Impact of signal transmission delays on power system damping control using heuristic dynamic programming
Date of Original Version
In this paper, the impact of signal transmission delays on static VAR compensator (SVC) based power system damping control using reinforcement learning is investigated. The SVC is used to damp low-frequency oscillation between interconnected power systems under fault conditions, where measured signals from remote areas are first collected and then transmitted to the controller as the inputs. Inevitable signal transmission delays are introduced into such design that will degrade the dynamic performance of SVC and in the worst case, cause system instability. The adopted reinforcement learning algorithm, called goal representation heuristic dynamic programming (GrHDP), is employed to design the SVC controller. Impact of signal transmission delays on the adopted controller is investigated with fully transient model based time-domain simulation in Matlab/Simulink environment. The simulation results on a four-machine two-area benchmark system with SVC demonstrate the effectiveness of the adopted algorithm on damping control and the impact of signal transmission delays.
IEEE Symposium on Computational Intelligence Applications in Smart Grid, CIASG
Tang, Yufei, Xiangnan Zhong, Zhen Ni, Jun Yan, and Haibo He. "Impact of signal transmission delays on power system damping control using heuristic dynamic programming." IEEE Symposium on Computational Intelligence Applications in Smart Grid, CIASG 2015-January, January (2015). doi:10.1109/CIASG.2014.7011567.