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MTDC Fault Detection and Localization Using High-Pass Filter 50

MTDC Fault Detection and Localization Using High-Pass Filter

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Khan, Saad Ahmed Ukil, Abhisek Nair, Nirmal-Kumar C. Li, Dongyu Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand School of Electrical and Automation Engineering East China Jiaotong University Nanchang330013 China

ISBN: (纸本)9781665464543

The DC link capacitor discharge in the event of a DC fault is rapid and contains high frequencies. This rapid discharge of high current and frequency interface with DC bus, Voltage Source Converter (VSC), and AC source. This interface damages the equipment and possibly living beings in proximity. Therefore, it is necessary to develop a topology to detect and identify fault types promptly, along with the isolation and restoration of the system. This study introduces an improved novel fault detection technique using the Highpass Chebyshev type 2 filter due to its flatter pass-band response. Further, the polarities of peaks obtained from the fault detection calculation are used again for another proposed novel fault location method. It identifies fault types to locate and isolate the faulty system using only polarities of amplitude response peaks. In the simulation, both methods were fast and accurate in detecting, locating, and identifying appropriate fault types only by High Pass Filter (HPF) amplitude response peaks and their polarities. © 2024 IEEE.

关键词： Chebyshev filters

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Control of Microgrid Cluster Using Raspberry Pi

Control of Microgrid Cluster Using Raspberry Pi

引用

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

作者： Carrasco-González, David Sarrias-Mena, Raúl Horrillo-Quintero, Pablo Llorens-Iborra, Francisco Fernández-Ramírez, Luis M. Suret Research Group Department of Electrical Engineering Algeciras Spain Uca Suret Reseach Group Dept. of Engineering in Automation Electronics and Computer Architecture and Networks Algeciras Spain

ISBN: (纸本)9781665471640

Currently, microgrid clusters (MGC) are being increasingly used owing to their benefits and human needs. MGs can be composed of power generation, consumption, and storage elements. They permit the combination of DC and AC technologies, even for a single microgrid (MG). In this paper, a new control system of a MGC consisting of two MGs connected to the main grid is developed. One of them is a DC MG that is composed of a wind turbine (WT), electrolyzer (EZ), ultracapacitor (UC), fuel cell (FC) and DC loads. The other MG is an AC MG, which is made up of a PV generator, a battery bank and AC loads. The MGC is tested under different working conditions in an experimental setup composed of a Raspberry Pi microcontroller, an OPAL-RT 4510 unit and a PC host. The experimental setup allows the system to run in real time and validate the results obtained. Finally, the control system implemented in the MGC exhibits a correct behaviour under different conditions, according to the results obtained. © 2023 IEEE.

关键词： energy management system Microgrid cluster Raspberry Pi real-time simulation

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A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System 12

A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System

引用

12th International Electrical engineering Congress, iEECON 2024

作者： Rakprayoon, Panjawee Ounsiri, Akechai Chongyoa, Ratchanon Bangkok University Faculty Of Engineering Dept. Ai Engineering And Data Science Pathum Thani Thailand Dna Robotics And Automation System Company Limited Pathum Thani Thailand Bangkok University Faculty Of Engineering Dept. Computer And Robotics Engineering Pathum Thani Thailand

ISBN: (纸本)9798350383591

For an AI tic-tac-toe manipulator application, a real-time vision-based approach is proposed. The technique employs the RealSense camera to capture color and dept. images. Combining object detection and image processing techniques to identify X's and O's positions on the game board. However, we recognize that errors can occur during object movement when using only 2D images. To solve this problem, a calibration method between RealSense and the manipulator based on the point-to-plane ICP algorithm is presented. This method provides the 3D object position in the robot coordinate, resulting in increasing the accuracy from 86.25% to 97.25%. Our experiment shows that this technique is highly effective and can accurately identify position objects. © 2024 IEEE.

关键词： Object detection

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

引用

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

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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Advanced migraine prediction hardware system 50

Advanced migraine prediction hardware system

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50th Summer computer Simulation Conference, SCSC 2018, Part of the 2018 Summer Simulation Multi-Conference, SummerSim 2018

作者： Henares, Kevin Pagán, Josué Ayala, José L. Risco-Martín, José L. Dept. of Computer Architecture and Automation Complutense University of Madrid Madrid Spain Electronic Engineering Department Universidad Politécnica de Madrid Spain CCS-Center for Computational Simulation Madrid Spain Dept. of Computer Architecture and Automation Complutense University of Madrid CCS-Center for Computational Simulation Madrid Spain

ISBN: (纸本)9781510860209

Migraine is one of the most disabling neurological diseases. Its prevalence reaches 15% of the population in developed countries and lead to high economic costs for private and national health systems. There is no cure for the migraine yet. Objective and accurate migraine prediction turns necessary in order to increase the effectiveness of the current treatments. To tackle this problem authors demonstrated in previous works the feasibility of the migraine modeling and presented a DEVS-based model for migraine prediction in a real time framework. This work presents the first step to bring migraine predictive models and simulations to real devices for the first time in the literature. To do this, the DEVS-based model has been ported to a robust hardware description language (VHDL) to be implemented into a FPGA. This paper leads to the final stage: the physical implementation of a predictive migraine monitoring device. © 2018 Society for Modeling & Simulation International (SCS).

关键词： Field programmable gate arrays (FPGA)

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Selection of Beamforming in 5G MIMO scenarios using Machine Learning approach 19

Selection of Beamforming in 5G MIMO scenarios using Machine ...

引用

19th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Silva, Douglas Henrique Augusto Ribeiro, David Ramirez, Miguel Arjona Lopes Rosa, Renata Chaudhary, Sushank Rodriguez, Demostenes Zegarra Federal University of Lavras Dept. of Computer Science MG Lavras Brazil Dept. of Automation Federal University of Lavras MG Lavras Brazil University of São Paulo Dept. of Electronic Systems Engineering São Paulo Brazil Chulalongkorn University Dept. of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

In new generation networks, 5G and 6G networks, intelligent mechanisms based on artificial intelligence algorithms are playing a relevant role in the performance improvement at different network levels. In 5G networks, different techniques are used, such as MIMO systems, and its network infrastructure is utilized by diverse services, for instance, vehicular communications. Thus, the description and the tracing of a communication scenario needs a large volume of data. Because of the difficulty to implement actual 5G networks, there is a lack of datasets containing complete 5G scenarios, the data is not enough or contain imbalanced classes to properly train machine learning (ML) algorithms. In this context, we propose a method to increase the amount of data to improve the machine learning performance of some classification models, specifically Random Forest, Multilayer Perceptron, and k-Nearest Neighbors. In the experimental results of the test phase, considering the inclusion of synthetic data, Random Forest, Multilayer Perceptron and k-Nearest Neighbors reached macro F1 scores of 0.9341, 0.9241 and 0.9456, respectively, which are superior to the results obtained when training with the original data only. © 2022 IEEE.

关键词： MIMO systems

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Flow-rate Feeder Control Design With PI-PI Cascade Controller on Biomass Power Plant

Flow-rate Feeder Control Design With PI-PI Cascade Controlle...

引用

2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial automation, ICAMIMIA 2023

作者： Prasetyo, Indrawan Budi Budiyarto, Aris Ciptian Weried, P. Wahyudi Farid, Imam Prakosa, Setya Widyawan Leu, Jenq-Shiou National Taiwan University of Science and Technology Dept. Electronic and Computer Engineering Taipei Taiwan PT. Parametrik Solusi Integrasi Bandung Indonesia Institut Teknologi Sepuluh Nopember Dept. Automation Electrical Engineering Surabaya Indonesia

ISBN: (纸本)9798350309225

The goal of gasification-based biomass power plants in this research is to be able to produce 5-10 KW of electricity through the thermal energy conversion process. Syngas, a byproduct of the burning of biomass, is created during the gasification process and may be utilized to power generators. The gasification process will diminish biomass, therefore if it reaches a particular level, it has to be frequently supplied to the feeder. Uncontrolled feeder transport capacity will result in the biomass that feeds the gasifier exceeding or falling short of the need, causing fluctuations in the amount of syngas generated. Due to this, the PI-PI cascade controller was designed to regulate biomass transport capacity. From the design of the PI-PI Cascade control system, the parameters for the primary loop controller Kp = 38.9;Ki = 0.81, and the secondary loop Kp = 3.5;Ki = 5. The results of the control design obtained time constant (ti) 0.0101 seconds;rise time(tr) for tr 10% at 0.014 seconds and tr 90% at 0.674 seconds;settling time(ts) of the response signal with Ts 0.05% at 9.5 seconds;delay time (td) at 0.065 seconds and if a disturbance is given when the condition is steady, it will return to the initial condition at 3.21 seconds after the disturbance is given. © 2023 IEEE.

关键词： Biomass

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Data-Enhanced Prediction with Decomposition and Amplitude-Aware Permutation Entropy in Distributed Computing Systems

Data-Enhanced Prediction with Decomposition and Amplitude-Aw...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Hu, Qinglong Bi, Jing Zhang, Wei Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China City University of Hong Kong Dept. of Computer Science 999077 Hong Kong College of Computer Beijing University of Technology Beijing100124 China University of Connecticut Dept. of Computer Science and Engineering StorrsCT06269 United States Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

In recent years, distributed computing has wit-nessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and inter-dependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVI for achieving high-accuracy workload prediction in distributed computing systems. SVI employs the Savitzky-Golay filter and variational mode decomposition for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVI improves the prediction accuracy by 35.4% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVI enhances the prediction accuracy of workload, CPU, and memory by 62.5%, 65.6%, and 69.1 %, respectively. © 2024 IEEE.

关键词： Quality of service

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Analysis of Energy Potential of Micro Wind Turbines at Three Locations in the Central-Eastern Part of the Bulgarian Danube River Region 5

Analysis of Energy Potential of Micro Wind Turbines at Three...

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5th IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Valov, Nikolay Gabrovska-Evstatieva, Katerina Stoyanov, Ivaylo Kaneva, Tsvetelina Kadirova, Seher Evstatiev, Boris Mihailov, Nicolay University of Ruse "Angel Kanchev" Dept. of Automation and Electronics Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Computer Science Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Electrical Power Engineering Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Computer Systems and Technologies Ruse Bulgaria

ISBN: (纸本)9798350352863

The share of renewable energy sources in the energy mix worldwide is gradually increasing. And even though they all have a highly variable nature, they can be forecasted statistically. The wind energy potential was investigated in this article in the Bulgarian Danube region, located between Ruse and Silistra. Two years of meteorological data, including the wind speed at 10 m height and the ambient temperature was used to estimate the average wind energy for each month of the year at three geographic locations - Ruse, Staro Selo (near Tutrakan), and Calarasi, Romania (near Silistra). The results showed that Staro Selo has the highest energy potential, reaching up to 0.75 kWh/day/m2 in February, followed by Calarasi with 0.38 kWh/day/m2. The city of Ruse showed the lowest wind potential with the highest average monthly value reaching only 0.0027 kWh/day/m2. The obtained results showed that the regions around Tutrakan and Silistra are appropriate for the application of micro wind turbines. © 2024 IEEE.

关键词： Windmill

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Fuzzy feed forward system based on T-S model applied for constant power control in wind power system

Fuzzy feed forward system based on T-S model applied for con...

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International Conference on Machine Learning and Cybernetics

作者： Peng, Hai-Liang Guo, Peng Jia, Jian-Jun Dept. of Automation and Computer Engineering North China Electric Power University Beijing 102206 China Zhumadian Electric Power Corporation Henan 463000 China

ISBN: (纸本)9781424465262

Constant power control technology is one of the key technologies in wind power control. PI control is the most widely used technology in constant power control system. Hysteresis of PI control will cause a large overshoot of the output power that will make interference to the power grid. Feedforward control system can reduce the system lag. The fuzzy controller based on T-S model can overcome the influence caused by non-linear and uncertain mathematical model of the system. The fuzzy controller makes the feedforward controller easy to realize. Introducing neural network training method into Fuzzy control will greatly simplify the design of fuzzy controller. Based on Neural Adaptive - Fuzzy technology, a feed forward system is proposed in this paper, which is able to timely adjust pitch angle according to changes of wind speed. At last, the control strategy is simulated by MATLAB. The simulation results show that the method greatly improves the performance of system dynamic response, thus ensuring a constant power output. © 2010 IEEE.

关键词： Fuzzy control

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