Adaptive Power System Emergency Control Using Deep Reinforcement Learning

By Qiuhua Huang, Renke Huang, Weituo Hao, Jie Tan, Rui Fan, Zhenyu Huang. IEEE Transactions on Smart Grid, 2019

348 citations recorded by the source corpus.

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived “worst” case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL) by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named Reinforcement Learning for Grid Control (RLGC) has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Robustness of the developed DRL method to different simulation scenarios, model parameter uncertainty and noise in the observations is investigated. Extensive case studies performed in both the two-area, four-machine system and the IEEE 39-bus system have demonstrated excellent performance and robustness of the proposed schemes.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ef4fbaaeb68d76b5840f64d0a7da88945eaafe8f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Frede Blaabjerg
2. Zhe Chen
3. Yujie Tang
4. Xin Chen
5. Guannan Qu
6. Steven H. Low
7. Na Li
8. Xiongfei Wang

In Electrical and Electronic Engineering

1. Na Li
2. Steven H. Low
3. Yujie Tang
4. Zhe Chen
5. Frede Blaabjerg
6. Xin Chen
7. Guannan Qu
8. Xiongfei Wang

Citations in the corpus

• Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges (2021, methodology): “…of RL in power systems include electricity market [140], [141], network reconfiguration [142], service restoration [143], [144], emergency control [145], maximum power point tracking [146], [147], cyber security [148], [149], maintenance scheduling [150], protective relay control [151], electric…”
• Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review (2020, methodology): “Reference [97] proposes a DQN-based power system emergency control algorithm via under-voltage load shed‐ ding and generator dynamic braking.”