TY - JOUR
T1 - Wide-Area Measurement System-Based Low Frequency Oscillation Damping Control through Reinforcement Learning
AU - Hashmy, Yousuf
AU - Yu, Zhe
AU - Shi, Di
AU - Weng, Yang
N1 - Funding Information:
Manuscript received January 23, 2020; revised May 27, 2020; accepted July 4, 2020. Date of publication July 10, 2020; date of current version October 21, 2020. This work was supported by SGCC Science and Technology Program under Project “AI Based Oscillation Detection and Control” under Contract SGJS0000DKJS1801231. Paper no. TSG-00112-2020. (Corresponding author: Yang Weng.) Yousuf Hashmy is with the AI and System Analytics, GEIRI North America, San Jose, CA 95134 USA, and also with the Department of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281 USA (e-mail: shashmy@asu.edu).
Publisher Copyright:
© 2010-2012 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Ensuring the stability of power systems is gaining more attention today than ever before due to the rapid growth of uncertainties in load and increased renewable energy penetration. Lately, wide-area measurement system (WAMS)-based centralized controlling techniques are offering flexibility and more robust control to keep the system stable. WAMS-based controlling techniques, however, face pressing challenges of irregular delays in long-distance communication channels and subsequent responses of equipment to control actions. This paper presents an innovative control strategy for damping down low-frequency oscillations in transmission systems. The method uses a reinforcement learning technique to overcome the challenges of communication delays and other non-linearity in wide-area damping control. It models the traditional problem of oscillation damping control as a novel faster exploration-based deep deterministic policy gradient (DDPG-S). An effective reward function is designed to capture necessary features of oscillations enabling timely damping of such oscillations, even under various kinds of uncertainties. A detailed analysis and a systematically designed numerical validation are presented to prove feasibility, scalability, interpretability, and comparative performance of the modelled low-frequency oscillation damping controller. The benefit of the technique is that stability is ensured even when uncertainties of load and generation are on the rise.
AB - Ensuring the stability of power systems is gaining more attention today than ever before due to the rapid growth of uncertainties in load and increased renewable energy penetration. Lately, wide-area measurement system (WAMS)-based centralized controlling techniques are offering flexibility and more robust control to keep the system stable. WAMS-based controlling techniques, however, face pressing challenges of irregular delays in long-distance communication channels and subsequent responses of equipment to control actions. This paper presents an innovative control strategy for damping down low-frequency oscillations in transmission systems. The method uses a reinforcement learning technique to overcome the challenges of communication delays and other non-linearity in wide-area damping control. It models the traditional problem of oscillation damping control as a novel faster exploration-based deep deterministic policy gradient (DDPG-S). An effective reward function is designed to capture necessary features of oscillations enabling timely damping of such oscillations, even under various kinds of uncertainties. A detailed analysis and a systematically designed numerical validation are presented to prove feasibility, scalability, interpretability, and comparative performance of the modelled low-frequency oscillation damping controller. The benefit of the technique is that stability is ensured even when uncertainties of load and generation are on the rise.
KW - Wide-area networks
KW - damping control
KW - low frequency oscillations
KW - reinforcement learning
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U2 - 10.1109/TSG.2020.3008364
DO - 10.1109/TSG.2020.3008364
M3 - Article
AN - SCOPUS:85094826674
SN - 1949-3053
VL - 11
SP - 5072
EP - 5083
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 6
M1 - 9138480
ER -