Abstract
This paper introduces a novel machine learning (ML) model for the lightning performance analysis of overhead distribution lines (OHLs), which facilitates a data-centrist and statistical view of the problem. The ML model is a bagging ensemble of support vector machines (SVMs), which introduces two significant features. Firstly, support vectors from the SVMs serve as a scaffolding, and at the same time give rise to the so-called curve of limiting parameters for the line. Secondly, the model itself serves as a foundation for the introduction of the statistical safety factor to the lightning performance analysis of OHLs. Both these aspects bolster an end-to-end statistical approach to the OHL insulation coordination and lightning flashover analysis. Furthermore, the ML paradigm brings the added benefit of learning from a large corpus of data amassed by the lightning location networks and fostering, in the process, a “big data” approach to this important engineering problem. Finally, a relationship between safety factor and risk is elucidated. THe benefits of the proposed approach are demonstrated on a typical medium-voltage OHL.
Petar Sarajčev
Full Professor | Department of Power Grids and Substations
Dino Lovrić
Associate Professor | Department of Theoretical Electrical Engineering and Modelling
Associate professor at the Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture in Split, with reserch focused on the development of numerical models of grounding systems in various types of soil, particularly in scenarios involving the dissipation of alternating current and transient currents caused by lightning strikes or switching overvoltages, also involved in developing models of dynamic and transient processes in power systems using modern numerical methods.
