30 citations · 33 across the 6 of their papers we have counts for
6 papers
Supervised Learning based Method for Condition Monitoring of Overhead Line Insulators using Leakage Current Measurement
Mile Mitrovic, Dmitry Titov, Klim Volkhov +5
As a new practical and economical solution to the aging problem of overhead line (OHL) assets, the technical policies of most power grid companies in the world experienced a gradua…
Data-Driven Stochastic AC-OPF using Gaussian Processes
Mile Mitrovic
The thesis focuses on developing a data-driven algorithm, based on machine learning, to solve the stochastic alternating current (AC) chance-constrained (CC) Optimal Power Flow (OP…
Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms
Ognjen Kundacina, Gorana Gojic, Mile Mitrovic +2
Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation.…
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
Mile Mitrovic, Ognjen Kundacina, Aleksandr Lukashevich +4
The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power gr…
Power System Anomaly Detection and Classification Utilizing WLS-EKF State Estimation and Machine Learning
Sajjad Asefi, Mile Mitrovic, Dragan Ćetenović +3
Power system state estimation is being faced with different types of anomalies. These might include bad data caused by gross measurement errors or communication system failures. Su…
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes
Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev +3
The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncert…