TY - JOUR
T1 - Direct heuristic dynamic programming for damping oscillations in a large power system
AU - Lu, Chao
AU - Si, Jennie
AU - Xie, Xiaorong
N1 - Funding Information:
Manuscript received July 30, 2007; revised February 6, 2008. This work was supported in part by the National Key Technology R&D Program under Grant 2006BAA02A17 and in part by the Tsinghua Basic Research Foundation. The work of J. Si was supported by the National Science Foundation under Grant ECS-0401405. This paper was recommended by Guest Editor F. Lewis.
PY - 2008/8
Y1 - 2008/8
N2 - This paper applies a neural-network-based approximate dynamic programming method, namely, the direct heuristic dynamic programming (direct HDP), to a large power system stability control problem. The direct HDP is a learning- and approximation-based approach to addressing nonlinear coordinated control under uncertainty. One of the major design parameters, the controller learning objective function, is formulated to directly account for network-wide low-frequency oscillation with the presence of nonlinearity, uncertainty, and coupling effect among system components. Results include a novel learning control structure based on the direct HDP with applications to two power system problems. The first case involves static var compensator supplementary damping control, which is used to provide a comprehensive evaluation of the learning control performance. The second case aims at addressing a difficult complex system challenge by providing a new solution to a large interconnected power network oscillation damping control problem that frequently occurs in the China Southern Power Grid.
AB - This paper applies a neural-network-based approximate dynamic programming method, namely, the direct heuristic dynamic programming (direct HDP), to a large power system stability control problem. The direct HDP is a learning- and approximation-based approach to addressing nonlinear coordinated control under uncertainty. One of the major design parameters, the controller learning objective function, is formulated to directly account for network-wide low-frequency oscillation with the presence of nonlinearity, uncertainty, and coupling effect among system components. Results include a novel learning control structure based on the direct HDP with applications to two power system problems. The first case involves static var compensator supplementary damping control, which is used to provide a comprehensive evaluation of the learning control performance. The second case aims at addressing a difficult complex system challenge by providing a new solution to a large interconnected power network oscillation damping control problem that frequently occurs in the China Southern Power Grid.
KW - Approximate dynamic programming (ADP)
KW - Direct heuristic dynamic programming (direct HDP)
KW - Neural networks
KW - Power system stability control
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U2 - 10.1109/TSMCB.2008.923157
DO - 10.1109/TSMCB.2008.923157
M3 - Article
C2 - 18632392
AN - SCOPUS:49049106959
SN - 1083-4419
VL - 38
SP - 1008
EP - 1013
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 4
ER -