced with switched capacitor MLI. Multiple renewable sources are at the input which include PV array, battery unit and PMSG wind module.

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Application of Many-Objective Arithmetic Optimization Algorithm and TOPSIS for Optimal Planning of DGS in Distribution Systems

The traditional planning of distribution networks is changing because of the accelerated expansion of distributed generation (DG) technologies in various capacities and forms. However, the improper integration of DGs in current distribution networks can give rise to several technical difficulties despite the advantages provided by distributed generation technologies. This paper presents the optimal DG planning in the distribution system using a Pareto-based many-objective arithmetic optimization algorithm (MOAOA) for optimal DG planning problems in the distribution system. This work focuses on improving four technical metrics related to distribution systems: mitigation of electrical energy not served (EENS), total voltage deviation (TVD) minimization, voltage stability index (VSI) maximization, and energy loss mitigation.

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VSC-STATCOM Performance Under Different Fault Sensing using PSO Tuned Hybrid SMC

In this paper, we investigate the PSO-tuned hybrid SMC performance based VSC-STATCOM under different conditions of fault using hybrid renewable energy sources (HRES). A hybrid renewable energy resource system (HRES) consists of PV, wind power, and batteries. Here the Irradiance is the PV input and the wind energy is Wind Input. The storage of energy is used for battery. The battery is used for changing weather condition or the changing the condition of the environment. Hybrid VSC-STATCOM controller based on SMC to reduce power quality issues like sag, swell, harmonics etc. associated with HRES system mainly due to non-linear load conditions.

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Analysis of Disc Motor with Asymmetrical Conducting Rotor

The homogenous disc rotor construction allows higher rotational speeds and relatively higher power densities. This paper deals with a two-phase induction motor with homogenous disc rotor construction. The field analysis of an axial flux disk motor with a conducting disc rotor is carried out, using a new application of the Maxwell's field equations. A Suggested model is introduced to establish the geometry of motor construction and to enable the derivation of the field system differential equations. A new strategy is applied for the boundary conditions to complete the field solution. This is carried out by determining the complex integration constants.

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Improvement of 5G Core Network Performance using Network Slicing and Deep Reinforcement Learning

Users have increasingly been having more use cases for the network while expecting the best Quality of Service (QoS) and Quality of Experience (QoE). The Fifth Generation of mobile telecommunications technology (5G) network had promised to satisfy most of the expectations and network slicing had been introduced in 5G to be able to satisfy various use cases. However, creating slices in a real-life environment with just the resources required while having optimized QoS has been a challenge. This has necessitated more intelligence to be required in the network and machine learning (ML) has been used recently to add the intelligence and ensure zero-touch automation. This research addresses the open question of creating slices to satisfy various use cases based on their QoS requirements, managing, and orchestrating them optimally with minimal resources while allowing the isolation of services by introducing a Deep reinforcement Machine Learning (DRL) algorithm.

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A Smart Secure model for Detection of DDoS Malicious Traces in Integrated LEO Satellite-Terrestrial Communications

For many researchers, defense against DDoS attacks has always been a major subject of attention. Within the LEO Satellite-Terrestrial (LSTN) network field, distributed denial of service (DDoS) attacks is considered to be one of the most potentially harmful attack techniques. For the facilitation of network protection by the detection of DDoS malicious traces inside a network of satellite devices, machine learning algorithms plays a significant role. This paper uses modern machine learning approaches on a novel benchmark Satellite dataset. The STIN and NSL-KDD datasets has been used to detect network anomalies.

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Forward Node Selection by Evaluating Link Quality Using Fuzzy Logic in WBAN

WBAN technology plays a vital role in human life monitoring and maintaining health remotely without being hospitalized, particularly during pandemic situations. The miniature-sized and heterogeneous sensors involved in WBAN with limited resources face reliability as a key challenge that limits the growth of WBAN technology. Designing an efficient routing protocol helps to achieve reliable data transmission between sensor nodes in WBAN. The proposed Fuzzy logic-based Forward Node Selection chooses the best node to transmit the data by introducing fuzzy logic on routing parameters such as link quality, data rate, node’s residual energy and node-to-node distance. The key advantages of our proposed system are to extend the network lifetime and boost the packet delivery ratio.

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Control for Wind Turbine System using PMSG when Wind Speed Changes

This paper presents the proposed model to control grid-connected wind turbine by permanent magnet synchronous generator (PMSG). With the wind speed changing continuously, the rotor system needs to be able to self-regulate according to wind speed and direction to ensure efficient operation of the turbine. The PMSG was chosen because the magnetic flux is always available thanks to the permanent magnet system glued to the rotor surface. The generator provides power with low rotational speed but high efficiency.

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Impact of Propagation Environment on the Performance of Direction Oriented Forwarding through Minimum Number of Edge Nodes (DOF-MEN) Routing Protocol In Ad Hoc Networks

One sort of wireless ad-hoc network is called MANET (Mobile Ad-hoc Network), which is an autonomous network made up of wireless nodes and routers connected by wireless connections. Transmission of data in effective way is important. The DOF-MEN (Direction Oriented Forwarding through Minimum Number of Edge Nodes) protocol is the protocol which lessens the amount of messages for route discovery. It attempts to choose just single node as the next following hop. The node's address is added by the sender. Therefore, only the chosen node will receive and subsequently transmit the data. This protocol increases the throughput, Packet Delivery Ratio, and cuts down on the Routing Overhead. Several aspects affect the routing protocol's execution accuracy in mobile ad hoc networks (MANET).

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Design and Analysis of Microstrip Sierpinski Fractal Antenna for Wireless application

This paper describes a novel design for a microstrip fractal antenna based on the Sierpinski triangle shape. It is built on a FR4 substrate and operates in the 5.5 GHz frequency range. The proposed antenna is designed and validated using ANSYS Electronic Desktop's High Frequency Structure Simulator (HFSS). The simulated results show good performance in terms of radiation pattern, gain and input impedance. This proposed antenna can be widely used in wireless communication equipment that is progressing towards miniaturization and high frequency.

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Derivation of Generalized Design Formulas for Modified Branch-Line Coupler with the Second Harmonic Suppression Characteristics

We propose a modified branch-line coupler composed of the open stubs and stepped impedance lines. This structure allows the second harmonic suppression and size reduction. We present the steps to transform from a conventional branch-line coupler to the proposed structure using equivalent circuits and derive the generalized design formulas. From the results analysis of the design sample, we demonstrate the validity of the derived formulas. The fabricated branch-line coupler provides more than 30 dB the second harmonic suppression and about 30 % size reduction compared to a conventional branch-line coupler.

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A Review on 5G Antenna: Challenges and Parameter Enhancement Techniques

The need for a high-speed mobile network has increased due to the COVID19 pandemic.5G is the newest and most sophisticated technology designed to handle the demands of the internet. The 5G network ensures connection security and simplifies mobile device connectivity to wireless devices. This paper explains every parameter related to 5G technology that has been covered in various papers. It addresses some of the difficulties that 5G technology faces. The development of Vivaldi, conformal, MIMO antennas satisfies the requirements of the 5G mobile network and presents opportunities to overcome obstacles. In the paper, MIMO antenna is discussed along with various techniques for enhancing its parameters, such as appropriate substrate selection, antenna element placement, and mutual coupling reduction.

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Analysis and optimization of 4G / LTE network pathloss using Particles Swarm Optimization algorithm

This paper aims to optimize the pathloss in 4G/LTE networks obtained by empirical Radio Frequency (RF) propagation models to enhance user access quality. The radio wave propagation models are mainly used to predict the pathloss which are necessary for planning and optimizing wireless communication systems. In this paper, we propose a parametric optimization for loss estimation in a 4G/LTE network leveraging the Particle Swarm Optimization (PSO) algorithm to enhance the performances of this type of networks and decrease their complexity. For this sake, comparison and performance analysis were conducted using different environments such as urban, sub-urban and rural areas. First, we provide an analysis of radio propagation models, namely: Okumura-Hata, Stanford University Interim (SUI) and Ericsson 9999 models that would be used for outdoor propagation in LTE.

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Capacity Optimization of an Isolated Renewable Energy Microgrid Using an Improved Gray Wolf Algorithm

To achieve the goal of allocating the generation capacity of isolated renewable energy system microgrids in a stable, economical, and clean manner, an optimization model considering economic costs, environmental protection, and power supply reliability was established. Compared with the normalization of fixed weight coefficients, a dynamic adaptive parameter method was used in this study to balance the weights of economic, environmental, and stability factors in the objective function. The Levy Flight Strategy, Golden Sine Strategy, and Dynamic Inverse Learning Strategy were embedded to increase algorithm performance for optimization and simulation to address issues such as local optima, slow convergence speed, and lack of diversity commonly associated with traditional Grey Wolf Optimization algorithm.

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An Enhanced Multi-Objective Evolutionary Optimization Algorithm based on Decomposition for Optimal Placement of Distributed Generation and EV Fast Charging Stations in Distribution System

An Enhanced multi-objective evolutionary optimization algorithm based on decomposition (E-MOEA-D) proposed for optimal placement of Distributed Generation (DG) and Electric Vehicle (EV) Fast Charging Station (FCS) in distribution system. The diversity of the evolutionary algorithm improves the convergence and diverse solution in the process of evolutionary optimization. The proposed algorithm is improved using enhanced diversity algorithm, which yield diverse candidate solutions in population. The optimal placement of DGs and FCS are formulated using three objective functions as i) Active power loss ii) Voltage deviation iii) DG cost. The proposed algorithm is simulated on IEEE-33 bus distribution system.

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Design, Fabrication and Performance Analysis of a Compact Unidirectional Quasi-Yagi Antenna for High Gain and High Directivity at 6.2 GHz

Antenna gain, directivity, and radiation efficiency are being enhanced by researchers to satisfy the demands of emerging mobile communication systems. Primarily, the quasi-Yagi antenna satisfies the expanded criterion. This study presents a microstrip quasi-Yagi antenna operating at 6.2 GHz. Enhancements are made to the antenna's gain, directivity, and radiation efficacy. At 6.2 GHz, the antenna was engineered to have a return loss S11 of -36 dB. In addition, from 5.85 to 6.4 GHz, -10 dB return loss was incorporated into its design.

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An IoT based Traffic Control System using Spatio-Temporal Shape Process for Density Estimation

In response to the escalating challenges posed by urban congestion and road accidents, this paper addresses the imperative for advanced traffic control systems in smart cities. However, there is limited research work available in the literature to develop this traffic management system due to unpredictable traffic flow occurring on the road. To overcome this shortcoming in the traffic control system, this paper proposed a novel vehicle density estimation method that considers group of vehicles, availability and applicability of IoT in smart cities provide an efficient medium to handle public safety by using condition-based intensity function that will be a medium to cope with traffic challenges and thus build an intelligent traffic control system.

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Advancing Sleep Stage Classification with EEG Signal Analysis: LSTM Optimization Using Puffer Fish Algorithm and Explainable AI

In this study, we introduce SleepXAI, a Convolutional Neural Network-Conditional Random Field (CNN-CRF) technique for automatic multi-class sleep stage classification from polysomnography data. SleepXAI enhances classification accuracy while ensuring explainability by highlighting crucial signal segments. Leveraging Long Short-Term Memory (LSTM) networks, it effectively categorizes epileptic EEG signals. Continuous Wavelet Transform (CWT) optimizes signal quality by analyzing eigenvalue characteristics and removing noise. Eigenvalues, which are scalar values indicating the scaling effect on eigenvectors during linear transformations, are used to ensure clean and representative EEG signals.

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Innovative Noise Reduction Strategies in Ultrasound Images Using Shearlet Transform and Bayesian Thresholding

Uterine fibroids are prevalent benign tumors affecting women, often diagnosed through imaging modalities such as ultrasound. Ultrasound imaging is a widely used diagnostic modality for uterine fibroid due to its non-invasive nature. However, the images obtained often suffer from speckle noise, which can obscure fine details and complicate accurate diagnosis. Existing methods for removing speckle noise have limitations, including losing texture and edge information and not being able to handle low frequency noises. This paper presents a novel approach for speckle noise reduction by combining Shearlet Transform with Bayesian thresholding. The proposed method aims to achieve superior noise reduction while retaining important image features crucial for accurate diagnosis.

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The potential of rice husks for electrical energy generation in Cambodia

The purpose of this study was to ascertain the electrical potential of rice husk as a viable fuel source for electricity generation in Cambodia. The rice husk potential in Cambodia for each year was determined by analyzing statistical data on rice output from 2000 to 2021. The results indicate a significant 120% improvement in the capacity of rice husk to be transformed into power during a span of 22 years. On average, about 5.4% per year. Annual husk potential was calculated using 2019 statistical data. In 2019, there is a potential of about 1,741 million tons of husks, equivalent to about 864,408 tons of coal, which provides electricity and a potential of about 6,483 GWh and 740,075 MW.

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Error Mitigation in Noma for Underlay CR Networks with Imperfect Successive Interference Cancellation

This study examines the outage probability in a partial relay selection relaying network, an underlay cognitive radio (CR) network, and a non-orthogonal multiple access (NOMA) system. NOMA, which consists of K half-duplex Decode and Forward (DF) relays, is used in the secondary network. These relays are used to enable data transmission to secondary users (SUs) from the secondary base station (SBS). By establishing mathematical formulations, it is feasible to quantify the outage probability that SUs experience while accounting for imperfect successive interference cancellation (i-SIC).

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Enhancing Smart Grid Stability: Data-Driven Predictive Modeling in Distribution Systems

The system's ability to retain the equilibrium state during regular and under disturbance decides the power system stability. The power system stability is highly affected by continuous load variation, voltage variation, frequency variation, power flow variation, topology and the work environment. Hence the stability analysis is made to ensure the acceptable equilibrium state throughout the operation of the power system while meeting the demand. As there has been numerous inclusion of renewable energy sources into the electric network, there occurs challenge to maintain the equilibrium level of this decentralized supply with temporary needs. So to establish this kind of scenario, a Decentralized smart grid control (DSGC) is developed.

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Power Quality Enhancement through Active Power Filters in Radial Distribution System using Pelican Optimizer

In this paper, an application of pelican optimization algorithms (POA) for the enhancement of power quality (PQ) using active power filters (APFs) in radial distribution systems (RDS) is addressed. The harmonics is the main concern of the PQ. Nonlinear loads (NLs) inject the harmonics into the RDS. Here, nonlinear distributed generation (NLDG) is also considered along with NL at two end nodes. By using APFs, the harmonics are minimized to standard limits. Here, APFs are placed with proper size to minimize the harmonics and to improve the PQ. The POA is utilized to optimize the size of APF at proper placement. Inspired by natural processes, the POA has balanced exploration and exploitation characteristics.

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Multi Renewable source system stabilization using ANFIS controller for energy storage module

When a system is operated with multiple renewable sources connected to the same bus, several power quality issues are raised which may damage the devices connected to it. The issues like DC voltage regulation, harmonics in the AC voltages and ripple in the currents of the devices might be a major concern in the system. This compromising power quality can be improved by integrating advanced adaptive controller into the system for stable voltages. For this a multi renewable source system is considered including PMSG wind farm, FC module, PV source and a battery unit energy storage module. The battery unit is a mandatory module which maintains the power exchange and DC link voltage stability. The fuel cell module is a backup unit to the system when the battery unit fails.

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Design and Analysis of 5G Broadband Elliptical Cut Octagon Patch Antenna

In this paper, a 5G Broadband Elliptical Cut Octagon Patch Antenna is designed whose operating frequency band is from 20.82- 22.95GHz, 25.13-28.46GHz. In this antenna, FR4 substrate whose dielectric constant is 4.4 and loss tangent (tan δ) is 0.002 is utilized as substrate. This antenna has a compact size of 15×25×1.6mm3 and has a radiation efficiency of 92.1%. In order to increase the band of frequency of an antenna, two similar elliptical cut octagon patches are added to form a Broadband Antenna. The resultant microstrip patch antenna is a 5G Broadband Elliptical Cut Octagon Patch Antenna.

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An Efficient Load Frequency Control for Multiple Power Systems Using Fuzzy Logic-Proportional Integral Derivative Controller

To ensure that customers receive a steady and dependable supply of electricity, power systems must operate and be under control. One of the main problems encountered during interruptions to the system is irregular electrical power flow through interconnected power areas and frequency aberrations. Therefore, the load frequency control system (LFC) was used to reduce frequency variations and provide a stable power flow in multiple-areas power system. This study presents several techniques for controlling the load frequency in two area power systems employing a combination of fractional order proportional integral derivative (FOPID) and fuzzy logic-proportional integral derivative (FPID) controllers and comparing them to conventional controllers (PID). MATLAB/Simulink is used to simulate the overall system. The error is estimated using the integral of time-weighted squared error (ITSE) goal function.

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Maximum Power Point Tracking Controller of PV System Based on Two Hidden Layer Recurrent Neural Network

Solar energy is one of the most well-known and cutting-edge energy sources in the age of renewable energy. However, because of fluctuating meteorological factors like solar insolation and temperature, the output of a solar photovoltaic system varies greatly. For the effective use of solar energy harvested using solar PV units under different climate factors, the Maximum Power Point Tracking (MPPT) technique is a crucial component that needs to be present. The MPPT system regulates the PV system's output (current and voltage) to give maximal power to the load. Conventional approaches may not efficiently use available electricity and may fail in partial shade conditions. This study describes how to build MPPT for a photovoltaic system utilizing a two-hidden-layer recurrent neural network (THLRNN).

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CGSX Ensemble: An Integrative Machine Learning and Deep Learning Approach for Improved Diabetic Retinopathy Classification

This research proposes an integrated approach for automated diabetic retinopathy (DR) diagnosis, leveraging a combination of machine learning and deep learning techniques to extract features and perform classification tasks effectively. Through preprocessing of retinal images to enhance features and mitigate noise, two distinct methodologies are employed: machine learning feature extraction, targeting texture features like Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM), and deep learning feature extraction, utilizing pre-trained convolutional neural networks (CNNs) such as VGG, ResNet, or Inception.

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Design DC/AC Converter for Renewable Energy Sources

Multilevel inverters are vastly used in power systems and “renewable energy sources” (RES) to provide an AC voltage with a low level of harmonic contents. This paper aims to design a 9-level inverter for RES such as photovoltaic (PV), wind turbines, and fuel cells… etc. The proposed inverter is constructed by 12 IGBT switching devices where all of which are powered by 4 DC sources of 81 V without balancing capacitors to make DC voltage 324 V. A Phase disposition (PD), alternative phase opposition disposition (APOD), and POD with a slight phase shift are the methods of modulations that are used to provide a sinusoidal waveform of the output voltage and current.

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A Novel Flying Robot Swarm Formation Technique Based on Adaptive Wireless Communication using MUSIC Algorithm

This paper presents a novel technique to address the challenge of coordinating swarm flying robots in a leader-follower configuration. A combination of the Multi Signal Classification (MUSIC) estimation algorithm, based on a wireless MIMO array antenna, along with onboard robot control are used for precise route tracking of an individual robot. Employing an array antenna reduces energy consumption for followers in passive mode and reduces computational complexity when measuring the angles of leader angle interferences, which depends on the phase difference of the impinging signal on the antenna elements of the array. Additionally, the angles estimation and beamforming processes, utilizing MUSIC algorithm, form an inner loop that furnishes orientation angles in 3D (Azimuth and elevation angles) for both the leader and potential interference sources.

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Deepfake Detection using Integrate-backward-integrate Logic Optimization Algorithm with CNN

The emergence of deepfake technology has spurred the need for robust and adaptive methods to detect manipulated media content. This study explores the integration of the Integrate-backward-integrate (IbI) Logic Optimization Algorithm with Convolutional Neural Networks (CNNs) for enhanced deepfake detection. The proposed approach involves a multi-phase iterative process: the CNN initially trained on a diverse dataset encompassing both real and deepfake images. The CNN serves as the foundation for the IbI-driven optimization. The integration phase employs the trained CNN to forward-integrate images, classifying them as real or deepfake. Subsequently, the IbI Logic Optimization Algorithm engages in the backward phase, utilizing feedback from the CNN's performance to iteratively refine the network's parameters, architecture, and feature extraction capabilities. This iterative optimization process aims to adaptively enhance the CNN's ability to discern subtle nuances between authentic and manipulated visuals.

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A Cascaded H-Bridge Multilevel Inverter with DC Cells Input Fault Tolerance Capability Based on PSC-PWM Control

Multilevel inverters have proven their efficiency in generating better AC voltage outputs. This functional quality is mainly due to the use of multi-source DC inputs. Despite the fact that this type of topology is generally reliable, switching faults caused by the complete loss of a switching component or DC input cell may still occur. Such incident may inflict heavy impacts to the conversion chain resulting in a permanent damage to the switching cells or the connected load. This article presents a dynamic switching control strategy capable of tolerating DC cells open-circuit input faults in basic symmetric Cascaded H-bridge multilevel inverter architectures.

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Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency

This research proposes a novel approach for efficient resource allocation in wireless communication systems. It combines dynamic neural networks, Proximal Policy Optimization (PPO), and Edge Computing Orchestrator (ECO) for latency-aware and energy-efficient resource allocation. The proposed system integrates multiple components, including a dynamic neural network, PPO, ECO, and a Mobile Edge Computing (MEC) server. The experimental methodology involves utilizing the NS-3 simulation platform to assess latency and energy efficiency in resource allocation within a wireless communication network, incorporating an ECO, MEC server, and dynamic task scheduling algorithms. It demonstrates a holistic and adaptable approach to resource allocation in dynamic environments, showcasing a notable reduction in latency for devices and tasks. Latency values range from 5 to 20 milliseconds, with corresponding resource utilization percentages varying between 80% and 95%.

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