Identification Method of Primary Frequency Modulation Capability of Thermal Power Plant based on BO-LSTM Network
Improving the identification of primary frequency modulation capability of thermal power plants is helpful to assist the dispatching of power grid and ensure the safe and stable operation of the power grid.Therefore,this paper proposed a method for identifying the primary frequency modulation capability of thermal power plants based on the Bayesian optimization(BO)algorithm and long short-term memory(LSTM)network,which achieved accurate modeling of the primary frequency modulation capability of thermal power plants.Firstly,the input feature variables of the model were established through the mech-anism analysis of plant and the correlation analysis of parameters.Then,the LSTM network structure was optimized by using Bayesian algorithm to obtain the primary frequency modulation capability identification model.Based on the operating data of a 600 MW coal-fired thermal power plant,the proposed model was compared with the traditional BP neural network model and the unoptimized LSTM network model.The results show that the root mean square errors(RMSE)of the proposed network model in this paper are 66.51％and 34.83％lower than that of the traditional BP neural network model and the unoptimized LSTM network model,which has higher model accuracy.
primary frequency modulationlong short-term memory(LSTM)neural networkBayesian optimization algorithmcorrelation analysis
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