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检索条件"机构=Dept. of Electrical and Computer Engineering and Technology"

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Sensitive Voltage Differential Protection Method for Transmission Lines Compensated with SC

Sensitive Voltage Differential Protection Method for Transmi...

引用

2024 International Conference on electrical and computer engineering Researches, ICECER 2024

作者： Manditereza, Patrick Central University of Technology Free State Dept. of Electrical Electronic and Computer Engineering Bloemfontein South Africa

ISBN: (纸本)9798331539733

New solar PV and wind generation farms are usually connected to the grid through existing transmission lines, while remote generating plants may be connected through newly constructed long transmission lines (TLs) with limited capacity. In both cases, the power transfer capability of the TLs can be increased by introducing series compensation. Introduction of the series capacitor (SC), however, complicates the protection of the TLs rendering the traditional distance protection ineffective. The literature reports on numerous schemes that are grounded in innovative principles for protection of TLs with series compensation. However, most of the techniques are designed for application with specific compensation points along the line - near end, mid-point or far end. The use of a sensitive voltage differential (SVD) algorithm for the effective protection of a transmission line regardless of the placement of a fixed series capacitor, is suggested in this paper. The algorithm generates a sensitivity-based fault detection index whose magnitude depends on changes in the magnitude and phase of the voltages at the SC and line terminals. The performance of the suggested scheme is evaluated through extensive DigSilent PowerFactory simulations, and the results show that the scheme is sensitive to faults over 100 % of the series-compensated TL and is effectively independent of fault resistance. © 2024 IEEE.

关键词： Power transmission lines

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Predicting COPD Severity Using Machine Learning and GOLD Criteria 3

Predicting COPD Severity Using Machine Learning and GOLD Cri...

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Kinikar, Atharva Chandwani, Maanav Rane, Tushar Pune Institute of Computer Technology Dept. Information Technology Pune India Pune Institute of Computer Technology Dept. Computer Engineering Pune India

ISBN: (纸本)9798350381931

Breathing problems that get worse over time are a symptom of chronic obstructive pulmonary disease, or COPD. It is a catch-all phrase for a number of conditions including asthma, bronchitis, and emphysema. It is defined by a progressive constriction of the airways and an early aging of the lungs. Experts have attested to the fact that early identification helps lessen the symptoms of COPD. Thus, using GOLD criteria, patients' COPD severity can be predicted using contemporary methods like machine learning algorithms. The proposed prediction models predict severity of COPD of the patient on a scale of 1 to 4 as prescribed by the GOLD system. Our model was trained to utilize a derived feature set and machine learning classifiers, such as SVC, Random Forest Classifier, XGBoost classifier, Naive Bayes, and Hard Voting Ensemble, with the best possible parameter choice to achieve a commendable accuracy of 97.6%. The global COPD epidemic will be lessened with the use of this method and medical professionals' experience. © 2024 IEEE.

关键词： Machine learning

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Emotion Recognition from Video Frame Sequence using Face Mesh and Pre-Trained Models of Convolutional Neural Network 24

Emotion Recognition from Video Frame Sequence using Face Mes...

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24th International Seminar on Intelligent technology and Its Applications, ISITIA 2023

作者： Adi, Derry Pramono Yuniarno, Eko Mulyanto Wulandari, Diah Puspito Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350313956

Emotions are a collection of subjective cognitive experiences and psychological and physiological characteristics that express a wide range of feelings, thoughts, and behaviors in human interaction. Emotions can be represented through several means, such as facial expressions, tone of voice, and behavior. Deep Learning (DL) research has focused on incorporating facial expressions. Images with facial expressions are commonly used as data input for the DL model. Unfortunately, most DL models in Facial Emotion Recognition (FER) use static images. This method does not take into consideration all conceivable facial expressions. The static image of facial expressions is insufficient for recognizing emotions, but a sequential image from a video is required. In this study, we extract MediaPipe's face mesh feature, the state-of-the-art multidimensional expression key points embedded in the video image sequence. Furthermore, we feed sequence image data into the pre-trained Convolutional Neural Network (CNN) model. The data we used is from The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) with the emotion classes of 'Anger,' 'Fearful,' 'Happy,' and 'Sad.' For this specific FER task, we found that the best pre-trained CNN model achieved 92.8% accuracy (using the VGG-19 model), with the fastest runtime of ∼2.3 seconds (achieved using the SqueezeNet model). © 2023 IEEE.

关键词： Emotion Recognition

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Lithium-Ion Battery Prognostic and Health Prediction Using Machine Learning Models 4

Lithium-Ion Battery Prognostic and Health Prediction Using M...

引用

4th IEEE International Conference on electrical, computer, and Energy Technologies, ICECET 2024

作者： Nsaka, Ozioma John Malki, Heidar A. University of Houston Dept. of Electrical & Computer Engineering HoustonTX United States University of Houston Dept. of Engineering Technology HoustonTX United States

ISBN: (纸本)9798350395914

Lithium-Ion battery prognostic and health prediction is an essential part of our modern world today. Reliable predictions of the current state and remaining useful life are critical to ensure safe operation of various industrial applications. There exists established and extensively researched battery state prediction models including the equivalent circuit and physics-based models. The emergence and advancements of machine learning algorithms, such as support vector machine (SVM), have led to a new era of data-driven models. In this research, we initially establish a baseline prediction using traditional machine learning algorithms and subsequently employ deep learning algorithms. Our analysis focuses on the battery's degradation pattern, specifically the predication of the State of Health (SOH) prediction and the Remaining Useful Life (RUL), aiming to predict the degradation from a specific threshold cycle to the EOL of the battery. In this study, our aim is to establish a comparable baseline for several machine learning-based prognostic predictions. To overcome this problem, we conduct several experiments on the same dataset using traditional algorithms and as well as neural network models. The classification accuracy aligns with several benchmarks established in the research literature. This research further underscores the viability of using Long Short-Term Memory (LSTM) models for RUL prediction, thereby advancing the role of data-driven models in prognostic and health management for critical engineering applications. © 2024 IEEE.

关键词： Electronic health record

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SolaClean: An Autonomous Robot for Optimized Solar Panel Cleaning

SolaClean: An Autonomous Robot for Optimized Solar Panel Cle...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： Fayad, Luay Nasr, Marcel Breiche, Zayan Boudargham, Nadine Hamad, Mustapha Kassem, Abdallah Notre Dame University -Louaize Dept. of Electrical and Computer & Communications Engineering Shouf Lebanon Notre Dame University -Louaize Zouk Dept. of Electrical and Computer & Communications Engineering Mosbeh Lebanon

ISBN: (纸本)9798331529604

This paper presents the development of an automated solar panel cleaning robot aimed at addressing the inefficiencies and practical challenges associated with manual cleaning in large-scale solar energy systems. The robot integrates a lightweight aluminum bridge with wheeled legs that traverse parallel rails mounted on the panels. It features a centrally mounted four-wheel car chassis, a microfiber wiper on an aluminum rod, and a water-spraying nozzle. The system is powered by lithium batteries and managed by an Arduino Mega, interfacing with various sensors and a Bluetooth module for communication with a mobile application. The software supports user control for initiating cleaning cycles, monitoring the robot's status and water levels, and receiving alerts for low water or emergencies. The operational process involves spraying water, moving forward while wiping, automatically stopping water flow upon reaching panel limits, and returning to the parking position, with autonomous obstruction handling. Extensive testing confirms that the robot meets all requirements, providing a reliable and efficient solution for maintaining solar panel performance. © 2025 IEEE.

关键词： Aluminum bridges

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Optimized Simultaneous Identification and Thermal Regulation for Industrial Pyrolytic Furnaces Using MATLAB and Atmel Microcontrollers

Optimized Simultaneous Identification and Thermal Regulation...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Sarkar, Shaktipada Bhattacharya, Samar Jain, Arvind Kumar National Institute of Technology Dept. of Electrical Engineering Agartala India Jadavpur University Dept. of Electrical Engineering Kolkata India

ISBN: (纸本)9798331540821

Temperature control is crucial for heater furnace systems in various industries, ensuring product quality, process efficiency, and safety. This abstract review temperature control methods, applications, and challenges in these systems. Precise regulation optimizes energy use, extends equipment lifespan, and maintains product consistency. Common methods include PID controllers, fuzzy logic, and model predictive control, using sensors like thermocouples. The document details PID controllers, explaining their role in industrial systems through Proportional (KP), Integral (KI), and Derivative (KD) parameters. Tuning these parameters is vital, with methods like Ziegler-Nichols and Chien-Hrones-Reswick discussed. The shift from analog to digital control highlights digital systems' flexibility. Hardware-In-Loop (HIL) simulation for real-time testing is covered, showing its benefits in control design. Microcontroller-based systems and MATLAB for real-time simulation and control, including the Data Acquisition Toolbox, are also discussed, emphasizing graphical programming and microcontroller control. © 2024 IEEE.

关键词： MATLAB

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Sensitivity Analysis for Normal and Canonical Internal Descriptions of A Notch Digital Filter

Sensitivity Analysis for Normal and Canonical Internal Descr...

引用

2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2024

作者： Hinamoto, Yoichi Doi, Akimitsu Dept. of Electrical and Computer Engineering National Institute of Technology Kagawa College Takamatsu761-8058 Japan Dept. of Computer Science Hiroshima Institute of Technology Hiroshima731-5193 Japan

ISBN: (纸本)9798350353464

This paper analyzes coefficient sensitivity for the internal descriptions (state-space models) of a 2nd-order recursive notch digital filter. To this end, a 2nd-order recursive notch digital filter is realized by a normal-form or canonical-form internal description. The coefficient sensitivity for each internal description is then analyzed to derive the improved l2-sensitivity measure. Moreover, the coefficient sensitivity of the normal-form internal description is compared with that of the canonical-form counterpart by means of numerical experiments. The simulation results clarify that the normal-form internal description has low coefficient sensitivity as a whole comparing to the canonical-form counterpart. ©2024 IEEE.

关键词： Notch filters

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Analytical Study of the Transient Period of Fault Current in Inverter-Based Resources 3

Analytical Study of the Transient Period of Fault Current in...

引用

3rd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Kunwar, Dol Raj Hossain, Shah Mohazzem Gujar, Abhilash Ioe Tribhuvan University Dept. of Electrical Engineering Nepal Military Institute of Science and Technology Dept. of Electrical Electronic and Communication Engineering Bangladesh College of Engineering Dept. of Electrical Engineering Pune India

ISBN: (纸本)9798350387193

The increasing integration of inverter-based re-sources (IBRs) in modern power systems poses challenges to traditional grid conventions due to features such as low inertia and limited fault current supply. Since IBRs exhibit distinct fault characteristics compared to synchronous generators, a reevalu-ation of existing protection schemes is necessary. Understanding the transient response of IBR during faults is crucial for both protection system design and ensuring that its outputs remain within the maximum limits of internal semiconductor materials, which is a factor heavily influenced by manufacturer-specific con-trol settings. While electromagnetic transient programs (EMTP) provide valuable insights, their application to large power systems is computationally intensive, underscoring the need for analytical fault models. This paper addresses these challenges by converting EMTP models of grid-following IBRs into transfer function models to study transient response. Additionally, it attempts to derive fault current expressions for LC-filter-based inverters and identifies the corresponding factors that influence the resulting transients. The accuracy of the developed analytical model is also evaluated against simulations in PSCAD. © 2024 IEEE.

关键词： Electromagnetic transients

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Transient Synchronization Stability of Grid-Following Converters with Integration of Grid-Forming Converters Under Symmetrical Grid Faults 4

Transient Synchronization Stability of Grid-Following Conver...

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4th International Conference on electrical engineering and Control Science, IC2ECS 2024

作者： He, Yifan Li, Xiaoqiang Wang, Jingqing Hu, Yue China University of Mining and Technology Dept. Electrical Engineering Xuzhou China China University of Ming and Technology Dept. Electrical Engineering Xuzhou China

ISBN: (纸本)9798331510107

The essence of transient instability of grid-following (GFL) converters is voltage imbalance, while the transient instability of grid-forming (GFM) converters is due to power imbalance. As a result, different transient instability mechanisms would lead to complicated transient synchronization characteristics of hybrid GFL/GFM converter systems due to the coupling between the converters. Previous studies have analyzed the influence of the GFL-controlled converter on the transient process of the GFM-controlled converter. To further complete the research, this paper uses the phase portrait and the equal area criterion (EAC) to analyze the influence of the GFM-controlled converter on the transient synchronization stability of the GFL-controlled converter under symmetrical grid faults. Simulation results are finally provided to verify the effectiveness of the analysis. © 2024 IEEE.

关键词： Transient stability

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Dynamic Network Optimization with Deep Learning and Deployment of Intelligent Reflecting Surfaces 19

Dynamic Network Optimization with Deep Learning and Deployme...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Jang, Nayeon Bae, Jeonghoon Tianyu, Tao Byun, Gyurin Bui, Phuoc-Nguyen Lee, Tae-Jin Choo, Hyunseung Dept. of AI Systems Engineering Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

The proliferation of 5G technologies has presented challenges such as signal attenuation and high energy consumption. This study proposes a novel approach to enhance wireless networks by integrating Intelligent Reflecting Surfaces (IRS) and Artificial Intelligence (AI) to optimize signal propagation and predict the Received Signal Strength Indicator (RSSI). A new framework combines K-means clustering for IRS deployment with a Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) model for signal prediction. Using data from 15,000 Wi-Fi access points in Herefordshire, UK, the results demonstrate significant improvements in signal strength, with the proposed model achieving a Mean Squared Error (MSE) of 37% lower than the existing models, indicating higher prediction accuracy. In addition to enhancing signal quality, the framework achieves throughput increases of 21.26% with single-group IRS and 64.91% with multi-group IRS. These results validate the proposed the potential of the framework to improve performance, making it suitable for next-generation wireless networks. © 2025 IEEE.

关键词： 5G mobile communication systems

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