A neural network-based power system stabilizer using power flowcharacteristics |
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Authors: | Young-Moon Park Myeon-Song Choi Lee KY |
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Affiliation: | Dept. of Electr. Eng., Seoul Nat. Univ.; |
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Abstract: | 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 |
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