Adnan Mujezinović

The tutorial begins with a general overview of classical numerical methods used for the calculation
of electric fields and magnetic flux densities in the vicinity of overhead transmission lines. Analytical and
numerical approaches such as the charge simulation method and Biot-Savart law based method will be briefly
reviewed to provide the theoretical foundation. Following the classical calculation methods, the tutorial will
address the modeling and analysis of stationary AC corona on overhead transmission line conductors.
Furthermore, the influence of higher-order current harmonics on the distribution of magnetic flux density near
overhead lines will be examined. Subsequently, the tutorial introduces modern computational trends,
including the application of machine learning techniques and evolutionary algorithms for field estimation. A
method based on artificial neural networks (ANN) will be presented for estimating electric field intensity and
magnetic flux density using geometric parameters of overhead lines combined with voltage and current data.
Practical computational examples involving different line configurations and operational scenarios will be
included. The tutorial will highlight the importance of generating appropriate synthetic datasets based on
overhead line configuration algorithms to enhance the training and accuracy of machine learning models.
Further, an approach for estimating magnetic flux density near multi-system overhead transmission lines will
be outlined. The method synthesizes the results from individual three-phase systems to produce the total
magnetic flux density distribution in the vicinity of multi-circuit lines and transmission corridors with shared
infrastructure. A method based on artificial neural networks for estimating higher-order harmonics of magnetic
flux density due to current waveform distortion will also be introduced. In addition, the tutorial will explore the
use of metaheuristic algorithms for optimizing parametric functions that describe the spatial distribution of
magnetic flux density. These optimization techniques enable improved model fitting and higher accuracy
compared to traditional methods. The tutorial will conclude by demonstrating how the integration of classical
modeling techniques with modern machine learning and evolutionary algorithms offers a powerful, accurate,
and scalable framework for the estimation of electric and magnetic fields near high-voltage transmission lines.

Biography:

 Adnan Mujezinović received his M.Sc. and Ph.D. degrees in Electrical
Engineering from the Faculty of Electrical Engineering, University of Sarajevo,
Bosnia and Herzegovina, in 2011 and 2017, respectively. Since 2012, he has been
affiliated with the same faculty and currently holds the position of Associate
Professor at the Department of Electric Power Engineering. He is a member of the
IEEE and CIGRE organizations. He is actively involved in CIGRE activities and
currently serves as the Chairman of the D1 Committee of BH K CIGRE. He has
authored numerous papers published in international journals and conference
proceedings. His research interests include numerical modeling and calculation of
electromagnetic fields, cathodic protection systems, and overhead transmission
lines.

Address:Sarajevo, Bosnia & Herzegovina