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High impedance fault classification in microgrids using a transformer-based model with time series harmonic synchrophasors under data quality issues

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Neural Computing and Applications 2024年第23期36卷 14017-14034页

作者： Cieslak, Dionatan A. G. Moreto, Miguel Lazzaretti, André E. Macedo-Júnior, José R. PPGEEL - Graduate Program in Electrical Engineering Federal University of Santa Catarina UFSC R. Eng. Agronômico Andrei Cristian Ferreira s/n Santa Catarina Florianópolis88040-900 Brazil CPGEI - Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná UTFPR Av. Sete de Setembro 3165 Paraná Curitiba80230-901 Brazil PPGEELT - Graduate Program in Eletrical Engineering Federal University of Uberlândia UFU Av. João Naves de Ávila 2121 Minas Gerais Uberlândia38408-100 Brazil

Recent advances in distribution networks, driven by the integration of renewable energy sources, have spurred the emergence of microgrids, elevating concerns regarded reliability and stability. In this context, precise monitoring of events, particularly those elusive to detection like high-impedance faults (HIFs), becomes imperative. The development of phasor measurement units (PMUs) with their harmonic synchronized measurements has enhanced the monitoring task and fostering the application of synchrophasors even on microgrids. This work introduces a novel method for event classification in microgrids, utilizing combined low-rate PMU data and harmonic synchrophasor time series. Central to our approach is the usage of a state-of-the-art transformer neural network, based on the attention mechanism, to effectively discern HIFs from other faulty and non-fault events. Notably, this methodology accounts for prevalent PMU data quality issues, including noise, missing data, and synchronism errors. Results from real-world HIF data demonstrate a robust performance, with an accuracy rate of approximately 98% in event classification. This harmonic synchrophasor-based strategy showcases promise as an original approach for handling commercial PMU data, offering sufficient robustness for deployment in real-world applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Phasor measurement units

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RIS-Aided Uplink NOMA in the Presence of CCI 32

RIS-Aided Uplink NOMA in the Presence of CCI

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32nd Telecommunications Forum, TELFOR 2024

作者： Ozduran, Volkan Soleimani-Nasab, Ehsan Istanbul University-Cerrahpaşa Istanbul Turkey Graduate University of Advanced Technology Department of Electrical and Computer Engineering Kerman Iran

ISBN: (纸本)9798350391053

This paper investigates the co-channel interference (CCI) effects on the uplink non-orthogonal multiple access (NOMA) users. The investigation considers that two NOMA users communicate with the base station (BS). The NOMA user close to the BS has a line of sight with the BS, while the far user only communicates with the BS via passive and absorptive reconfigurable intelligent surface (RIS). The investigation also considers that BS is under the effect of the randomly deployed CCI and interfere the received signal from the NOMA users. Performance of the aforementioned system is measured by means of the outage probability performance metric. Analytical derivations are verified by means of the Monte Carlo-based computer simulations. The results indicate that increasing the number of RIS elements degrades the outage performance for the near user but enhances it for the far user. Furthermore, the findings reveal that CCI causes minor loss in coding gain for NOMA users when the RIS has a small number of elements. However, this negative impact of CCI diminishes as the number of RIS elements increases. © 2024 IEEE.

关键词： Cochannel interference

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device 8

Objective Sleep Quality Measurement based on Fuzzy Logic and...

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8th International Conference on computer Science and Computational Intelligence, ICCSCI 2023

作者： Wijaya, Jason A. Andersen, Pauw D. Surantha, Nico Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method. © 2023 The Authors. Published by Elsevier B.V.

关键词： actigraphy artificial intelligences fuzzy logic sleep sleep quality

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Comparison of 2L and 3L-NPC Power Converter-Based a Level-2 Isolated Off-Board Charger

Comparison of 2L and 3L-NPC Power Converter-Based a Level-2 ...

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2023 IEEE International Conference on electrical, computer and Energy Technologies, ICECET 2023

作者： Thanakam, Thidarat Kumsuwan, Yuttana Graduate Program in Electrical Engineering Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand

ISBN: (纸本)9798350327816

This paper presents an off-board charger system for any electric vehicle based on a single-stage power converter operation that performs as a three-phase pulse width modulation rectifier. To enhance the safety issue by itself, a step-down transformer with galvanic isolation is added. Additionally, the off-board charger system allows bidirectional power flow in both grid-to-vehicle (G2V) and vehicle-to-grid operations. To analyze this system, only G2V operation is shown by comparing a two-level power converter charger with a three-level neutral point clamped power converter charger. In light of its controller, the off-board charger is designed to charge a battery in constant current and constant voltage mode in compliance with Level 2 of charging power. Based on this controller, it can support grid power quality under a unity power factor. Simulation-based verification is conducted on a maximum power rating of 22 kW to claim the performance of steady-state operation and dynamic operation. © 2023 IEEE.

关键词： Electric vehicles galvanic isolation off-board charger three-level neutral point clamped power converter two-level power converter

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Applied Depthwise Separable-based Convolutional Neural Network Classification on MobileNet Algorithm for Human Fall Detection 12

Applied Depthwise Separable-based Convolutional Neural Netwo...

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12th International electrical engineering Congress, iEECON 2024

作者： Sakarin, Pipat Sitjongsataporn, Suchada Mahanakorn University Of Technology The Electrical Engineering Graduate Program Faculty Of Engineering And Technology Bangkok Thailand Mahanakorn University Of Technology Department Of Electronic Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

This paper presents the human fall detection by convolutional neural network (CNN) classification and MobileNet algorithm. In order to reduce the number of parameter, we propose a vision-based fall detection model, that is called 'Applied Depthwise separable-based CNN-MobileNet (ACM) algorithm'. This model is based on CNN, deptwise convolution and pointwise convolution of MobileNet algorithm. Experimental results show the accuracy of proposed ACM model about 97.68% and number of parameters of proposed ACM model is less than conventional CNN model. © 2024 IEEE.

关键词： Convolution

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Control of Three-Level PWM Inverter-Fed Induction Motor Drives

Control of Three-Level PWM Inverter-Fed Induction Motor Driv...

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2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2023

作者： Mueangngoen, Sutthimat Jarutus, Neerakorn Kumsuwan, Yuttana Chiang Mai University Graduate Program in Electrical Engineering Department of Electrical Engineering Faculty of Engineering Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand

ISBN: (纸本)9798350314274

This paper presents the control of three-level PWM committed to a three-level twelve-switch inverter-fed induction motor drive. The proposed control is totally capable of extracting the maximum DC utilization with the classical continuous and discontinuous PWM. Moreover, in the light of the multi-level inherency, the low switching frequency is attractive to this application to avoid the high power losses, while it can maintain the stability of the operation. Regardless of the single or double carrier waves, optimized switching states associated with the proposed logical operation part, which is responsible for obtaining the multi-level characteristic of the inverter, are also proposed. Importantly, through the MATLAB/Simulink program, the steady-state and dynamic response of the high-performance drive with the classically indirect vector control are definitely investigated to confirm the effectiveness of the proposed control strategy. © 2023 IEEE.

关键词： Pulse width modulation

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Deep Convolutional Neural Networks for Accurate Solar Module Classification in Thermal Images 12

Deep Convolutional Neural Networks for Accurate Solar Module...

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12th International electrical engineering Congress, iEECON 2024

作者： Phungket, Chutinun Nangthin, Prasit Pathumwan Institute Of Technology The Graduate Program Faculty Of Engineering Faculty Of Engineering Bangkok Thailand Pathumwan Institute Of Technology Faculty Of Engineering Department Of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

The rapid and continuous technological advancements in computer and internet technologies, combined with data management techniques, find applications in various domains. For example, they aid in predictive maintenance systems, evaluate the performance of solar modules, and differentiate efficiently performing solar modules from less efficient ones using thermal imagery. The accuracy of solar module classification significantly impacts the subsequent assessment. In this study, we employ advanced techniques, particularly deep learning through Convolutional Neural Networks (CNN), to classify thermal images of solar modules. The practical aspects of applying CNN to the dataset of thermal images are explored, including transfer learning from pretrained CNN architectures and custom CNN architecture training. Experiments are conducted with three prominent types of solar module conditions: normal, cracked, and dusty. The results reveal that the most effective approach involves learning and fine-tuning pre-trained architectures on detailed studies, achieving a classification accuracy of up to 94.5% © 2024 IEEE.

关键词： Convolutional neural networks

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On-Board Integrated Charger Based on Open-End Winding AC Machine

On-Board Integrated Charger Based on Open-End Winding AC Mac...

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2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2023

作者： Thanakam, Thidarat Jarutus, Neerakorn Kumsuwan, Yuttana Chiang Mai University Graduate Program in Electrical Engineering Department of Electrical Engineering Faculty of Engineering Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand

ISBN: (纸本)9798350314274

This paper presents an on-board integrated charger for electric vehicles (EVs) based on an open-end winding AC machine. To reduce weight, volume, and costs, including additional space area in EVs, the main component of the traction system is utilized as a part of the charger system, i.e., the wound rotor open-end winding AC machine fed by a dual converter with a single battery source. In charging operation, the rotor winding side of the AC machine is at standstill and reconfigured through magnetic contactors to interface with the three-phase utility grid, whereas the stator winding side is still connected to the converters. It performs as a transformer isolated by itself. On the other hand, the rotor windings are shorted together for the driving operation. Besides, the charger system assists the stability of the utility grid by consuming constant power, absorbing maximum power rated at 22 kW. To enhance excellent control, the modulation strategy of the dual converter is determined by means of different phase angles of 120° As a result, the differential-mode voltage problem is suppressed. The simulation results are validated to confirm the performance of the proposed system. © 2023 IEEE.

关键词： Winding

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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A Low-Noise Capacitively-Coupled Chopper Instrumentation Amplifier with SAR-Assisted Low Current Ripple Reduction Technique 21

A Low-Noise Capacitively-Coupled Chopper Instrumentation Amp...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Jo, Jungkook Jun, Jaehoon Inha University Department of Electrical and Computer Engineering Korea Republic of Inha University Program in Semiconductor and Device Korea Republic of

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377088

This paper presents a novel design technique for reducing ripple in various sensor read-out integrated circuits (IC) by calibrating the amplifier offset through current control in bias resistors. Traditional methods for reducing ripple often involve complex amplifiers consuming static power. In contrast, the proposed ripple reduction technique employs successive-approximation register (SAR) logic to control the current flowing through the bias resistor, thus minimizing static power consumption. Experimental results obtained through simulations in a 28 nm CMOS process demonstrate significant ripple reduction to 8.94 μVpp from 2 mVpp. This approach is suitable for low-power design, particularly in wearable device applications, and can potentially extend to other amplifier configurations utilizing chopping technique. © 2024 IEEE.

关键词： Chopper amplifiers

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