| Title |
Parameter Estimation Using an Off-Policy Learning-Based LQR and Its Application to a Disturbance Observer Design for Out of Step Detection of a Power System |
| Authors |
양선직(Sun Jick Yang) ; 장수영(Su Young Jang) ; 손영익(Young Ik Son) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.1.130 |
| Keywords |
Off-policy learning-based LQR; Reinforcement learning; Parameter estimation; Disturbance observer; Power system; Out-of-step detection |
| Abstract |
In this paper, a reinforcement learning-based Linear Quadratic Regulator(LQR) design method has been adopted to identify unknown system parameters. The off-policy learning-based LQR can obtain the optimal control gain through an iteration technique known as policy iteration, without using the system model parameters. Augmented states, using the system output integration, can help to alleviate the rank condition on the proposed parameter estimation method. Increasing the system model information accuracy allows the disturbance observers to detect various system faults with the least amount of estimation error. The line fault detection ability of a power system for out-of-step prediction has been studied by applying the proposed parameter estimation scheme to a single machine infinite bus system. Simulation results show that both the system parameters and the external disturbance can be successfully estimated through the proposed method |