Convergence of direct heuristic dynamic programming in power system stability control

Chao Lu; Jennie Si; Xiaorong Xie; Jie Song

doi:10.1109/IJCNN.2007.4371079

Convergence of direct heuristic dynamic programming in power system stability control

Chao Lu, Jennie Si, Xiaorong Xie, Jie Song

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

In this paper a neural network-based approximate dynamic programming method, namely direct heuristic dynamic programming (direct HDP), is applied to power system stability control. Direct HDP makes use of learning and approximation to address nonlinear system control problems under uncertainty. The contribution of the paper includes a convergence proof of the direct HDP algorithm using an LQR framework. Under this setting, the paper proposes a direct HDP learning control algorithm for a static var compensator (SVC) supplementary damping control in a standard benchmark power system. The results are used to evaluate the online learning ability of the proposed direct HDP controller, and also to demonstrate that the learning controller does converge to the theoretical limit as derived.

Original language	English (US)
Title of host publication	The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages	908-913
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2007.4371079
State	Published - 2007
Event	2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States Duration: Aug 12 2007 → Aug 17 2007

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)	1098-7576

Other

Other	2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/Territory	United States
City	Orlando, FL
Period	8/12/07 → 8/17/07

Keywords

Direct heuristic dynamic programming
Linear quadratic regulator
Neural networks
Power system stability control

ASJC Scopus subject areas

Software

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10.1109/IJCNN.2007.4371079

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Lu, C., Si, J., Xie, X., & Song, J. (2007). Convergence of direct heuristic dynamic programming in power system stability control. In The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings (pp. 908-913). Article 4371079 (IEEE International Conference on Neural Networks - Conference Proceedings). https://doi.org/10.1109/IJCNN.2007.4371079

Convergence of direct heuristic dynamic programming in power system stability control. / Lu, Chao; Si, Jennie; Xie, Xiaorong et al.
The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings. 2007. p. 908-913 4371079 (IEEE International Conference on Neural Networks - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lu, C, Si, J, Xie, X & Song, J 2007, Convergence of direct heuristic dynamic programming in power system stability control. in The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings., 4371079, IEEE International Conference on Neural Networks - Conference Proceedings, pp. 908-913, 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, United States, 8/12/07. https://doi.org/10.1109/IJCNN.2007.4371079

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abstract = "In this paper a neural network-based approximate dynamic programming method, namely direct heuristic dynamic programming (direct HDP), is applied to power system stability control. Direct HDP makes use of learning and approximation to address nonlinear system control problems under uncertainty. The contribution of the paper includes a convergence proof of the direct HDP algorithm using an LQR framework. Under this setting, the paper proposes a direct HDP learning control algorithm for a static var compensator (SVC) supplementary damping control in a standard benchmark power system. The results are used to evaluate the online learning ability of the proposed direct HDP controller, and also to demonstrate that the learning controller does converge to the theoretical limit as derived.",

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AU - Si, Jennie

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N2 - In this paper a neural network-based approximate dynamic programming method, namely direct heuristic dynamic programming (direct HDP), is applied to power system stability control. Direct HDP makes use of learning and approximation to address nonlinear system control problems under uncertainty. The contribution of the paper includes a convergence proof of the direct HDP algorithm using an LQR framework. Under this setting, the paper proposes a direct HDP learning control algorithm for a static var compensator (SVC) supplementary damping control in a standard benchmark power system. The results are used to evaluate the online learning ability of the proposed direct HDP controller, and also to demonstrate that the learning controller does converge to the theoretical limit as derived.

AB - In this paper a neural network-based approximate dynamic programming method, namely direct heuristic dynamic programming (direct HDP), is applied to power system stability control. Direct HDP makes use of learning and approximation to address nonlinear system control problems under uncertainty. The contribution of the paper includes a convergence proof of the direct HDP algorithm using an LQR framework. Under this setting, the paper proposes a direct HDP learning control algorithm for a static var compensator (SVC) supplementary damping control in a standard benchmark power system. The results are used to evaluate the online learning ability of the proposed direct HDP controller, and also to demonstrate that the learning controller does converge to the theoretical limit as derived.

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KW - Linear quadratic regulator

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Convergence of direct heuristic dynamic programming in power system stability control

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