1기 무한모선 시스템의 선로 고장판별을 위한 강화학습 기반 외란관측기 설계

다국어 초록 (Multilingual Abstract)

According to the increase of electric power demand in the modern society the power system is gradually expanding. This results in a growing need for an intelligent method of fast determination and protection against various failures in the power system. As the computer platform is improved, the system fault detection and reliable protection devices have been trying to enhance their performances using artificial intelligence techniques. If a failure occurs in the single-machine infinite bus(SMIB) system. the electrical output of the generator changes, which can be regarded as a result of an external disturbance input. This paper presents a line fault detection method by using a reinforcement learning-based disturbance observer that estimates the magnitude of the equivalent disturbance. Reinforcement learning is an algorithm that models the relationship between the behavior of an agent and the reward from environment. This paper has adopted the Deep Q-Network for training of the proposed disturbance observer. The performance of the proposed reinforcement learning-based disturbance observer is verified by computer simulations. The results show that the disturbance can be estimated successfully and the estimate can be used to detect the line fault.

목차 (Table of Contents)

• Abstract
• 1. 서론
• 2. 본론
• 3. 결론
• References

참고문헌 (Reference)

1 남순열, "송전 선로의 사고 거리에 따른 특성 주파수 해석" 대한전기학회 53 (53): 432-437, 2004

2 H. Shim, "Yet another tutorial of disturbance observer : robust stabilization and recovery of nominal performance" 14 (14): 237-249, 2016

3 X. Glorot, "Understanding the difficulty of training deep feedforward neural networks" 9 : 249-256, 2010

4 Wei Yao, "TCSC Nonlinear Adaptive Damping Controller Design Based on RBF Neural Network to Enhance Power System Stability" 대한전기학회 8 (8): 252-261, 2013

5 A. Juliani, "Simple Reinforcement Learning with Tensorflow" Hanbit Publishing Network 2017

6 S. Das, "Secured zone-3 protection during power swing and voltage instability : an online approach" 11 (11): 437-446, 2017

7 A. Levant, "Robust exact differentiation via sliding mode technique" 34 (34): 379-384, 1998

8 R. S. Sutton, "Reinforcement Learning: An Introduction" MIT press 1998

9 Ali Karami, "Radial Basis Function Neural Network for Power System Transient Energy Margin Estimation" 대한전기학회 3 (3): 468-475, 2008

10 C. J. C. H. Watkins, "Q-learning" 8 (8): 279-292, 1992

11 G. W. Kim, "Power System Analysis Using MATLAB 1" UUP 2005

12 J. D. Glover, "Power System Analysis & Design" Cengage Learning 2016

13 A. R. Bergen, "Power System Analysis" Prentice Hall 2000

14 H. Saadat, "Power System Analysis" McGraw-Hill 2002

15 D. Silver, "Mastering the game of Go with deep neural networks and tree search" 529 : 484-489, 2016

16 V. Mnih, "Human-level control though deep reinforcement learning" 518 : 529-533, 2015

17 A. P. Sakis Meliopoulos, "Dynamic state estimation-based protection : Status and Promise" 32 (32): 320-330, 2017

18 J. Chang, "Dynamic state estimation using a nonlinear observer for optimal seriescapacitor switching control" 19 (19): 441-447, 1997

19 Y. Cui, "Dynamic state estimation assisted out-of-step detection for generators using angular difference" 32 (32): 1441-1449, 2017

20 R. S. Sutton, "Dyna, an integrated architecture for learning, planning, and reacting" 2 (2): 160-163, 1991

21 W. H. Chen, "Disturbance observer-based control and related methods-An overview" 63 (63): 1083-1095, 2016

22 이동규, "DFT 기반의 개선된 페이저 연산 기법을 적용한 거리계전 알고리즘" 대한전기학회 59 (59): 1360-1365, 2010

23 S. Russel, "Artificial Intelligence: A Modern Approach" Prentice Hall 2003

24 S. Paudyal, "Application of equal area criterion conditions in the time domain for out-of-step protection" 25 (25): 600-609, 2010

25 M. J. Reddy, "Adaptive-neuro-fuzzy inference system approach for transmission line fault classification and location incorporating effects of powr swings" 2 (2): 235-244, 2008

26 D. P. Kingma, "Adam: a method for stochastic optimization" 2015

27 Y. I. Son, "A robust state observer using multiple integrators for multivariable LTI systems" E93-A (E93-A): 981-984, 2010

28 E. Farantatos, "A predictive generator out-of-step protection and transient stability monitoring scheme enabled by a distributed dynamic state estimator" 31 (31): 1826-1835, 2016