Jul 10, 2020 · This paper proposes a data-driven control scheme, based on Reinforcement Learning (RL), for grid-forming Voltage Source Converters (VSCs),
This paper proposes a data-driven control scheme, based on Reinforcement Learning (RL), for grid-forming Voltage. Source Converters (VSCs), with the goal of ...
MDP formulations of primary power/frequency control define states based on physical variables such as voltage magnitudes, phase angles, power flows, ...
Jul 13, 2020 · This paper proposes a data-driven control scheme, based on Reinforcement Learning (RL), for grid-forming Voltage Source Converters (VSCs), with ...
Jun 3, 2024 · This dissertation addresses the urgent challenges faced by modern power systems due to the high penetration of renewable energy sources, ...
This paper proposes a reinforcement learning-based approach for optimal transient frequency control in power systems with stability and safety guarantees.
Missing: Fast | Show results with:Fast
The potential of using Fast Frequency Response (FFR) to enhance frequency control in power systems with low inertia is investigated in detail.
This work proposes a novel inertia estimation technique based on convolutional neural networks (CNN) that use local frequency measurements.
Missing: Reinforcement | Show results with:Reinforcement
In this paper, an extended virtual synchronous machine (VSM) control strategy is proposed to provide better frequency support in the hybrid low-inertia grids.
Missing: Reinforcement Learning
A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems · Engineering, Computer Science. 2020 52nd North American Power ...