Vladimir Katic


ADVANCED METHODS OF POWER QUALITY ANALYSIS

University of Novi Sad, Faculty of Technical Sciences

Vladimir Katic


ADVANCED METHODS OF POWER QUALITY ANALYSIS

University of Novi Sad, Faculty of Technical Sciences
+381 21 485 4547
katav@uns.ac.rs

Biography

Prof. Vladimir KATIĆ, the University of Novi Sad, the Faculty of Technical Sciences in Novi Sad, Serbia, received his B.Sc. degree from the University of Novi Sad in 1978 and M.Sc. and Ph.D. degrees from the University of Belgrade in 1981 and 1991, respectively, all in Electrical Engineering. He was also promoted to Honorary Professor of the University Politechnica in Timisoara, Romania, and received the Honorary Doctorate (Doctor Honoris Causa) from the Stefan Cell Mare University in Suceava, Romania.

He is the author or co-author of 22 scientific monographs, textbooks, and teaching materials (scripts). The most important are: “Power Electronics 1 – Components and AC/x converters” (2020), “Power Electronics 2 – DC/x converters and power supplies” (2020), “Power Quality – problems and laboratory exercises” (2018), “Renewable electrical energy sources – problems” (2018), “Real-Time Modeling of Power Electronics Converters” (2011), “Electric Power Quality” (2007), “Renewable Sources of Electrical Energy” (2007), “Microprocessor Applications in Power Engineering” (2006), and a Monograph “Electric Power Quality – Harmonics” (2002), all in Serbian. Prof. Katic is the author or co-author of more than 600 scientific papers published in international and national journals or conference proceedings. His papers have been cited over 1300 times with h=15 (according to the Scopus), and over 2600 times, with h=23 (according to Google Scholar). He is an active reviewer with over 800 reviews recorded at the Clarivate Web of Science. Prof. Katic has been the head, main researcher, and researcher in 14 international and around 50 national scientific projects or studies.

He is an Academic Editor and member of the Editorial Board of 21 scientific journals, and a Member of Program or Steering Committees of more than 75 International Conferences around the World. He chaired numerous international and national conferences, among which the International Conference EPE-PEMC 2012 ECCE Europe, EUROCON 2019, and the biannual International Symposium on Power Electronics (Ee) are the most known. Prof. Katić is a Life Senior Member of the IEEE (USA). He is also the Founder and the President of the Power Electronics Society of Serbia (Novi Sad, Serbia), a Member of the Executive Council of the European Power Electronics Association (EPE, Brussels, Belgium), a Member of the Executive Council of the Power Electronics and Motion Control (PEMC, Budapest, Hungary), a President of the Society of ETRAN (Belgrade, Serbia), and a Founder and a Member of the Executive Board of the National Committee of CIRED Serbia (Novi Sad, Serbia).

 

ADVANCED METHODS OF POWER QUALITY ANALYSIS

Prof. Vladimir Katić

University of Novi Sad, Faculty of Technical Sciences

ABSTRACT:

Power quality (PQ) issues have been present in the power system since its early beginning. Although the system itself, technology, measurement, and communications have been significantly improved, some of these issues, especially ones related to the delivered quality of the electricity are still present. In nowadays world that is focused on electrical energy as a form of green energy and in the modern power systems that are moving towards smart grids, power quality gains in importance. The “clean” power is not requested only for the industrial loads, and large computer or microprocessor-based systems, but also for the new loads in the form of electrical vehicle chargers, battery storage units, and all other new devices connected to the grid, including low-inertia distributed energy resources. Further, the possibility of having part of the system in the form of DC grids turns focus on DC power quality issues, like DC voltage variations and similar. All of this requires improvements or new approaches in the existing methods of the PQ analysis.

Different types of PQ issues are treated, from current and voltage distortions to different voltage and frequency disturbances. The analysis methods are based on traditional Nyquist ones and different approaches are available. Standard methods are focused on the time-frequency domain as it can represent all the defined, measured, or monitored issues. They use the specific feature(s) for each PQ parameter and compare it to the permissible values stipulated by different PQ standards to detect the critical values.

However, digitalization in power systems and extensive use of computers and other microprocessor-based equipment enable significant improvement in PQ analysis methods. Modern methods use different digital signal analysis techniques and treat the digital signatures of the PQ disturbances with advanced digital signal processing methods, like parametric, nonparametric, or hybrid ones. Recently, compressive sensing methods have been introduced for signal digital treatment and transmission. Also, the disturbances classification, characterization, and possible localization become very important for a fast and adequate response, so artificial intelligence, through machine learning has been proposed or considered. Furthermore, the protection from some PQ issues and their fast mitigation requires suitable prediction or forecasting, which brings an additional aspect of importance to adequate analysis methods.

The lecture is intended to give a fast introduction to the PQ and the most significant issues. It will give an overview of their characteristic features (in synthetic or real form), and different methods in their detection, classification, and characterization. The methods using harmonic footprint as a feature, as well as some of the deep-learning algorithms for voltage sags analysis, will be presented in more detail. Also, some case studies and examples will be presented to enrich the explanations and bring practical experience.

Keywords: Electric Power Quality, Digital signal processing, PQ features, Harmonic footprint, Deep-learning.