With the promotion of China's “carbon peaking and carbon neutral” strategy

thermal power units are more involved in deep peak regulation. Under the conditions of deep peak regulation

the thermal power unit is insufficient in heat storage

and the primary frequency regulation capability decreases

resulting in a large deviation between the unit's primary frequency regulation capability calibrated under the rated operating condition and the actual frequency regulation capability

threatening the frequency security of the power grid. Aiming at this problem

an online estimation method of primary frequency regulation capability of deep peak regulation thermal power units based on LSTM neural network is proposed. The static model of steady-state unit design was improved to a dynamic model

considering the dynamic operation process of the unit by using the time sequence memory ability and nonlinear feature extraction ability of LSTM neural network

and the errors caused by the disturbance factors such as the load changing process and the historical action of primary frequency regulation were corrected. Based on the hierarchical modeling method

the sub-models with different neural network structures were designed for the different characteristics of the factors affecting the frequency regulation capacity

such as heat storage of the unit and steam turbine work performance

and the effects of furnace side were taken into account to improve the accuracy of frequency regulation estimation results. Compared with the traditional method used in the power system

the estimation result of this method has higher accuracy

and has better performance under different working conditions such as steady state and variable load.