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A neural network-based power system stabilizer using power flow characteristics

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3 Author(s)
Young-Moon Park ; Dept. of Electr. Eng., Seoul Nat. Univ., South Korea ; Myeon-Song Choi ; Lee, K.Y.

A neural network-based power system stabilizer (neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The use of power flow dynamics provides a PSS for a wide range of operation with reduced size neural networks. The neuro-PSS consists of two neural networks: neuro-identifier and neuro-controller. The low-frequency oscillation is modeled by the neuro-identifier using the power flow dynamics, then a generalized backpropagation-through-time (GBTT) algorithm is developed to train the neuro-controller. The simulation results show that the neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS

Published in:

Energy Conversion, IEEE Transactions on  (Volume:11 ,  Issue: 2 )