Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …

The dynamic nature of sustainable energy and electric systems can vary significantly along
with the environment and load change, and they represent the features of multivariate, high …

In this paper, we review past (including very recent) research considerations in using
reinforcement learning (RL) to solve electric power system decision and control problems …

This paper reviews existing works on (deep) reinforcement learning considerations in
electric power system control. The works are reviewed as they relate to electric power …

With dramatic breakthroughs in recent years, machine learning is showing great potential to
upgrade the toolbox for power system optimization. Understanding the strength and …

Integration of large-scale renewable energy sources in legacy power system has negative
impact on inertia and low-frequency oscillations (LFOs). Critical LFO modes are the primary …

The impact of wind turbine system (WTS) on power system stability is elaborately addressed
in the literature. However, low-frequency oscillations, especially inter-area oscillations …

Dam** of low-frequency oscillations (LFOs) has become a critical challenge in modern
power system. Since interarea LFO modes are associated with power oscillation, therefore …

This paper presents a real-time wide-area dam** controller design based on a hybrid
intelligent and direct method to improve the power system transient stability. The algorithm …

In this paper, an intelligent power system stabilizer based on a stable and optimal hybrid
learning-based adaptive control architecture is proposed which is evolved from approximate …