bluuebunny/crossref_metadata_2025 huggingface dataset:
Solana blockchain technology: a review
Authors: Mishra Debani Prasad , Behera Sandip Ranjan , Behera Subhashis Satyabrata , Patro Aditya Ranjan , Salkuti Surender Reddy
Published: July 2024

The introduction of a review article on the Solana blockchain is critical to setting the stage for the arguments and evidence to follow. This paragraph will provide context to the reader by discussing the current state of blockchain technology and introducing Solana as a potential solution. Blockchain technology has the potential for countless applications, ranging from financial transactions to secure data storage. However, existing blockchain systems suffer from scalability issues, were confirmation times and network congestion limit transaction volumes. This review paper on the Solana blockchain is valuable for those seeking an in-depth understanding of the design and efficacy. Given the increasing number of blockchain technologies available in the market, potential adopters face the challenge of selecting the most suitable blockchain network for their specific use case. A well-constructed review provides necessary information on the functioning of the technology, including its strengths and limitations. It also enables readers to compare various blockchain technologies and judge their suitability for their specific needs. Therefore, reviews like this one play a crucial role in helping to advance blockchain technology by driving the adoption of superior blockchain networks.
Alzheimer’s disease diagnosis using convolutional neural networks model
Authors: Samanvi Potnuru , Agrawal Shruti , Mallick Soubhagya Ranjan , Lenka Rakesh Kumar , Palei Shantilata , Mishra Debani Prasad , Salkuti Surender Reddy
Published: July 2024

The global healthcare system and related fields are experiencing extensive transformations, taking inspiration from past trends to plan for a technologically advanced society. Neurodegenerative diseases are among the illnesses that are hardest to treat. Alzheimer’s disease is one of these conditions and is one of the leading causes of dementia. Due to the lack of permanent treatment and the complexity of managing symptoms as the severity grows, it is crucial to catch Alzheimer’s disease early. The objective of this study was to develop a convolutional neural network (CNN)-based model to diagnose early-stage Alzheimer’s disease more accurately and with less data loss than methods previously discovered. CNN, is adept at processing and recognising images and has been employed in various diagnostic tools and research in the healthcare sector, showing limitless potential. Convolutional, pooling and fully linked layers are the common layers that make up a CNN. In this paper, five CNN modelswere randomly chosen (ResNet, DenseNet, MobileNet, Inception, and Xception) and were trained. ResNet performed the best and was chosen to undergo additional modifications to improve accuracy to 95.5%. This was a remarkable achievement that made us hopeful for the performance of this model in larger datasets as well as other disease detection.
Design of a model for multistage classification of diabetic retinopathy and glaucoma
Authors: Mundada Rupesh Goverdhan , Nawgaje Devesh
Published: July 2024

This study addresses the escalating prevalence of diabetic retinopathy (DR) and glaucoma, major global causes of vision impairment. We propose an innovative iterative Q-learning model that integrates with fuzzy C-means clustering to improve diagnostic accuracy and classification speed. Traditional diagnostic frameworks often struggle with accuracy and delay in disease stage classification, particularly in discerning complex features like exudates and veins. Our model overcomes these challenges by combining fuzzy C means with Q learning, enhancing precision in identifying key retinal components. The core of our approach is a custom-designed 45-layer 2D convolutional neural network (CNN) optimized for nuanced detection of DR and glaucoma stages. Compared to previous approaches, the performance on the IDRID and SMDG-19 datasets and associated samples shows a 10.9% rise in precision, an 8.5% improvement in overall accuracy, an 8.3% enhancement in recall, a 10.4% larger area under the curve (AUC), a 5.9% boost in specificity, and a 2.9% decrease in latency. This methodology has the potential to bring about significant changes in the field of DR and glaucoma diagnosis, leading to prompt medical interventions and possibly decreasing vision loss. The use of sophisticated machine learning techniques in medical imaging establishes a model for future investigations in ophthalmology and other clinical situations.
Improving 4G LTE network quality using the automatic cell planning
Authors: Yuhanef Afrizal , Yusnita Sri , Khairani Redha Anadia
Published: July 2024

The growing demand for network services leads to an increase in traffic load on eNodeB, resulting in decreased network quality and performance, necessitating optimization. This research analyses the results of optimising 4G reference signal received power (RSRP), signal to interference noise ratio (SINR) and throughput parameters using the automatic cell planning (ACP) method. ACP has been shown to significantly improve the performance and quality of 4G LTE networks compared to traditional cell planning methods. Based on the standard parameter RSRP, increased after ACP optimisation which is dominant in the range ≥ -100 s.d ˃ -85 dBm and obtained an average value of -98.59 dBm with good category. The average SINR has increased by 18.23 dB with a good category. The dominant throughput is in the 14,000 Kbps range with an average value of 50,241.08 Kbps with the excellent category. The ACP method can enhance the performance of 4G LTE networks, potentially addressing operator issues of unstable network quality due to poor coverage. The ACP method significantly enhances 4G LTE network performance, coverage, and user experience, potentially addressing unstable network quality due to poor coverage. This research is crucial for both users and the telecoms industry.
Blockchain and ML in land registries a transformative alliance
Authors: Shukla Vishnu , Raipurkar Abhijeet Ramesh , Chandak Manoj B.
Published: July 2024

This study presents a novel method for merging blockchain security and machine learning (ML) valuation to update land register systems. The system offers a safe, open, and effective framework for documenting and managing land ownership, addressing issues with conventional land registry procedures. Blockchain technology creates a tamper-proof record by cryptographically combining transactions and time-stamped entries to provide an immutable and decentralized ledger. In addition to building a solid foundation for the land registry system, this strengthens trust. Simultaneously, ML algorithms examine variables such as amenities and location to remove inflated pricing, providing accurate assessments and encouraging openness in the real estate sector. The system has been put into practice and verified in small-scale applications. Its features include enhanced data security, expedited ownership transfers, and accurate asset appraisals. Collaboration between governments, regulatory agencies, and technology suppliers is necessary for widespread deployment. Land registration procedures will change as a result of the revolutionary partnership between blockchain and ML technology, which offers a more effective, safe, and future-ready environment. Accepting this ground-breaking technique establishes a new benchmark for the updating of land ownership data and is a major step toward a more sophisticated and dependable method in the industry.
Medical X-ray images enhancement based on super resolution convolution neural network
Authors: Rani Sharda , Kaur Navdeep
Published: July 2024

Pneumonia is a severe lung infection, chest X-ray (CXR) image preferred to find infection. Real images lost its quality, resolution and other feature due to transmission. So good qualitative datasets are very limited. Quality enhancement in medical images is challenging task for researchers. And quality in clinical diagnosis of any disease in deep learning play a very important role. So, this paper presents an aspect with importance of quality in medical images CXR of a particular dataset and how to enhance and create new images with high quality resolution, that is re-used for classification in deep learning. Super resolution convolutional neural netwok (SRCNN) is deep learning based method, which is used for improving resolution in image. Super resolution means low resolution (LR) images from dataset is to be reconstructed or magnified into high resolution (HR). The objective behind this study is to measure the effect of super resolution with quality index, peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index measure (SSIM). This experinment performed on 200 images with 10 batches, each batch has 20 images from Kermany dataset, select LR images and converted into HR with SRCNN. Then we find PSNR value of image is increase upto 2 to 5 DB, and MSE of qood quality images is near to zero and MSE decrease up to 20-25, SSIM value have little variation due to same pattern is found in input and output images. Enhancement means highlight or improve the region of interest of pneumonic images. Main goal of this study is to preapare a modified dataset which is further used for classification.
Enhancing PI controller performance in grid-connected hybrid power systems
Authors: Pavan Gollapudi , Babu A. Ramesh
Published: July 2024

The optimal operation of a microgrid was buildup of both uncontrollable (solar, wind) and controllable (batteries, diesel generators) electrical energy sources are enclosed in this paper. By replacing controllers, the variations in wavering power supply caused by load fluctuations are managed. The objective of the research paper is to optimize these controller gain settings for effective use of electrical energy. In this paper integral time square error principle is combined along with the Cuckoo search algorithm (CSA) and particle swarm algorithm (PSA) to obtain the accurate, precise and appropriate results. It enhances the microgrid's steady-state sensitive responsiveness in comparison to trial-and-error techniques, assuring a stable supply of electricity to the load.
Face recognition using haar cascade classifier and FaceNet (A case study: Student attendance system)
Authors: Maryuni Susanto Bekti , Surateno Surateno , Jullev Atmadji Ery Setiyawan , Pramulintang Ardian Hilmi , Apriliano Galuh , Wulansari Tanti , Angga Gumilang Mukhamad
Published: July 2024

Face recognition is increasingly widely utilised, and there are numerous face recognition systems. Face recognition is typically utilised for attendance on e-learning platforms in the field of education. The haar cascade classifier is one method for face identification; it is used to identify facial areas. Faces are classified using an alternative model, FaceNet. In this research, we purposefully designed an e-learning platform that authenticates students based on face recognition. Based on the findings of this investigation, the system can accurately recognise faces. Ten students were evaluated based on their participation in two attendance trials. Successful presence has an achievement success value of 19, and 1 failed out of a total of 20 attempts. Several variables, such as illumination, and the use of marks on hats, that could have influenced attendance caused the experiment to fail.
Comparative analysis of heart failure prediction using machine learning models
Authors: Kanakala Srinivas , Prashanthi Vempaty
Published: July 2024

Heart failure is a critical health problem worldwide, and its prediction is a major challenge in medical science. Machine learning has shown great potential in predicting heart failure by analyzing large amounts of medical data. Heart failure prediction with the help of machine learning classification algorithms involves the use of models such as decision trees, logistic regression, and support vector machines to identify and analyze potential risk factors for heart failure. By analyzing large datasets containing medical and lifestyle-related variables, these models can accurately predict the likelihood of heart failure occurrence in individuals. In our research, the heart failure prediction and comparison are done using Logistic Regression, KNN, SVM, decision tree and random forest The accurate identification of high-risk individuals enables early intervention and better management of heart failure, reducing the risk of mortality and morbidity associated with this condition. Overall, machine learning algorithms play a major role in improving the accuracy of heart failure risk assessment, allowing for more personalized and effective prevention and treatment strategies.
IoT based MPPT techniques for photovoltaic frameworks management under different environmental conditions: a review
Authors: Khan Mohammad Junaid , Akhtar Md. Naqui , Alam Afroj , Afthanorhan Asyraf
Published: July 2024

Solar energy (SE) is the most attractive form of renewable energy (RE) source for electrification. To harness SE, the photovoltaic (PV) system is required towards converting sunlight into direct electricity. The PV frameworks can be placed in areas with high energy potential. The performance of PV frameworks is complex work which depends on various parameters of the frameworks and their operations. The performance of PV frameworks can be evaluated using MATLAB/Simulink platform and real-time implementation. In this research article, the internet of things (IoT) is investigated to regulate and monitor PV system performance in various environments. IoT-based maximum power point tracking (MPPT) technology improves the response of real-time operating characteristics which makes it possible to control remote PV systems management, quickly diagnose problems and maintain them effectively. Additionally, it allows for recording production and performance data for analysis.
Misconceptions of metaverse: from etymology to technology
Authors: Putawa Rilliandi Arindra , Izumi Calvina , Sugianto Dwi , Ghaffar Soeltan Abdul
Published: July 2024

The emergence of the metaverse in society is followed by certain confusions, whereas the line between virtual reality and the metaverse remains unclear. Ironically, this has affected the development of the metaverse itself, focusing more on virtual reality while being one of its side components. This has led to the concept losing popularity compared to artificial intelligence technology. This research is a qualitative study that aims to explore the issues at the root of misconceptions and reconstruct the true meaning of the metaverse itself. This research indicates that the misconception already existed when the term was first used alongside virtual reality technology. The term "meta" refers to a higher reality, whereas the terms "digiverse" or "virtuverse" can be used, considering that the terms "digital" and "virtual" can refer to realities lower than the universe.
Bidirectional power flow in an electric vehicle using predictive control algorithm including sneak circuit analysis
Authors: Eti Sri Latha , Sudha Kasibhatla Rama , Lahari Molleti Venkata Pankaj , Sekhar Akkapeddi Chandra
Published: July 2024

The use of electric vehicle battery chargers is becoming more common for transferring power between grid and energy storage system. This paper focuses on providing a comprehensive understanding of various modes of operation of these battery chargers. Two different controllers are used; one is the predictive power controller at the grid end to generate active and reactive power references to operate a 3-phase AC/DC converter connected between the power grid and the DC link and the other is a hysteresis current controller used to operate a reversible DC/DC converter connected between the DC link and electric vehicle battery and is considered as the main component of the energy storage system. Also, sneak circuit analysis is carried out on the DC/DC converter and the controller is designed accordingly. Results from simulations validate the suggested control scheme's viability and efficacy. The adopted topology is validated in real time with dSpace 1104 hardware in the loop prototype operating in different scenarios, both in steady-state and during transients.
Calculation of power losses in a frequency inverter
Authors: Al-Rawashdeh Ayman Yasseen , Dunayev Mikhail Pavlovich , Alzyoud Khalaf Y. , Dovudov Sarfaroz U.
Published: July 2024

This study's main goal is to make a new simulation model of the power losses calculation block for frequency converter power switches that can correctly figure out the transistors and diodes' static and dynamic power losses in a 1.5 kW SIEMENS SINAMICS G110 semiconductor converter (SSG110SC). We use simulation modeling tools in the MATLAB/Simulink environment to look at the semiconductor circuits of a rectifier and an autonomous pulse-width modulation voltage inverter. The study presents analytical expressions describing static and dynamic power losses in power semiconductor diodes and transistors. We used polynomials to get close to the power characteristics of insulated-gate bipolar transistor or IGBTs and then used mathematical expressions to show how they depend on Erec (Ic), Vse (Ic), Vf (If), Eon (Ic), and Eoff (Ic). By utilizing the acquired expressions, a MATLAB/Simulink block was constructed to calculate static and dynamic power losses. as well as power loss dependences on switching frequency and load current, were computed utilizing the developable block system. By comparing the simulation outcomes of the present study to the data provided by the manufacturer, the results were validated. Specific diode and transistor characteristics can be accounted for by the method developed in the present study.
African vulture optimizer algorithm for fuzzy logic speed controller of fuel cell electric vehicle
Authors: Elnaghi Basem E. , Dessouki Mohamed Elshahat , Mohamed Sara Wahied , Ismaiel Ahmed M. , Abdel-Wahab Mohamed Nabil
Published: July 2024

This research article introduces a novel optimization strategy for fuel cell electric vehicles (FCEVs) in order to reduce the integral square error to enhance dynamic performance. African vulture optimizer algorithm (AVOA) improves a speed fuzzy logic controller's (FLC) internal controller settings. The AVOA is renowned for its simplicity in implementation, and low demand on computational resources. The speed drive of FCEV is investigated using MATLAB/Simulink 2023a. The results of FLC-AVOA provide lower settling time, lower overshoot, lower undershoot, and high dynamic response when compared to FLC and proportional-integral (PI) controllers designed using genetic algorithm (GA). The FLC-AVOA reduced the rising time for speed dynamic response by 2.31% and the maximum peak overshoot by 55.23% as compared to FLC-GA.
Real-time advanced sensorless control of axial flux synchronous motor
Authors: Hassan Amir Yassin , Ebrahim Essamudin Ali , Salem Saber Mohamed Saleh , Elzalik Mohamed
Published: July 2024

Interior rotor axial flux permanent magnet synchronous motor (IRAFSM) sensorless control is essential to inject the motor inside the electric vehicles’ tire. The proposed straight-line guided by the reference speed (SLGBRS) sensorless space vector pulse width modulation (SVPWM) inverter-direct torque control (DTC) technique for driving the IRAFSM used for electric vehicles (EVs) is evaluated using laboratory emulator setup. A hardware-in-the-loop (HIL) controller and data acquisition are used as a real-time emulation, while the results are compared with MATLAB simulation results. Both simulation and real-time application of the proposed sensorless control for the IRAFSM offer a good speed response. The results of real-time evaluation are identical with the simulation results so, the control is accurate and suitable for practical applications. Urban dynamometer driving cycle (UDDS) for heavy-duty vehicles is used as a driving cycle for simulation and real time evaluation. UDDS is used to ensure the reliability of the proposed control against a wide range of speed changes to be applicable for many applications. The proposed technique allows utilizing the IRAFSM in many applications that requires less contact and reduced sensors like robotics and inside EV’s tire with a good reliable control.
Enhancing the efficiency of a dual three-phase permanent magnet synchronous motor with modified switching table for direct torque control
Authors: Labchir Fouad , Afia Aziz El , Benkirane Karim , Khafallah Mohamed
Published: July 2024

Conventional direct torque control (DTC) can be served to drive a dual three-phase permanent magnet synchronous motor (DTP-PMSM) by controlling the torque and speed. It relies on the direct application of controlled sequences through the use of a combination of dual hysteresis controllers with a transitioning table. In the course of conventional DTC implementation, there's generation of high armature current with lower-level harmonics, leading to increased losses that affect the machine's effectiveness. To enable a diminishment in these harmonics and consequently enhance the motor's efficiency, an approach to modify the conventional DTC is proposed. Specifically, this strategy involves adapting a new distribution of sectors and substituting the elements of the obtained switching table with synthetic elements. Simulated data validate the effectiveness of the chosen methodology.
Direct torque control of induction motor based on double-power-super-twisting sliding mode speed control for electric vehicle applications
Authors: Mencou Siham , Yakhelf Majid Ben , Tazi Elbachir
Published: July 2024

To improve the performance and energy efficiency of the direct torque control of induction motors used in electric vehicles a double-power-super-twisting sliding mode control (DPSTA SMC) strategy has been introduced in the closed speed loop. This strategy is based on the novel double-power-super-twisting algorithm (DPSTA), which combines the performance of the traditional super-twisting algorithm (STA) with the double power reaching law (DPRL). The stability of the algorithm has been proven using a quasi-quadratic Lyapunov function. The performances of the proposed DPSTA SMC controller have been compared with that of PI, fuzzy logic, and STA SMC controllers. Detailed simulations are carried out using MATLAB/Simulink software. The results demonstrate that this approach effectively improves tracking accuracy, system robustness and energy efficiency, while significantly reducing the chattering phenomenon.
Experimental study of PID for attitude control of a quadcopter using an ESP32
Authors: Moussaoui Ahmed Khalil , Habbab Mohamed , Abdeldjebar Hazzab , Slimane Hireche , Chandra Ambrish , Gouabi Hicham , Abdellah Alhachemi Moulay
Published: July 2024

Aerial robotics encompasses intricate kinematics and dynamics that govern the flight of quad-rotor systems. Among the various methods employed for flight control using microcontrollers like the ESP32 developed by ESPRESSIF; the proportional integral derivative (PID) controller stands out as a widely adopted approach. The ESP32 microcontroller offers a superior interface, delivering enhanced performance and response time, particularly in dynamic environments. This article delves into the implementation and viability of the ESP32 platform for communication with MATLAB/Simulink, as well as real-time data acquisition to control the attitude of quadcopter withe chassis F450. The PID controller was designed to specifically work with the ESP32 platform and rigorously tested on an actual quadcopter during flight operations. lastly, a comprehensive analysis of the data gained and empirical results from the physical model demonstrates that the proposed framework is effective.
Microgrid confrontations and smart resolution
Authors: Modi Sangeeta , Usha Pasumarthi
Published: July 2024

Hybrid microgrids are emerging as an alternate solution for connecting distributed AC/DC energy resources. Effective fault detection and response are highly essential for the microgrid controller (MGC) for protection of the microgrid. The conventional schemes of protection cannot be applied in microgrid because of dynamic conduct and unconventional topology of the microgrids. It is highly essential to develop an appropriate scheme for detection and classification of faults for the effective protection of microgrids. In this paper, a novel and smart solution based on the application of an intelligent machine learning (ML) fine tree algorithm is applied to the hybrid microgrid controller. This algorithm resulted in effective detection & classification of faults which in turn was used for separation of faulty segment. The intelligent model obtained with the proposed algorithm performed well and fault detection accuracy has been showcased for various fault scenarios. The overall fault detection accuracy obtained is 98%. Severity of faults and associated confrontations are also discussed in this work. Performance efficacy of the proposed ML based protection algorithm for MGC is substantiated in MATLAB environment.
Enhancing stability and voltage quality in remote DC microgrid systems through adaptive droop control approach
Authors: Lam Hong Phuc , Nguyen Hung Duc , Pham Minh Duc
Published: July 2024

To ensure the stable and accurate operation of "rural areas”, a reliable power source is necessary, and voltage issues must be carefully considered in power system design to ensure patient safety. Remote DC microgrids provide a viable option for transferring energy across power sources while assuring stability and high efficiency. In this paper, an adaptive droop control approach is developed and compared to the standard droop control method. The suggested technique recommends a dynamic modification of droop coefficients intending to effectively limit the buildup of mistakes in current sharing and departures from the preset voltage setpoints. Through the implementation of the adaptive droop control method, the remote DC microgrid not only enhances current balancing performance but also contributes to a substantial improvement in voltage stability, thereby increasing the overall operational efficiency of the system. Simulation and experimental results on a small-scale remote DC microgrid validate the proposed adaptive droop control approach, proving its effectiveness in the small-scale microgrid system.
A neural network controller and a simple circuit of SVPWM technique to increase five-level VSC STATCOM performance during voltage sag and swell
Authors: Almelian Mohamad Milood , Mohd Izzeldin I. , Aker Elhadi. E. , Omran Mohamed A. , Salem Mohamed , Ahmad Abu Zaharin , Albishti Abibaker A.
Published: July 2024

The most critical disturbance faced in the electrical distribution systems is power service interruptions due to voltage sag or swell which results in economic losses on the user’s side. To compensate voltage sag or swell, advanced custom power devices are used and one of such devices is the static synchronous compensation (STATCOM). This paper presents the implementation of 5-level voltage source converter (VSC) STATCOM using a neural network (NN) and a simplified space vector pulse width modulation (SVPWM) circuit. The primary objective of the NN controller and SVPWM circuit is to enhance the performance and response time of the STATCOM system, specifically in terms of improving voltage and power factor (PF) when faced with voltage sag or swell. The performance of STATCOM was examined within the context of the IEEE 3-bus system. The investigation focused on two scenarios: a single-line-to-ground fault resulting in voltage sag, and the sudden connection of a capacitive load leading to voltage swell. The findings unequivocally demonstrated the efficacy of the STATCOM with a NN controller in comparison to a conventional controller. The utilization of the NN controller resulted in notable improvements in voltage and PF within a remarkably short time frame of 0.02 seconds.
DSP implementation and discretization of phase locked loop methods in presence of grid imperfections
Authors: En-Naoui Ilias , Radouane Abdelhadi , Mouhsen Azeddine , Jarmouni Ezzitouni , Ennajih Elmehdi
Published: July 2024

The fluctuation of grid variables affects the performance of the phase-locked loop, considerably reducing the efficiency of grid energy injection or compensation currents generation during active filtering. The phase locked loop is the main tool for grid synchronization, offering continuous, real-time extraction of grid variables. As these techniques are implemented on digital computers, their discretization and analysis of resource requirements is an important step. This work represents a discretization and implementation on a digital signal processing (DSP) board of two distinct phase-locked loop (PLL) techniques as well as a comparative study of the latter. Our study covers various aspects, including the discretization of the PLLs to be studied, an assessment of the hardware resources required, their implementation on a DSP board, and their effectiveness in quickly identifying grid variables in the presence of imbalance and harmonics, which represent the most frequent grid imperfections.
A new configuration of multilevel inverter to generate higher voltage level with lower components
Authors: Rajavel Premkumar , Asokan Vimala Juliet
Published: July 2024

Nowadays, the demand for cleaner and higher quality electricity supply is increasing among various industries and individual consumers. When compared to conventional two-level inverters, multi-level inverters are becoming more and more common, as these inverters deliver high-quality power with fewer harmonics. Here a new multilevel inverter circuit designed with variable direct current (DC) voltage sources is proposed, this circuit requires limited circuit components, and is compared with the other topologies with the same voltage in the output. The proposed topology requires nine switches in order to generate a single-phase 13-level output voltage without connecting to a polarity-generating circuit. The output voltage level and performance parameters associated with the total harmonic distortion (THD) of the voltage level in the output generated by the proposed multilevel inverter or MLI are evaluated in a MATLAB environment. The final simulation results confirm the behavioral accuracy in the proposed topology while creating all the levels. Also, real-time work is done to verify the operation of the inverter and the results are showcased.
Efficiency enhancement in a switched capacitor cell interleaved buck converter using GaN-FETs
Authors: Kaur Puneet , Jaswal Payal , Gupta Aditi
Published: July 2024

In this paper, a switched capacitor cell buck converter, combining two-phase interleaved operation with gallium nitride or GaN-FET technology has been presented. These technologies make it suitable for wide range of applications where efficient power conversion is essential. To validate efficacy of the proposed converter with GaN-FET, its performance is examined in relation to a similar converter that utilizes metal-oxide semiconductor field-effect transistor (MOSFET) switches. The proposed converter offers a potential advantage over the DC-DC interleaved buck converter with MOSFET switches by allowing operation at higher frequencies, resulting in a smaller inductor value. The design parameters of the inductor have also been presented. The simulation results indicate that the converter exhibits better performance than MOSFET-based converter with regards to total power losses, input and output current ripple, output voltages and subsequently achieves higher efficiency at various duty cycles and different loads. Finally, experimental verification is done to validate the simulation results.
An approach of dynamic wireless charging structure for an electric vehicle based on mutual inductance calculation
Authors: Thangavel Chithras , Krishnamoorthy Vinoth , Jayasankar Vairavasamy
Published: July 2024

A number of reasons, including the ongoing decline in photovoltaic (PV) module prices, the quick rise in electric vehicles (EV) sales, and worries about greenhouse gas emissions, have contributed to the increasing integration of PV with EV charging systems. This electric vehicle can be charged through dynamic wireless charging system while moving on separate charging lane, it will be eliminating the need to stop an hour for the sake of charging in parking stations. By this we can save time while travelling a long distance. Through a mobile app, these lanes can be found and followed. People also can pay through the same app when they are travel in particular lane. According to user needs, additional lanes may be added to highways or existing lanes may be widened to minimize traffic. It might be environmentally benign because a charge controller powers the battery using the solar system. By calculating an exact mutual inductance, we can automate the charging current. A solar panel, battery, transformer, AC to DC converter, regulatory circuits, copper coils are all used in the system. Also, we used here Arduino mega 2560 controller, GSM 900, and LCD display to develop the system. We can measure the state and consumption
A carrier pulse width modulation for asymmetric three-level NPC inverter
Authors: Cuong Quoc Thai Lam , Nguyen Nho Van
Published: July 2024

The three-level neutral point clamped (NPC) inverters are widely used in practice. One of nowadays research trends of multilevel inverter topologies has been reduction of switch number. For this aim, this paper presents an asymmetric three level NPC inverter and study on output performance of phase disposition (PD) carrier pulse width modulation (CBPWM) for different offset voltage functions. A MATLAB/Simulink model of three-level asymmetric NPC inverter is developed to examine the impact of varying the offset voltage on the CBPWM output performances. Total harmonics distortions factors (THD) of voltages and currents are investigated for the whole modulation indices range. The obtained results show that harmonics voltage contents would be advantageous to set in discontinuous pulse width modulation (PWM) methods, particularly at lower voltage range. For asymmetrical topology, simulation results show that switching frequency optimal (SFO)-PWM method has not good performance at low modulation indices and its harmonics content presents an improved at high modulation indices range. Finally, a comparison of the output voltage and current quality via THD index is made between the asymmetric three-level NPC circuit and the conventional three-level NPC circuit are also provided to evaluate the feasibility of the asymmetric three-level NPC inverter in applications.
A level shift carrier based SPWM for reduced switch 5-level multilevel inverter topology
Authors: Nagarajappa Champa Patanegere , Deshpande Abhay Anandarao
Published: July 2024

Multilevel inverters (MLI) seek attention from many researchers these days for high/medium power industrial applications because their output power quality is better than 2-level inverters. This research work presents a detailed comparative analysis of multicarrier level shift (LSPWM) technique implemented on five level conventional and modified multilevel inverters in MATLAB/Simulink software. With the aim of decreasing number of gate drives, switching devices, and DC sources there is a greater focus on emerging multilevel topologies, even though majority of traditional topologies are employed in important application. MLIs have bright future in industry-focused applications, but their size, cost, device count, and switching complexity have hindered their commercial acceptance. Researchers are always creating next generation topologies, or reducing the components and switches used in (RSC) MLIs, to illustrate the shortcomings of MLIs. Conventional five level inverter uses eight semiconductor switches, eight driver circuit and suffers from switching complexity while the proposed symmetrical 5-level smart MLI topology offers reduced quantity of switching elements, gate driver circuits, low cost, space requirement, low dv/dt stress, low switching losses over the traditional topology. The effect of % output harmonic contents are analyzed with phase-disposition and phase-opposition disposition technique for different loads.
A new GaN-based converter design for electric vehicle charging system
Authors: Snehalika Snehalika , Patel Ranjeeta , Abhishek Amruta , Panigrahi Chinmoy Kumar , Pati Ranjan Keshari
Published: July 2024

The research work proposes a gallium nitride (GaN) based isolated bidirectional DC-DC (IBDC)-triple active bridge (TAB) based multi-port converter (MPC) for electric vehicle (EV) charging. The proposed GaN IBDC-TAB based MPC incorporates one input port (Port 1) and two output ports (Port 2 and Port 3) for charging, implying the use of fewer components compared to a conventional dual active bridge (DAB) charging system. The output ports can be operated individually in single active bridge (SAB), Dual active bridge (DAB), and TAB modes. A 12 kW GaN IBDC-TAB based MPC converter is designed for simulation study using LTspice XVII software. In the TAB mode of operation, output Port 2 designated for Level-1 (L-1) charging produces 1.5 kW, and output Port 3 designated for Level-2 (L-2) charging produces 10.6 kW. The maximum efficiency of 98.87% is obtained in simulation for the MPC converter in TAB mode. For experimental validation, a 0.91 kW prototype is presented with Port 1, Port 2, and Port 3 voltages as 230 V, 120 V, and 200 V respectively with the considered switching frequency of 100 kHz. The single-phase-shift control strategy is implemented for controlling the transmitted power in the proposed GaN IBDC-TAB converter.
THD analysis of 15-level multilevel inverter using lesser number of switches with nearest level control technique
Authors: Susheela Nunsavath , Chandra Vilasagaram Revanth
Published: July 2024

The multilevel inverters are considered as the efficient power converters. Because of the predominant properties of multilevel inverters over the two-level inverters, these are acquiring more popularity. The aim of research is to minimize the number of switches as well as sources. In this paper, total harmonic distortion (THD) analysis for different modulation indices is carried out for a 15-level inverter with fewer switches. This 15-level inverter uses three asymmetrical sources with nine switching devices. Nearest level control modulation technique provides the pulses for the switching devices at fundamental switching frequency. Evaluation of THD has been carried out in MATLAB/Simulink.
Advancements in electrical systems for E-bike battery charging: a technical examination of conventional and wireless power transfer technologies
Authors: Bunyamin Wan Muhamad Hakimi Wan , Baharom Rahimi , Munim Wan Noraishah Wan Abdul , Zolkiffly Mohd Zaid , Ahmad Ahmad Sukri
Published: July 2024

Electric bicycles (E-bikes) are becoming key to making transportation more eco-friendly, leading to cleaner air, and lower carbon emissions. The rising popularity of E-bikes calls for innovative battery charging solutions that cater to their specific needs, emphasizing faster charging, high energy efficiency, safety, compact design, smart features, and compliance with international standards. This paper reviews existing and new charging technologies for E-bikes, focusing on their design, charging processes, and safety features. It points out the issues with traditional chargers, such as their negative effects on power quality and grid stability, and introduces wireless power transfer (WPT) as a groundbreaking approach to E-bike charging. WPT enhances convenience by removing the need for physical cables and is seen as a step forward with the integration of power factor correction techniques for better efficiency and energy use. The discussion extends to the future of E-bike charging, exploring emerging technologies that could redefine electric transportation. The study aims to deepen the understanding of E-bike battery charging technologies, their challenges, and future directions, contributing to the advancement of E-bike technology.
Optimizing wireless power transfer efficiency: an empirical analysis of switching frequency variations
Authors: Baharom Rahimi , Hayat Muhammad Amirul Ashraf Zarrul
Published: July 2024

This study explores the impact of switching frequency variations on wireless power transfer (WPT) system efficiency through rigorous experimental analysis. Our tests reveal that lower switching frequencies can enhance system efficiency by up to 30% by reducing resistive losses. These findings establish an optimal frequency range that significantly improves performance. The research integrates empirical data with theoretical models to elucidate electromagnetic principles like the skin effect and its impact on frequency-dependent behaviors. This comprehensive approach not only confirms the experimental methodology but also provides robust numerical evidence, making a novel contribution to the field. The results have significant implications for renewable energy and sustainable technology development, suggesting practical applications in designing energy efficient WPT systems for consumer electronics and electric vehicle charging. This paper quantitatively defines the efficiency benefits of specific frequency ranges, advancing the deployment of wireless power technologies.
AC to DC bridgeless SEPIC converter-based fuel cell emulator
Authors: Prajapati Ashish , Gupta Tripurari Das , Chaudhary Kalpana
Published: July 2024

Fuel cell (FC) technology is still in a growing phase and intensive research is required for its wider applications. However, the high cost of the FC stack is a major hurdle for researchers. This paper presents a new method of FC emulation done by using a Bridgeless SEPIC converter (BSC). The key features of the designed emulator are single stage AC-DC conversion, improved efficiency and nearly unity power factor at the input side. AC-DC BSC is controlled by using the coded model to emulate the behavior of FC. The emulated FC exhibits all three characteristics activation losses, ohmic losses, and concentration losses during operation. The MATLAB/Simulink background is used to implement the FC model and compile it into a C program. The real-time mathematical model is implemented by using a DSP(TMS320F28335) controller. Analysis, modeling, and control strategy of emulated FC are presented, static and dynamic behavior is validated through the 950W laboratory developed prototype.
Development and design of the effect of adding a heatsink as a coolant on improvement output power on solar panels
Authors: Jalil Asri Muhammad , Away Yuwaldi , Nasaruddin Nasaruddin , Sara Ira Devi , Jalil Saifuddin Muhammad
Published: July 2024

This study discusses the use of solar energy as a clean and widely available alternative. The performance of solar panels is influenced by various factors including solar radiation, module, temperature, and distribution of the sunlight spectrum. Temperature issues in solar panels can be addressed through passive cooling, such as installing a heatsink. A passive cooling system is implemented by installing a heatsink with four layers of fins on the body panel of the solar module. The advantage of passive cooling with constellations is that it does not require additional energy from outside, while a large number of fins can achieve the best passive cooling constellation. Each heatsink utilizes eight fins with a total of three heatsinks, manufactured from aluminum. Vector values of wind speed, humidity, and temperature are studied through direct measurements. A 100 W polycrystalline solar module at a standard temperature of 25 ℃ produces an open circuit voltage of 22 +V.
Management and monitoring of lithium-ion battery recharge with ESP32
Authors: Gomez-Huaylla Estefany , Mejía-Cruz Luis , Paiva-Peredo Ernesto
Published: July 2024

Air quality is important for human health, the use of clean energy is one way to improve it. And the management and monitoring of the recharge of ion-lithium batteries used in electric vehicles and other devices requires efficient systems. The objective is to develop an intelligent electrical recharging system for lithium-ion batteries using internet of thing (IoT) technology. In this article, an electrical recharging system for lithium-ion batteries was designed and carried out, which is made up of a source, a diode bridge, L298 n driver, current sensor, a voltage divider sensor and the ESP32 microcontroller. The system determines the storage capacity of the battery and monitor it remotely via WIFI. The data is sent to a Shiftr.io server and graphically displayed on a NODE RED platform. The message queuing telemetry transport (MQTT) protocol is used to communicate the devices and decide the best time to recharge the batteries. The results show that the system works correctly and offer useful information that optimizes the charging process, it contributes to improving savings in the payment of electricity consumption and the use of clean energy. The limitations of the study are the small sample size and the lack of comparison with other similar systems.
Balancing the charge: the evolution of battery active equalizers in shaping a sustainable energy storage future
Authors: Rovianto Eki , Khairunnisa Khairunnisa , Lenggana Bhre Wangsa , Fardan Muhammad Faris , Harsito Catur , Prasetyo Ari
Published: July 2024

As the world embraces sustainable energy solutions, energy storage systems are becoming increasingly critical for the effectiveness of renewable energy sources. Batteries have emerged as a promising option. However, to fully harness the potential of batteries, the challenge of cell must be overcome. This review article delves into the evolution of battery active equalizers on the quest for sustainable energy storage solutions. The review begins by exploring the fundamental principles of battery active equalization and its significance in optimizing energy storage system performance and efficiency. Traditional battery management techniques are limited in their ability to address imbalances effectively, making active equalization a compelling alternative. The review provides a comprehensive analysis of early developments and current state-of-the-art active equalization systems. The reviewed article demonstrates successful applications, showcasing how active equalizers can significantly improve energy storage performance and overall system stability. While active equalization offers tremendous promise, it is not without challenges. The review explores how these technologies seamlessly fit with renewable energy sources and grid systems, opening up possibilities for future energy infrastructure. Industry perspectives play a vital role in realizing innovative technologies. Therefore, the review incorporates insights from leading experts, presenting their viewpoints on the adoption of battery active equalizers.
A character driver of amplifier for the RF-generator 77.78 MHz at cyclotron DECY-13 in Yogyakarta
Authors: Atmono Trimarji , Dwiatmaja Agus , Piero Muhamad Rangga Del , Suntoro Achmad
Published: July 2024

Measurement of power supply for design of experimental cyclotron in Yogyakarta (DECY-13), i.e. RF-generator 77.78 MHz, especially driver amplifier as important part, has been carried out, in order to minimize the reflected power. Starting from direct digital synthesizer (DDS) as exciter of the radio frequency (RF) wave, having power up to 20 W, the signal sent then to the driver, that has an amplifying factor of around 50. Output of driver works as input for final power of designed DECY-13, which needs about 10 kW of RF power supply for operating the cyclotron. Using network analyzer to get the best position of the RF-power-coupler, it was found the value of dee impedance equals to (50.9 - j0.3) Ω. The signal detected has the behavior as capacitively having value of around 6 nF, that showed by smith chart. An output of 0.8 W by DDS has resulted of 300W to the driver. By a pure resistance 50 Ω dummy load, the system can achieve final power of almost 10 kW.