46 citations · 54 across the 14 of their papers we have counts for
6 papers · 1 filter
Quantum-Accelerated Deep Reinforcement Learning for Frequency Regulation Enhancement
Amin Masoumi, Mert Korkali
In modern power systems, frequency regulation is a fundamental prerequisite for ensuring system reliability and assessing the robustness of expansion projects. Conventional feedbac…
Quantum-Embedded Dynamic Security Control using Hybrid Deep Reinforcement Learning
Amin Masoumi, Mert Korkali
Dynamic security control (DSC) is considered a pivotal step for the future power grid, which is increasingly penetrated by inverter-based resources. However, the efficiency of such…
Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning
Amin Masoumi, Mert Korkali
This paper introduces a framework to address the critical loss of transient stability caused by reduced inertia in grids with high inverter-based resource (IBR) penetration. The pr…
Deep-Learning-Directed Preventive Dynamic Security Control via Coordinated Demand Response
Amin Masoumi, Mert Korkali
Unlike common faults, three-phase short-circuit faults in power systems pose significant challenges. These faults can lead to out-of-step (OOS) conditions and jeopardize the system…
Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment
Benjamin M. Peter, Mert Korkali
The increasingly challenging task of maintaining power grid security requires innovative solutions. Novel approaches using reinforcement learning (RL) agents have been proposed to…
Fast Critical Clearing Time Calculation for Power Systems with Synchronous and Asynchronous Generation
Xuezao Wang, Yijun Xu, Wei Gu +4
The increasing penetration of renewables is replacing traditional synchronous generation in modern power systems with low-inertia asynchronous converter-interfaced generators (CIGs…