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Evaluating the Influence of Phase-Locked Loops on the Response of Grid-Following Inverter-Based Resources to Interconnection Transmission Line Faults

Evaluating the Influence of Phase-Locked Loops on the Respon...

引用

Workshop on Communication Networks and Power Systems (WCNPS)

作者： Pedro Henrique de Morais Martins Luciano Sales Barros Felipe Vigolvino Lopes Postgraduate Program in Renewable Energies Federal University of Paraíba Department of Computer Systems Federal University of Paraíba Department of Electrical Engineering Federal University of Paraíba

ISBN: (数字)9798331507152

ISBN: (纸本)9798331507169

In this paper, the influence of different Phase-Locked Loops (PLLs) on the fault response of a grid-following (GFL) Inverter-Based Resource (IBR) is evaluated. The analyzed IBR represents a wind power plant with rated power equal to 100 MVA, being composed of 50 turbines with 2 MVA each, whose controls follow the Brazilian grid code. Two PLL models are taken into account to conduct the proposed studies, which include the analysis of voltage and frequency estimated by each PLL, and currents injected into the IBR interconnection transmission line. The Synchronous Reference Frame (SRF) PLL and the Quadrature Signal Generator-based Second-Order Generalized Integrator (QSG-SOGI) PLL are considered, assessing their influence under different transmission line fault scenarios. The obtained results indicate that the PLL can present severe instabilities during faults, which can be more or less critical depending on the fault type and on the PLL type. Indeed, the results reveal that different PLLs can result in distinct behaviors of currents injected into the interconnection line, raising the importance of knowing the PLL structures when studies on IBR fault responses are of interest.

关键词： Power transmission lines Grid following Conferences Voltage Wind power generation Power system stability Frequency estimation Phase locked loops Inverter-based resource Turbines

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Can adaptive strategies sustain bidirectional LoRaWAN traffic?

Can adaptive strategies sustain bidirectional LoRaWAN traffi...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Emiliano Sisinni Alessandro Depari Paolo Bellagente Alessandra Flammini Ivanovitvh Silva Thommas Flores Paolo Ferrari Dept. of Information Engineering University of Brescia Brescia ITALY Dept. of Computer Engineering and Automation UFRN Natal BRAZIL Postgraduate Program in Electrical and Computer Enigeneering UFRN Natal BRAZIL

Low Power Wide Area Networks (LPWANs) are a viable solution for addressing the requirements of Internet of Things (IoT) applications that can tolerate sporadic updates, possibly only in the uplink (from the field towards the cloud and the user) direction. LoRaWAN is currently the most diffused example of LPWAN, exploiting LoRa radios. LoRa, an enhanced version of chirp spread spectrum modulation, trades off the radio sensitivity for the data rate, permitting extensive area coverage. However, the price is a relatively long frame duration, leading to high collision probability in dense, asynchronous scenarios; fortunately, polarity inversion minimizes collisions between uplink and downlink traffic. Therefore, adaptive strategies (based on some feedback from the backend) have also been suggested to adapt the data rate depending on the environment condition dynamically. This work compares the ADR (Adaptive Data Rate) and the ADP (Adaptive Data Payload) strategies when bidirectional traffic exists, using a purposely designed simulator. Results considering a different number of gateways show that ADP tries to maximize the amount of data effectively received in both direction but suffer from the adoption of longer frame duration, leading to higher collision probability. In contrast, ADR tries to force the adoption of the fastest data rate, limiting the overall amount of transferred data.

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A Framework to Support Collection, Processing and Analysis of Smart Vehicles Data

A Framework to Support Collection, Processing and Analysis o...

引用

IEEE World Forum on Internet of Things (WF-IoT)

作者： Mariana Azevedo Thais Medeiros Morsinaldo Medeiros Carlos M. D. Viegas Marianne Silva Ivanovitch Silva Daniel G. Costa Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte - Natal Brazil INEGI Faculty of Engineering University of Porto Porto Portugal

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Thanks to the Internet of Things (IoT) revolution and advancements in embedded systems, the incorporation of an extensive array of sensors and computational resources into vehicles has become a reality, significantly enhancing their capabilities to produce relevant data. This has opened up new opportunities for acquiring and processing vehicular data in various areas, supporting new applications such as vehicular monitoring to the use of information for artificial intelligence for improved decision-making tasks. However, although the benefits are promising, there is a current challenge when providing flexible and standardized procedures to capture, process, and store this information for further analysis. In this context, this article proposes an framework to support the the handling of vehicular data, including data processing by a server and storage in a cloud database. This framework was validated through a real-world case study, analyzing the collected sensor data. The results indicated the feasibility of the proposal, contributing to the availability of vehicular data analysis in a way that is distinct from proposals in the literature. Additionally, this framework offers another significant contribution, related to the use of an appropriate database for time series, enabling scalability and high availability.

关键词： Data analysis Torque Databases Time series analysis Proposals Internet of Things Servers

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An Adaptive TinyML Unsupervised Online Learning Algorithm for Driver Behavior Analysis

An Adaptive TinyML Unsupervised Online Learning Algorithm fo...

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Metrology for Automotive (MetroAutomotive), IEEE International Workshop on

作者： Marianne Silva Thais Medeiros Mariana Azevedo Morsinaldo Medeiros Mikael Themoteo Tatiane Gois Ivanovitch Silva Daniel G. Costa Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte Natal Brazil INEGI Faculty of Engineering University of Porto Porto Portugal

The Internet of Things (IoT) has significantly impacted various industries, particularly the automotive sector, due to the growing integration of IoT technologies in vehicles. As a result, the volume of data generated by vehicular sensors has increased, leading to a surge in studies focusing on driver behavior to enhance road safety and optimize transportation networks. However, traditional approaches to analyzing driver behavior have relied on supervised offline learning models, which are unsuitable for handling data streams in online learning environments. This study introduces an unsupervised online k-fix AutoCloud algorithm for detecting driver behavior patterns, leveraging the concepts of typicity and eccentricity while considering the historical-temporal relationships between samples. Furthermore, the algorithm autonomously and adaptively evolves without requiring a supervised training phase, making it compatible with the TinyML concept, encompassing Artificial Intelligence algorithms designed for low-power IoT devices. To validate the proposed method, a real case study was conducted over four days using a vehicle to compare the quantity and quality of clusters generated by the algorithm. The findings demonstrate the potential of the proposed approach for optimizing data processing with minimal computational power.

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Real Time Monitoring System of Drinking Water Quality Using Internet of Things 24

Real Time Monitoring System of Drinking Water Quality Using ...

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24th International Electronics Symposium, IES 2022

作者： Abidin, Zainul Maulana, Eka Nurrohman, Muhammad Yogi Wardana, Fitri Candra Warsito Universitas Brawijaya Faculty of Computer Science Department of Computer Engineering Malang Indonesia Universitas Brawijaya Faculty of Engineering Department of Electrical Engineering Malang Indonesia Universitas Brawijaya Master Program in Environmental Resources and Development Management Postgraduate School Malang Indonesia Universitas Brawijaya Faculty of Mathematics and Natural Sciences Department of Chemistry Malang Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

In order to reduce Polyethylene Terephthalate (PET) bottle plastic waste, Universitas Brawijaya (UB) provided reverse osmosis drinking water in some buildings. Water monitoring is needed to keep the water quality. Therefore, a water monitoring system based on Total Dissolved Solids (TDS), pH, and water flow was proposed. Since the UB buildings have good internet connection (WiFi), Internet of Things (IoT) was employed to the system to support real time monitoring. So, data of TDS, pH, and water flow can be periodically sent to cloud and displayed to a specific website. The system worked very well and the data can be summarized and used by operator as indicators of performance of the reverse osmosis machine. When water quality and performance decrease, operator must check filters membrane or water pump for further maintenance to support long life use. Although the sensors have been calibrated properly, some measurement errors occur. The measurement error percentages of TDS and pH are 0.62% and 0.94%, respectively. © 2022 IEEE.

关键词： Internet of things

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A Voice-Assisted Approach for Vehicular Data Querying from Automotive IoT-Based Databases

A Voice-Assisted Approach for Vehicular Data Querying from A...

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International Workshop on Secure Internet of Things (SIoT)

作者： Matheus Andrade Esther Wanderley Mariana Azevedo Thaís Medeiros Marianne Silva Ivanovitch Silva Daniel G. Costa Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte - Natal Brazil INEGI Faculty of Enzineering University of Porto Porto Portugal

The automotive industry's transformative conver-gence of cutting-edge technologies, such as the Internet of Things (loT), electronic voice assistants, and custom APls, have paved the way for a remarkable array of opportunities to enhance vehicle security while revolutionizing user-car interactions. Actually, when leveraging loT sensors of multiple types and configurations, their produced, collected, and stored real-time data may offer in-depth and comprehensive insights into vehicle information, opening many possibilities when loT-generated databases are created. In this context, the primary goal of this paper is to create a new approach based on the popular Amazon Alexa voice assistant, which would allow facilitated queries of vehicular data from cloud-based databases. For that, a new Alexa service (skill) is created, as well as a custom API, allowing easy access to different types of data previously retrieved from vehicular sensors and properly stored. Doing so, the implemented skill indirectly processes data from loT sensors through the custom API, enabling users to access vital vehicular information using intuitive voice commands, remotely. A case study in a real scenario is conducted to validate and confirm the feasibility of real-time vehicle information access, showcasing the benefits of the proposed approach when combined with other solutions in a macro vehicular-centric loT ecosystem.

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A Framework for Efficient Communication in IoT Applications in the Automotive Industry with OBD-II Edge

A Framework for Efficient Communication in IoT Applications ...

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International Workshop on Secure Internet of Things (SIoT)

作者： Morsinaldo Medeiros Mariana Azevedo Miguel Amaral Carlos M. D. Viegas Marianne Silva Ivanovitch Silva Daniel G. Costa Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte - Natal Brazil INEGI Faculty of Enzineering University of Porto Porto Portugal

Driven by the Internet of Things (IoT) revolution, the automotive industry is transforming itself significantly. Ve-hicles, once merely transportation machines, have now evolved into complex networks of sensors and actuators. This evolution heralds an era of intelligent and connected vehicles that collect and analyze real-time data, benefiting various sectors. In response to this scenario, this paper introduces a framework for collecting and processing vehicular information, leveraging the capabilities of Edge Computing. The framework integrates the OBD-II Edge (Freematics ONE+) technology for efficient data acquisition, harnessing the power of Edge Computing to enable real-time data processing and immediate response at the network's edge. Additionally, Node-RED and Kafka technologies are utilized for efficient data handling and routing, while the Timescale database ensures optimized storage of time-series data. Finally, the Streamlit tool provides an intuitive data visualization interface, enhancing data analysis and decision-making processes. Evaluated through a real-case scenario study, this proposal has proven viable and effective, providing analyses that directly contribute to vehicular safety and preventive maintenance, while also enhancing logistical operations in the field.

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Ball Position Transformation with Artificial Intelligence Based on Tensorflow Libraries 23

Ball Position Transformation with Artificial Intelligence Ba...

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23rd International Electronics Symposium, IES 2021

作者： Setiawardhana DIkairono, Rudy Dewantara, Bima Sena Bayu Pratama, Afis Asryullah Politeknik Elektronika Negeri Surabaya Dept of Informatic and Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept of Electrical Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Postgraduate Program Dept of Electrical Engineering Surabaya Indonesia

ISBN: (纸本)9781665443463

Research on wheeled soccer robots has been carried out by several researchers. This is due to the existence of national and international competitions. Previous research was to create a ball position transformation system with a modified method of neural network architecture. This research was developed by building an intelligent transformation system with the Tensorflow library. This transformation system aims to be able to directly measure the distance of objects in real terms without first changing the environmental image from an omni field to a flat plane with conventional camera calibration techniques. This process can replace manual calibration with a variety of field size changes The system can transform with mean error 0.0000026 on epoch 10000 using 'conda-tensorflowneural network' libraries. It can transform the position of the ball from the omni space to the cartesian space. This system was implemented on wheeled soccer robot as keeper. © 2021 IEEE.

关键词： Sports

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State of The Art: Fragility Function of electrical Power System

State of The Art: Fragility Function of Electrical Power Sys...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Elvy Sahnur Nasution Syahrial Syahrial Ira Devi Sara Yuwaldi Away Doctoral Program Postgraduate School of Engineering Universitas Syiah Kuala Universitas Muhammadiyah Sumatera Utara Banda Aceh Indonesia Dept. Electrical and computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia

Natural disasters, particularly earthquakes, can cause the electric power system to collapse, which can be brought on by one of the infrastructure breakdowns in the power system. Damaged power system can cause losses to electricity providers, electricity users, so it is necessary to understand the vulnerability of electric power system assets to earthquakes. The research fragility function presents the concept of fragility function in power system of the entire power system, showed fragility of power plant, transmission line, tower transmission, transformers, generator, and substation. Substation is more fragile than the others because substation consist of circuit breaker and disconnecting switch the most fragile asset. In addition, this paper also presents how to creating the fragility function and several solutions to increase the resilience of the electric power system to earthquakes.

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TinyML for Safe Driving: The Use of Embedded Machine Learning for Detecting Driver Distraction

TinyML for Safe Driving: The Use of Embedded Machine Learnin...

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Metrology for Automotive (MetroAutomotive), IEEE International Workshop on

作者： Thommas Flores Marianne Silva Mariana Azevedo Thais Medeiros Morsinaldo Medeiros Ivanovitch Silva Max Mauro Dias Santos Daniel G. Costa Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte Natal Brazil INEGI Faculty of Engineering University of Porto Porto Portugal Federal University of Technology Ponta Grossa Brazil

Among the main causes of road accidents, one of the most significant is related to driver distraction while driving, which is responsible for 18% of car accidents worldwide. This situation has demanded the development of mechanisms to automatically detect this dangerous behavior while driving. One of the computational solutions that has been considered viable to detect situations like this is the use of Convolutional Neural Networks (CNN), but some complex issues arise when deploying CNN models in microcontroller-based embedded devices with constrained processing and memory capabilities. In this context, this paper proposes a driver distraction detection system that achieves high accuracy (99.3%) and low latency (72ms) while requiring minimal computational resources (Peak- RAM of 164 KB and Flash of 52.7 KB). This solution exploiting Tiny Machine Learning (TinyML) algorithms was developed with the support of the Edge Impulse platform, used to perform the entire ML pipeline, from data pre-processing and ML model creation to deployment into an Arduino Portenta H7 board. By designing a driver assistance system that can be integrated into vehicles, it is expected that an affordable solution based on embedded machine learning is provided, tackling a real-world problem by potentially reducing accidents caused by driver distractions.

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