IntelliHealer
An imitation and reinforcement learning platform for self-healing distribution networks. IntelliHealer uses imitation learning framework to learn restoration policy for distribution system service restoration so as to perform the restoration actions (tie-line switching and reactive power dispatch) in real time and in embedded environment.
The toolkit can be found: https://github.com/whoiszyc/IntelliHealer
The work is published in: Y. Zhang, F. Qiu, T. Hong, Z. Wang, F. Li, “Hybrid imitation learning for real-time service restoration in resilient distribution systems,” IEEE Trans. Ind. Informat., vol. 18, no. 3, pp. 2089–2099, 2022. [arXiv][IEEE][Open-source Toolkit: IntelliHealer] [Google]
Andes-Gym
A versatile environment for deep reinforcement learning research in power system dynamic control. Currently, a frequency control use case is included.
The toolkit can be found: https://github.com/sensl/andes_gym
The work is published in: H. Cui, Y. Zhang, “Andes-gym: a versatile environment for deep reinforcement learning in power systems,” in Proc. IEEE Power Energy Soc. Gen. Meeting, Denver, CO, USA, Jul. 17–21, 2022, pp. 1–5. Best paper award [Open-source Toolkit: Andes-gym] [Arxiv] [IEEE] [Google]