3 papers
eess.SY2025
A Learning-Driven Stochastic Hybrid System Framework for Detecting Unobservable Contingencies in Power Systems
Hamid Varmazyari, Masoud H. Nazari
This paper presents a new learning based Stochastic Hybrid System (LSHS) framework designed for the detection and classification of contingencies in modern power systems. Unlike co…
eess.SY2025
A Learning-based Hybrid System Approach for Detecting Contingencies in Distribution Grids with Inverter-Based Resources
Hamid Varmazyari, Masoud H. Nazari
This paper presents a machine-learning based Stochastic Hybrid System (SHS) modeling framework to detect contingencies in active distribution networks populated with inverter-based…
eess.SY2025
Detecting Unobservable Contingencies in Active Distribution Systems Using a Stochastic Hybrid Systems Approach
Erfan Mehdipour Abadi, Hamid Varmazyari, Masoud H. Nazari
This paper introduces a distributed contingency detection algorithm for detecting unobservable contingencies in power distribution systems using stochastic hybrid system (SHS) mode…