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7th International Youth Conference on Radio Electronics, electrical and power engineering, REEPE 2025

作者： Soliman, Ismail A. Tulsky, Vladimir El-Ghany, Hossam A. Abd National Research University 'MPEI' Electrical Power Systems Department Moscow Russian Federation Tanta University Faculty of Engineering Electrical Power and Machines Engineering Department Tanta Egypt

ISBN: (纸本)9798331531836

The worldwide shift toward electric vehicles (EVs) aims to mitigate climate change and reduce pollution. Advancements in battery technology have significantly boosted the EV industry, accelerating the transition from hybrid to fully electric vehicles. To sustain this growth, the widespread availability of EV charging stations (EVCS) is essential. However, integrating EVCS into the distribution network introduces challenges, including potential power system constraints and quality standards violations. This study proposes an optimal approach for integrating EVCS into distribution systems while adhering to power quality constraints, such as minimizing energy losses, enhancing voltage stability, and improving the system power factor. A Genetic Algorithm is employed to define the optimal locations for EVCS, Vehicle-to-Grid (V2G) allocation, and V2G power factor. Simulations are conducted using the radial system IEEE-33 bus, along with load curves and EV charging/discharging profiles, demonstrating the proposed approach's effectiveness in improving voltage stability and reducing energy losses. © 2025 IEEE.

关键词： charging stations electric vehicle optimal allocation power quality vehicle to grid

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Optimization of power Supply System of Motorway Energy Consumers through Reliability Analyses

Optimization of Power Supply System of Motorway Energy Consu...

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2023 International Conference on electrical, Computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Pašalić, Fuad Batalović, Mirza Smaka, Senad Berberović, Sead Matoruga, Mirza Int Bh Sarajevo Design and Development Department Sarajevo Bosnia and Herzegovina Faculty of Electrical Engineering Department of Electrical Power Engineering Sarajevo Bosnia and Herzegovina Faculty of Electrical Engineering and Computing Department of Electrical Engineering Fundamentals and Measurements Zagreb Croatia Elektroprenos - Elektroprijenos BiH Sector for Power Lines Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350322972

This paper presents assessment of selected concept of medium voltage (MV) and low voltage (LV) power supply system of motorway energy consumers/systems on the Pan-European motorway corridor, branch Vc, in Bosnia and Herzegovina (BiH) through methods of reliability analyses. Assessment of selected concept is performed through comparison with different, enhanced and reduced, possible MV network and MV/LV transformer substation configurations. Comparison and analysis of different configurations have been made using of serial, parallel, serial-parallel and delta-star reliability model and Fault tree model. Objective was to confirm that selected concept offers optimized technical solution which provides reliable power supply, failure rate of the supply system as low as possible, together with relative simplicity of design. The results from the selected approach of reliability analyses indicate that this power supply concept entirely based on direct power supply from the primary transformer stations 110/x kV, which ensures two-sided MV power supply of MV/LV transformer substations and their redundant configuration provides reliable power supply of motorway consumers. © 2023 IEEE.

关键词： Stars

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Data-Driven Approach for Condition Monitoring and Improving power Output of Photovoltaic systems

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Computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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Patient Data-Driven Scratch Intensity Classification with LSTM 2

Patient Data-Driven Scratch Intensity Classification with LS...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Jafri, Muhammad Jawad Khan, Fawad Salam Ahmed, Sajjad Amjad, Furqan Air University Faculty of Computing and Ai Department. of Creative Technologies Islamabad Pakistan The University of Larkano Faculty of Engineering Sindh Pakistan Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia

ISBN: (纸本)9798331542573

Everyone is curious about knowing functionality of human body. There are many facts and figures that make's human body center of interaction. Our body consist of bone and muscles. Our body contain around 600 muscles. Those muscles have its unique functionality in our body. When our body is attacked by unknown bacterial infection then our skin reacts and we start itching on particular area. Sometime itching is temporary but some time its causes major problems. In this paper we are using EMG data to identify scratch intensity which help us to identify that itching is normal or contain major issues behind them. Artificial Intelligence is one of great invention of human. Its give power to machine to do task just like human with the passage of time Artificial intelligence show great progress to learn new think so far and improving himself leaps and bounds with their prior knowledge. For example, Neural Network. Especially in the Medical Field we will exam that use of deep learning model increase because of their progress. In this paper we are use LSTM Model to quantification of Scratch intensity of patient at real time data and getting desire results. © 2024 IEEE.

关键词： Deep neural networks

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural computing and Applications 2025年第17期37卷 11583-11605页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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Automatic Generation Control for a Two-Area Multi-Source power System with Renewable Energy Using the Komodo Mlipir Algorithm

Automatic Generation Control for a Two-Area Multi-Source Pow...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Windiarto Nappu, Muhammad Bachtiar Hasniaty, A. Arief, Ardiaty Hasanuddin University Electricity Market and Power Systems Research Group Department of Electrical Engineering Faculty of Engineering Gowa92119 Indonesia

ISBN: (纸本)9781665471640

This paper represents the use of the Komodo Mlipir Algorithm (KMA) optimization method for automatic generation control (AGC) of a two-area multi-source power system with renewable energy sources (thermal, gas, hydro, and wind). Its aim is to enhance the system's dynamic responses. The FO-PID controller is utilized to acquire KMA-optimized controller parameter values using the integral of time weighted absolute Error (ITAE) objective function to demonstrate the proposed approach. In accordance with the system dynamic response, the FO-PID controller surpasses the regular PID and 2DOF-PID controllers. The FO-PID controller's robustness is also tested in both domains with a step load. Finally, the simulation results reveal that KMA outperforms alternative population-based optimization strategies. Thus, KMA can be one of the options as a controller tuning algorithm for AGC system modeling. © 2023 IEEE.

关键词： Automatic Generation Control (AGC) Fractional Order of PID (FO-PID) Integral of Time weighted Absolute Error (ITAE) Komodo Mlipir Algorithm (KMA) Renewable Energy Sources

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Incremental based PSO for optimal power flow considering wind power producers

Incremental based PSO for optimal power flow considering win...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Nappu, Muhammad Bachtiar Waris, Tajuddin Hasniaty, A. Arief, Ardiaty Ajami, Willy Akbar Abdullah, Yusril Ikbal, Rahmat Ramadhani Setiawan, Didin Hasanuddin University Electricity Market and Power Systems Research Group Department of Electrical Engineering Faculty of Engineering Gowa92119 Indonesia

ISBN: (纸本)9781665471640

The optimal power flow (OPF) is a mathematical optimization computation used in electrical power systems engineering to determine the most efficient and cost-effective way to operate a power grid while meeting various constraints and objectives. Furthermore, nowadays, with the increasing assimilation of renewable energy resources like wind and solar power into the grid, the OPF becomes even more critical. These sources are variable and intermittent, and OPF can help manage their integration by adjusting conventional generation and grid infrastructure to maintain system stability and reliability. This manuscript presents how to acquire OPF in power systems with the incorporation of wind power producers (WPP). The objective function of this work is minimizing generation costs and network losses. A modified technique called incremental particle swarm optimization (IPSO) is proposed to resolve the non-convex and non-linear OPF hindrance effectually. IPSO is a variant of the classic PSO in which a new particle is included to the population at each iteration, and the new particle adjusts its position based on information from other particles with "social learning"rules. The WPP outputs are set varies to assess the effectiveness of IPSO OPF for various levels of WPP outputs. Simulations are performed for conventional OPF, PSO OPF, and IPSO OPF and evaluate the efficacy of the proposed method. © 2023 IEEE.

关键词： economic dispatch incremental particle swarm optimization optimal power flow power losses wind power producers

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Transient stability improvement of Croatian power system using FACTS 11

Transient stability improvement of Croatian power system usi...

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11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Šešok, Anica Pavić, Ivica University of Zagreb Faculty of Electrical Engineering and Computing Dept. of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9798350399660

In the last twenty years, the share of renewable energy sources (RES) in total electricity production has increased significantly, which has led to certain problems in power system operation. In systems with a significant share of RES, connected to the transmission network via power electronics devices, the inertial response of the system is reduced. Due to the reduced inertia of the power system, synchronous generators are more sensitive to their disturbed states and as a result, can easily lose stability. Loss of stability of one generator can lead to failure of other system elements, which may ultimately result in partial or even complete breakdown of the power system. Recently, a large increase in production from renewable sources has been recorded in the Croatian power system, therefore this paper investigates their impact on the system's transient stability and the possibilities of its improvement using Flexible AC Transmission systems (FACTS). © 2022 IEEE.

关键词： Flexible AC transmission systems

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Active and Reactive power Control in Asymmetrical Cascaded Multilevel Inverter 3

Active and Reactive Power Control in Asymmetrical Cascaded M...

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3rd International Conference on Emerging Trends in electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Tauseef, Muhammad Fahad Uzair, Muhammad Abbas, Ghulam Power Electronic Systems R&D Ohmitron Inc. Lahore Pakistan Department of Electrical Engineering Faculty of Engineering Islamic University of Madinah Madinah Saudi Arabia Department of Electrical Engineering The University of Lahore Lahore Pakistan

ISBN: (纸本)9798331504861

This paper presents a 27-level multilevel inverter topology employing three H-Bridge modules in conjunction with high-efficiency power conversion. The proposed topology utilizes a cascaded configuration of single-phase H-Bridge inverters, following a 1:3:9 voltage ratio, to produce a 27-level staircase waveform. This grid-supporting inverter can supply reactive power as well as active power, and it has a smooth dynamic response and flexible management of reactive power. The design method is in line with grid compliance norms and improves overall power quality by ensuring minimal harmonic distortion. The modeling and simulation of a 600-kVA inverter have been developed, incorporating a liquid cooling system to enhance thermal management, achieving a high-power density of up to 25 kW/L and a full-load efficiency of 98%. The PLECS environment is used to simulate the proposed complete inverter system under varying conditions. The topology is adaptable for varied power ratings and allows great scalability, according to detailed analysis, making it versatile for a variety of renewable energy and industrial applications. The inverter might greatly enhance the present energy infrastructure and grid stability, as shown by its effective deployment and performance results. ©2024 IEEE.

关键词： Electric power system control

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Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy systems

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Computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque Mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

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