Power oscillation damping control using PI-neuron network controller for distributed generator grid connection with MVDC

Kittaya Somsai, Nakarin Sripanya, Chaiyut Sumpavakup


Distributed generator (DG) connection to the system with the DC grid called medium voltage direct current (MVDC) grid connection has received attention and gradually integrated into the distribution grid. The linear controller, such as the PI controller, usually uses the MVDC grid control for power oscillation damping. The PI controller is limited and does not show satisfactory results when the load and parameters of the system itself are changed. This paper proposes the PI with neuron network (NN) as a feed-forward controller (PINNF) to improve the control of the DG grid connection with the MVDC. The proposed PINNF controller is applied to control the power oscillation damping. Since the NN can estimate the proper feed-forward control signals in each situation to the control system, the proposed PINNF controller performs better than the conventional PI controller. The effectiveness of the proposed PINNF controller is validated using nonlinear dynamic simulations on the MATLAB/Simulink program. Four case tests are presented and discussed in this paper. Results indicate the improvement of power oscillation damping stability and performance in the MVDC grid connection with the proposed PINNF controller.


Distributed generator; Grid connection; MVDC; Neural network; Power oscillation

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DOI: http://doi.org/10.11591/ijpeds.v13.i4.pp2541-2554


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