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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Seyedamirhossein Talebi Salar Sadeghian Seyed Fariborz Zarei Department Electrical and Computer Engineering North Carolina State University North Carolina State USA School of Electrical Engineering Iran University of Science and Technology Tehran Iran Department of Electrical and Computer Engineering Qom University of Technology (QUT) Qom Iran

ISBN: (数字)9798331525132

ISBN: (纸本)9798331525149

The Dual Active Bridge (DAB) converter is one of the suitable isolated structures for high-power applications. With the increasing demand for energy, consumers such as electric vehicles and similar devices require significant electrical power. Due to the limitations of power handling in switching converter components, such as the maximum current and voltage ratings of semiconductors, it has become necessary to connect multiple converters in a modular configuration to achieve the required total power. However, this approach can result in power imbalance among the modules due to non-idealities and mismatched component characteristics in each module. In this paper, by modifying the inductor of the DAB converter, power balance between the modules is achieved. Finally, two DAB converter modules providing a total power of 10 kW are simulated and verified in MATLAB software.

关键词： Inductance Switching converters Bridge circuits DC-DC power converters Voltage Transformer cores Transformers Software Inductors MATLAB

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A machine learning based power load prediction system for smart grid energy management

A machine learning based power load prediction system for sm...

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2023 International Conference on Recent Advances in science and engineering technology, ICRASET 2023

作者： Jyothi, S. Mallikarjunaswamy, S. Kavitha, M. Kumar, Narendra Sharmila, N. Kavya, B.M. Global Academy of Technology Dept of Computer Science and Engineering Bengaluru India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India Rns Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology University Dept of Electrical and Electronics Engineering Mysore India Ks Insititute of Technology Dept of Electronics and Communication Bengaluru India

ISBN: (纸本)9798350306927

In recent years, the electricity crisis has intensified due to the more electric applications, electrical vehicles, and automotive industries that have been developed. Due to this prediction, power generation, utilisation, and facilities in individual houses, areas, districts, states, and countries are very challenging tasks in a renewable-based smart grid. In conventional methods, they have predicted power utilisation and consumption using advanced metering infrastructure (AMI), big data (BD), artificial neural networks (ANN), and the Internet of Things (IoT), but using these methods has been inaccurate. To overcome these drawbacks, a proposed power management system for the smart grid uses machine learning (PMS-ML) to efficiently determine power usage, generation, and faults as compared to conventional methods. As per the simulation results, the proposed PMS-ML shows better performance as compared to conventional methods with respect to prediction, RMSE, and MSE of 0.25%, 013%, and 0.45%, respectively. © 2023 IEEE.

关键词： Neural networks

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Automatic Frequency Control for Interlinked DSTS Power Grid Using Dragonfly algorithm tuned PID Controller 20

Automatic Frequency Control for Interlinked DSTS Power Grid ...

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20th IEEE India Council International Conference, INDICON 2023

作者： Boopathi, D. Jagatheesan, K. Satheeshkumar, R. Samanta, Sourav Anand, B. Jaya, J. Paavai Engineering College Electrical and Electronics Engg Tamilnadu India University of Burdwan Computer Science Engg West Bengal India Hindusthan College of Engineering and Technology Electronics and Communication Engg Tamilnadu India

ISBN: (纸本)9798350305593

This article discussed the automatic frequency fluctuation control in an interlinked power network. Power quality issue arises at the time uncertainty in the loading. The power network is framed by two unequal power sources for investigation. By the complexity of the network the maintain the power quality is too much risk. Area one consists of a dish Stirling thermal system (DSTS). Area two has a thermal power plant alone. For automatic frequency stability control, the Load frequency control (LFC) scheme is presented with a secondary controller. In this study proportional integral derivative (PID) controller is recommended. The parameters of PID are optimized by the Dragonfly algorithm (DA) using Integral Time Absolute Error (ITAE) cost function. The DA technique supremacy is proved by comparing the results of the Ant colony optimization (ACO), Differential evolution (DE), Genetic algorithm (GA), and Particle swarm optimization (PSO) technique with DA. The DA - PID controller response is better than the GA, ACO, DE, and PSO techniques. The control response proved the quick oscillation control at 16s, also very minimal peak values. © 2023 IEEE.

关键词： Ant colony optimization technique Load frequency control PID controller secondary controller system stability

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LISAC: Learned Coded Waveform Design for ISAC with OFDM

LISAC: Learned Coded Waveform Design for ISAC with OFDM

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IEEE Conference on Wireless Communications and Networking

作者： Chenghong Bian Yumeng Zhang Deniz Gündüz Department of Electrical and Electronic Engineering Imperial College London Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We propose a novel deep learning based method to design a coded waveform for integrated sensing and communication (ISAC) system based on orthogonal frequency-division multiplexing (OFDM). Our ultimate goal is to design a coded waveform, which is capable of providing satisfactory sensing performance of the target while maintaining high communication quality measured in terms of the bit error rate (BER). The proposed LISAC provides an improved waveform design with the assistance of deep neural networks for the encoding and decoding of the information bits. In particular, the transmitter, parameterized by a recurrent neural network (RNN), encodes the input bit sequence into the transmitted waveform for both sensing and communications. The receiver employs a RNN-based decoder to decode the information bits while the transmitter senses the target via maximum likelihood detection. We optimize the system considering both the communication and sensing performance. Simulation results show that the proposed LISAC waveform achieves a better tradeoff curve compared to existing alternatives.

关键词： Deep learning Recurrent neural networks Transmitters OFDM Simulation Bit error rate Receivers Integrated sensing and communication Particle measurements Numerical models

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Visual recognition of wheel hubs with convolutional neural network 3

Visual recognition of wheel hubs with convolutional neural n...

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3rd International Conference on Consumer Electronics and computer engineering, ICCECE 2023

作者： Dai, Yining Fang, Zaojun Zhong, Caiming Ningbo University Faculty of Electrical Engineering and Computer Science NingBo Zhejiang China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology NingBo Zhejiang China

ISBN: (纸本)9798350331578

The traditional recognition methods of wheel hubs are mainly based on extracted feature matching. In practical production, their accuracy, robustness and processing speed are usually greatly affected. To overcome these problems, this paper proposes a recognition method based on convolutional neural network. The basic steps include two parts: wheel image pre-processing and wheel model classification. The image processing method, mainly using the detection algorithm of hough circles, obtains the center coordinates and radius of the wheel. Then it maps the ring-shaped wheel in right-angle coordinates to polar coordinates by the center coordinates and radius. This stepcan extract the ring-shaped feature information of the wheel image and reduce the influence generated by redundant features. Then a network architecture with an improved Resnet is designed to classify the wheel models. Finally, the wheel model recognition algorithm is evaluated, and the effectiveness of the method is verified through the comparison experiments of SVM, KNN and other models. The experiments show that the recognition accuracy can reach about 99.8% for 10 kinds of wheels. © 2023 IEEE.

关键词： Wheels

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Defective Microstrip Structure Based Phase Shifter with Reduced Size for Phased Antenna Array Applications 12

Defective Microstrip Structure Based Phase Shifter with Redu...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: technology and Applications, IDAACS 2023

作者： Khan, Hajra Khan, Shahid Nasir, Jamal Ali Shah, Syed Ahson Dalarsson, Mariana Alibakhshikenari, Mohammad Comsats University Islamabad Department of Electrical and Computer Engineering Pakistan Hanyang University Applied Bioelectronics Laboratory Seoul Korea Republic of School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Stockholm Sweden Universidad Carlos Iii de Madrid Department of Signal Theory and Communications Spain

ISBN: (纸本)9798350358056

In this work, we propose a small-size and low-cost phase shifter based on defective microstrip structure (DMS) technique, with a modified reconfigurable unit cell (MRDMS) for WLAN applications at 5.2 GHz. The phase shifter is made up of an MRDMS unit, created by etching slots on a simple microstrip line with an outline of three bridges. To further increase the phase shift, a T-shaped slot is introduced in the central bridge and PIN diodes on the edges of the outer bridges are used. By switching the diodes on and off, the MRDMS unit cell is capable of operating in a variety of different modes to give a phase shift of 37.71° at 5.2 GHz. Additionally, MRDMS unit cells are cascaded for higher phase shift as required. Cascaded two- and four-cell phase shifters were designed and simulated in HFSS and a parametric study on various parameters was conducted to show the detailed operating properties of the proposed phase shifter. Compared to previously reported research, the present study shows that two cascaded phase shifters introduce 75.08° phase shifts and 151.75°, respectively. To the best of our knowledge, this is the maximum phase shift obtained by a phase shifter based on MRDMS. © 2023 IEEE.

关键词： defected microstrip structure phase shifter reconfigurable microwave system

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Transforming Data Analysis through AI-Powered Data science 2

Transforming Data Analysis through AI-Powered Data Science

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Kumar, V. Mathan Vijayaraghavan, P. Meshram, Vidula V. Sharma, Mohit Kumar Nithya, M.S. Kumar, Ritesh Department of Computer ScienceKarpagam Academy of Higher Education Coimbatore641021 India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350308846

AI-powered records science is revolutionizing the way facts are analyzed and understood. It can significantly improve the exceptional of information evaluation and boost its speed. AI-powered facts technological know-how enables access to more extensive, extra complicated information sets, faster insights, faster trouble solving, and higher choice making. Using the use of AI-powered information technological know-how techniques and tools, organizations can provide more accurate outcomes with shorter times to choices. AI-powered facts technology also offers more correct predictions of activities and developments and superior accuracy in sample reputation. Furthermore, AI-powered facts science techniques can help agencies discover anomalies in datasets, learn new patterns, and better understand the points. This paper discusses the advantages of AI-powered statistics science and how it may enhance data analysis. It additionally discusses a number of the challenges going through organizations in imposing AI-powered records science projects and gives excellent practices for deploying and coping with records technology projects. © 2023 IEEE.

关键词： Challenges Deploying Number Practices technology

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Sustainable augmented framework using smart sensors with ATM inspired 5G technologies for enterprise networks

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International Journal of Information technology (Singapore) 2025年第3期17卷 1805-1812页

作者： Kagi, Shivakumar Venugopal, Anita Ramaiah, V. Subba Panda, Sunita Ambikapathy, A. MB, Abhishek Department of Computer Science and Engineering Sharnbasva University Karnataka Kalaburagi India IT Unit Dhofar University Salalah Oman Department of CSE Mahatma Gandhi Institute of Technology Telangana Hyderabad India Department of Electrical Electronics and Communication Engineering GITAM School of Technology Bengaluru Campus Karnataka Bengaluru India Department of Electronics and Communication Engineering ITS Engineering College Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru India

The primary driving force behind the development of fifth-generation (5G) applications is the necessity of Concurrent Multipath Transfer (CMT). Nevertheless, CMT systems that rely on Transport Control Protocol/Internet Protocol (TCP/IP) will continue to be the key point in 5G as multipath transport protocols are not often utilized. In today’s worldwide networks, integrating TCP/IP with asynchronous transfer mode (ATM) is a difficult structural problem since the TCP/IP protocol has to accommodate the special features of ATM. An effective mechanism for the effective use of both approaches will be a detailed characterization of TCP/IP performance via ATM. ATM network performance may be negatively impacted by improper use of TCP error and flow control methods, which were created for best-effort networks. In order to facilitate effective communication, this article offers a comprehensive and standardized examination and analysis of TCP/IP over ATM in the context of the 5G network architecture. It also proposes a tuned energy efficient transmission control protocol (TEETCP) to enhance Quality of Service (QoS) in TCP/IP applications. The performance of the approach is validated against existing recent works based on evaluation metrics such as packet delivery ratio (PDR), average throughput (AP), end-to-end delay (e2e), utilization of energy and computation overhead. The results indicate the better performance of our proposed work in all aspects. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： 5G ATM network CMT Network performance TEETCP

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Predictive Analysis of Wildfire Smoke-Induced Wiggle Effect on Low-Inertia Trending Power Grids

Predictive Analysis of Wildfire Smoke-Induced Wiggle Effect ...

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作者： Ali, Amjad Jebril Zhao, Long Kapourchali, Mohammad Heidari Lee, Wei-Jen The Department of Electrical Engineering and Computer Science South Dakota School of Mines and Technology Rapid CitySD57701 United States The Department of Electrical Engineering University of Alaska Anchorage AnchorageAK99508 United States The Electrical Engineering Department University of Texas at Arlington ArlingtonTX76019 United States

By the end of 2021, the United States had installed a 92.5 gigawatts of solar systems. Simultaneously, the rise of inverter-based resources (IBRs) has resulted in a noticeable decline in power grid inertia, which poses a risk to frequency stability in power systems. With the increasing prevalence of wildfires worldwide, it is crucial to examine the impact of wildfire smoke on solar systems and its implications for power grid operations. This study explores the oscillatory power output of PV systems named as "Wiggle Effect," a phenomenon observed in PV systems during days affected by wildfire smoke. Distinctive from impact of cloud cover on PV systems, wildfire smoke covers much more landmass and can cause regional impacts on power grids. Additionally, wildfire smoke lasts for a longer time. Therefore, this study investigates the oscillatory patterns of PV power output, which have the potential to jeopardize the frequency stability of the power grid due to sudden fluctuations in power generation. The study investigates this effect and its impact on power system stability, focusing on power systems characterized by low inertia trends. Understanding the influence of wildfire smoke on power grid operations is essential for system operators to develop effective frequency control practices. By gaining insights into the Wiggle Effect, operators can enhance the stability and reliability of power grid operations, mitigating the risks associated with wildfire smoke episodes. The study’s findings contribute to a comprehensive understanding of this issue, facilitating informed decision-making in managing power systems in the presence of wildfire smoke. © 2023 IEEE.

关键词： Decision making

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Deep Learning for UAV Detection and Classification via Radio Frequency Signal Analysis

Deep Learning for UAV Detection and Classification via Radio...

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International Conference on Mobile Data Management

作者： Prajoy Podder Maciej Zawodniok Sanjay Madria Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Department of Computer Science Missouri University of Science and Technology Rolla MO USA

ISBN: (数字)9798350374551

ISBN: (纸本)9798350374568

Unmanned Aerial Vehicles (UAVs) are advertised as great tool that benefits society and humanity. However, UAVs also pose significant security threats ranging from privacy invasions, to interfering with commercial aircraft landing and takeoff, to accidently crashing into vehicles or people, to military or terrorist attacks. Consequently, there is a pressing need to detect and identify UAVs to mitigate such potential risks. While image-based methods are crucial for UAV detection, radio frequency (RF) emissions offer additional valuable insights. Analyzing RF signals, such as those used in UAV-ground station communications, can provide information about UAV types based on distinct frequency usage or communication patterns. This work introduces a deep-learning-based approach for recognizing and identifying UAVs using their RF emissions. Captured RF signals are transformed into spectrograms, which are subsequently analyzed using deep neural networks. Existing methods achieve low identification accuracy, for instance the ResNet-50V2 model achieves an accuracy of 85.39% even in controlled, laboratory, noise-free conditions. Moreover, in outdoor environments at distances of 50m and 100m, the accuracy drops to 68.90% and 56.88%, respectively. To improve classification accuracy in outdoors, a CNN model was developed, yielding an accuracy of 78.12%. Leveraging the ResNet 50 V2 architecture, remarkable accuracy of 95.08% was attained in binary classification tasks involving a dataset comprising 195 mixed UAV images and 290 non-mix UAV images.

关键词： Radio frequency Deep learning Meters Training Accuracy Terrorism RF signals

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