A Neural Network-Based Power System Stabilizer using Power Flow Characteristics - Energy Conversion, IEEE Transactions on
نویسندگان
چکیده
Absfrad A neural network-based Power System Stabilizer (Neuro-PSS) is designed for a generator connected to a multimachine power system utilizing the nonlinear power flow dynamics. The uses of power flow dynamics provide a PSS for a wide range 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-Thorough-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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