State Forecasting Based on Artificial Neural Ne1works
نویسندگان
چکیده
State forecasting is a powerful tool to enhance the noise suppression ability of the state estimation process of a power system. Artificial neural networks can perceive complex nonlinear interactions among variables that improve the predictions accuracy and robustness. This paper investigates the applicability of artificial neural networks to state forecasting . Nonlinear autoregressive neural models are proposed and their performance are evaluated for an entire day using the IEEE-24 bus system data.
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