Power Systems Laboratory, Department of Electrical and Computer Engineering, Democritus University of Thrace, Komotini, Greece.
Modeling and analysis of power systems dynamics using system identification techniques.
With the advent of smart grids, the increased installation of phasor measurement units (PMUs) at transmission networks, frequency disturbance recorders (FDRs) and micro-PMUs (μ-PMUs) at distribution grids, the measurement-based approach on power systems modelling has gained significant scientific interest. Synchrophasor measurements can enable a wide range of applications. Among them, special interest is focused on the analysis of the dynamic performance and the stability of power systems. This is of utter importance due to the growing dynamic activity observed, especially in distribution networks by the operation of distributed renewable energy sources. For this purpose, system identification techniques are used as advanced mathematical tools to directly analyze the dynamic characteristics of power systems and develop models using recorded data sets. Several system identification techniques have been proposed in the literature. Also, towards this direction, different measurement-based is presented in detail. First, the state-of-the-art smart grid applications taking advantage of synchrophasor measurements are described categorically and discussed in detail. Next, the presentation focuses on the dynamic analysis of smart grids using synchrophasor measurements. The fundamentals of mode estimation and of the most known identification techniques are presented. The formulation of measurement-based approaches for dynamic load and active distribution networks modelling is also discussed. The demonstrated studies highlight the benefits and advances introduced by the measurement-based approach for the efficient operation of smart grids.
Theofilos Papadopoulos is Associate Professor with the Power Systems Laboratory, Department of Electrical and Computer Engineering, Democritus University of Thrace, Komotini, Greece. His research interests include power systems modeling, dynamic equivalencing, and computation of electromagnetic transients with emphasis on earth conduction effects. He has participated in several European and national Projects and been an author or co-author in more than 150 journal and conference papers on the above topics. He is a member of IEEE (SM18) and IET (MIET).
Brian K. Johnson
University Distinguished Professor in the Department of Electrical and Computer Engineering at the University of Idaho, USA.
AC and DC Side Protection of HVDC Systems
High-voltage direct current (HVDC) systems are becoming more common in modern power systems. HVDC system specific protection must be coordinated with protective systems on the ac network. Protective systems related to HVDC systems are different than those on the rest of the power system. One major difference is that HVDC converter stations are often purchased as a complete system based on project specifications, often including most, if not all the protection devices. Some HVDC system owners require that protective relays be selected according to their standard practices.
It is difficult to clearly distinguish between a control and a protection. In fact, the converter controls perform some HVDC protection functions using the same equipment that controls the converter in its normal operation. There are two commonly applied types of power electronic converters applied in HVDC systems, line-commutated current source converter (LCC) schemes (sometimes referred to HVDC classic) and voltage sourced converter (VSC) HVDC schemes, with an increasing percentage of new installations using VSC HVDC. The differences in basic operation of these converters also results in differences in their behavior in response to ac and dc side faults, resulting in differing protection requirements. This presentation will introduce LCC and VSC HVDC transmission systems and discuss ac and dc protection schemes for these systems. Multiterminal HVDC grids and HVDC circuit breakers will be introduced along with their impacts on protection system requirements and design.
Brian K. Johnson is the Schweitzer Engineering Laboratories Endowed Chair in Power Engineering and University Distinguished Professor in the Department of Electrical and Computer Engineering at the University of Idaho. He received a PhD degree in electrical engineering from the University of Wisconsin-Madison. His teaching and research interests include power system protection, integration of inverter-based generation, HVDC transmission, power systems transients, industrial control systems cybersecurity and power system resilience. Dr. Johnson is a registered professional engineer in the State of Idaho.
Edson Hirokazu Watanabe
COPPE / Universidade Federal do Rio de Janeiro, Brazil
HVDC and FACTS – Some Basic Concepts
This presentation introduces some basic concepts on line-commutated converter (LCC) high- voltage direct current (HVDC) transmission systems showing how this technology is being used in Brazil where a total of up to 20.6 GW of HVDC links are in operation now. High reliability, relatively low cost for very high power and log distance transmission and good performance is, in general, the main advantages of this technology. However, since it uses thyristor valves, commutation failure is a nightmare of the operators as the power transmission is interrupted for some hundreds of milliseconds when a commutation failure occurs at the inverter stations. Some commutation failure mitigation techniques will be discussed. Then the voltage source converter (VSC) HVDC system is presented as a new technology based on self-commutated switches. The modular multilevel converter (MMC) based on half-bridge or full-bridge submodules and their applications are presented. Some simulation studies for the Brazilian grid are presented showing great advantages of the MMC over LCC except for cost. Finally, some basic concepts on flexible AC transmission system (FACTS) devices for the first (thyristor based) and second (self- commutated switches) generations are presented as well as the concept of grid following and grid forming.
Edson H. Watanabe holds a degree in Electronic Engineering from the Polytechnic School of the Federal University of Rio de Janeiro (1975), a Master’s degree in Electrical Engineering from COPPE / Federal University of Rio de Janeiro (1976) and a PhD degree in Electrical Engineering from the Tokyo Institute of Technology (1981). He is currently a Full Professor at the Electrical Engineering Program at COPPE / Federal University of Rio de Janeiro. He has experience in Electrical Engineering, with emphasis on Power Electronics applied to Power Systems, working mainly on the following topics: Instantaneous Power Theory, Active Filters, FACTS and Alternative Energy Sources (wind, waves, solar and fuel cells). In 2005, he was admitted, in the class of Comendador, to the National Order of Scientific Merit; in 2013, he received the IEEE PES Nari Hingorani FACTS Award and was elected a member of the National Academy of Engineering. In 2015, he was elected a full member of the Brazilian Academy of Sciences. In 2016, he received the Friend of the Navy Medal. He was awarded the InRio Personality of the Year Award 2016 Edition. In 2017, he was elevated to the category of Fellow of the IEEE, received the Diploma of Merit from the Consulate-General of Japan, in Rio de Janeiro and was awarded the Medal of Merit Marshal Cordeiro de Farias, from the Escola Superior de Guerra. In April 2020, he was awarded the “Order of the Rising Sun, Golden Rays with Bows” by the Emperor of Japan. In March 2022 he received the “One step on electrotechnology – Look back to the future” Award from the IEE-Japan.
Onel Luis Alcaraz López
University of Oulu, Finland
Enabling Sustainable IoT: A look at RF Wireless Power Transfer Technology
Industry and academia are tirelessly pursuing the vision of a data-driven sustainable society, enabled by near-instant, secure, unlimited, and green connectivity. Stringent performance guarantees in terms of security and trust, throughput, sensing capabilities, dependability, scalability, and energy availability and efficiency are instrumental to such a vision. Our presentation is related to the latter aspect as we discuss sustainable Internet-of-Things networks, especially from an energy connectivity perspective. For this, we delve into a promising sustainability enabler, the radio frequency (RF) wireless power transfer technology, and overview its basics, potentials, recent advances, and key research directions. Finally, we discuss whether 6G will be the generation to first exploit the RF energy market.
Onel L. A. López (S’17-M’20) was born in Sancti-Spíritus, Cuba, in 1989. He received the B.Sc. (1st class honors, 2013), M.Sc. (2017) and D.Sc. (with distinction, 2020) degree in Electrical Engineering from the Central University of Las Villas (Cuba), the Federal University of Paraná (Brazil) and the University of Oulu (Finland), respectively. From 2013-2015 he served as a specialist in telematics at the Cuban telecommunications company (ETECSA). He is a collaborator to the 2016 Research Award given by the Cuban Academy of Sciences, a co-recipient of the 2019 IEEE European Conference on Networks and Communications (EuCNC) Best Student Paper Award, and the winner of the 2020 Best Doctoral Dissertation Award and 2022 Young Researcher Award in the field of technology in Finland. He currently holds an Assistant Professorship (tenure track) in sustainable wireless communications engineering at the Centre for Wireless Communications (CWC), University of Oulu, Finland.
José Mairton B. da Silva Jr.
Marie Skłodowska-Curie Postdoctoral Fellow (Princeton University, USA, and KTH Royal Institute of Technology, Sweden)
Wireless for Machine Learning: Fundamentals and Applications
A large part of machine learning services in the future will take place over wireless networks, and conversely, a large part of wirelessly transmitted information will be related to machine learning. As data generation increasingly takes place on devices without a wired connection, machine learning over wireless networks becomes critical. Many studies have shown that traditional wireless protocols are highly inefficient or unsustainable to support distributed machine learning services. This creates the need for new wireless communication methods, specifically on the medium access control and physical layers that will be arguably included in 6G. This seminar gives the fundamentals and application scenarios for these methods. Specifically, we investigate an Internet of Things scenario where simultaneous transmission of data and wireless power supports distributed machine learning tasks over the wireless network.
José Mairton B. da Silva, Jr. (Member, IEEE) received the Ph.D. from KTH Royal Institute of Technology, Stockholm, Sweden, in 2019. He is currently a Marie Skłodowska-Curie Postdoctoral Fellow with Princeton University, USA, and KTH Royal Institute of Technology, Sweden. He is starting as Assistant Professor in the Division of Computer Systems at Uppsala University from April 2023. He has served as the Secretary of the IEEE Communications Society Emerging Technology Initiative on Full Duplex Communications between 2018-2021. He has been involved in the organization of many IEEE conferences and workshops, including IEEE SECON 2022 (Co-chair), IEEE GLOBECOM 22 Workshop on Wireless Communications for Distributed Intelligence (Co-Chair). He gave several tutorials on “Wireless for Machine Learning” at many IEEE flagship conferences, including ICASSP, PIMRC, ICC, and GLOBECOM. His research interests include distributed machine learning and optimization over wireless communications.
Anderson C. A. Nascimento
University of Washington, Tacoma Campus, USA
Making Privacy Preserving Machine Learning Practical
Machine learning (ML) provides us with movie recommendations, self-driving cars, voice recognition and automated medical diagnostics. However, in order to benefit from such services, ML models need to be trained on users’ data. Whenever such data is sensitive, one faces a dilemma: data privacy or give up the convenience of ML powered services. In this talk we will survey and present recent advances in the field of privacy-preserving machine learning. We will show how advances in differential privacy, federated learning and secure multi-party computations are reconciling the benefits of ML with strong privacy guarantees.
Anderson C. A. Nascimento obtained his Ph.D. from the University of Tokyo in 2004. He is currently a senior and endowed professor with the University of Washington, Tacoma Campus. Previously, he was a professor with the Universidade de Brasilia and a researcher with the cryptography group of Nippon Telegraph and Telecom, Corp. Dr. Nascimento works in cryptography, information theory and information security. His current main area of research is in privacy enhancing techniques and their applications to machine learning.