Abstract
A novel nonlinear optimal controller for a rectifier in HVDC transmission system, using artificial neural networks, is presented in this paper. The action dependent heuristic dynamic programming(ADHDP), a member of the adaptive critic designs family is used for the design of the rectifier neurocontroller. This neurocontroller provides optimal control based on reinforcement learning and approximate dynamic programming(ADP). A series of simulations for a rectifier in dulble-ended unipolar HVDC system model with proposed neurocontroller and conventional PI controller were carried out in MATLAB/Simulink environment. Simulation results are provided to show that the proposed controller performs better than the conventional PI controller. the current of DC line in HVDC system with the proposed controller can quickly track with the changing of the reference current and prevent the occurrence of the current of DC line collapse when the large disturbances occur.
This work was supported in part by the Natural Science Foundation of China under Grant 60964002; the Natural Science Foundation of Guangxi Province of China under Grant 0991057; the Science & Research Foundation of Educational Commission of Guangxi Province of China under Grant 200808MS03.
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Song, C., Zhou, X., Lin, X., Song, S. (2011). Optimization Control of Rectifier in HVDC System with ADHDP. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21111-9_16
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DOI: https://doi.org/10.1007/978-3-642-21111-9_16
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