TY - GEN
T1 - Direct neural dynamic programming method for power system stability enhancement
AU - Lu, Chao
AU - Si, Jennie
AU - Xie, Xiaorong
AU - Yang, Lei
PY - 2005
Y1 - 2005
N2 - A neural network-based approximate dynamic programming (ADP) method, the direct neural dynamic programming (direct NDP), is introduced in this paper. The paper covers the basic principle of this learning scheme and an illustrative example of how direct NDP can be implemented. The paper focuses on how direct NDP can be applied to power system stability control. In this case direct NDP is based on real-time system measurements provided by wide area measurement system (WAMS) to compensate for nonlinearities and uncertainties in the system. The learning objective used in controller design makes use of a reward function that reflects system global characteristics if available. This learning control mechanism is adopted in the implementation of a static var compensator (SVC) supplementary damping control and two DC power modulation control systems. The design and evaluation of the learning controller and the system performance are evaluated based on simulations of a standard 2-area system. Results demonstrate the adaptive and learning features of the neural controller which is advantageous over traditional control designs.
AB - A neural network-based approximate dynamic programming (ADP) method, the direct neural dynamic programming (direct NDP), is introduced in this paper. The paper covers the basic principle of this learning scheme and an illustrative example of how direct NDP can be implemented. The paper focuses on how direct NDP can be applied to power system stability control. In this case direct NDP is based on real-time system measurements provided by wide area measurement system (WAMS) to compensate for nonlinearities and uncertainties in the system. The learning objective used in controller design makes use of a reward function that reflects system global characteristics if available. This learning control mechanism is adopted in the implementation of a static var compensator (SVC) supplementary damping control and two DC power modulation control systems. The design and evaluation of the learning controller and the system performance are evaluated based on simulations of a standard 2-area system. Results demonstrate the adaptive and learning features of the neural controller which is advantageous over traditional control designs.
KW - Direct neuro dynamic programming
KW - Power system stability control
KW - Wide area measurement system
UR - http://www.scopus.com/inward/record.url?scp=33847365758&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33847365758&partnerID=8YFLogxK
U2 - 10.1109/ISAP.2005.1599252
DO - 10.1109/ISAP.2005.1599252
M3 - Conference contribution
AN - SCOPUS:33847365758
SN - 1599750287
SN - 9781599750286
T3 - Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05
SP - 128
EP - 135
BT - Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05
T2 - 13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05
Y2 - 6 November 2005 through 10 November 2005
ER -