Directly using the operation data of high-voltage direct current （HVDC） transmission lines in their high-frequency steady-state control can lead to deviations in the control results. To address this problem， a high-frequency steady-state control method for HVDC transmission lines based on a normal cloud neural network is explored. A normal cloud neural network model is constructed. The structure and wiring mode of HVDC transmission lines are analyzed， and the frequency difference signals between the inverter side and the rectifier side of the lines and their rates of change are selected as the control signals according to the analysis results. The control signals are used as inputs， and the input signals are processed by the normal cloud part of the model to reduce noise and uncertainty. The processed signals are then input into the generalized dynamic fuzzy neural network part of the model to obtain the steady-state control signal output and realize the high-frequency steady-state control of the high-voltage direct current transmission line. Experimental results show that the method can achieve steady-state control of high-voltage DC transmission lines at high frequency. When normal operation and power disturbances are added， it can quickly and effectively suppress the oscillation of the transmission line， maintain the generator power and DC transmission power to quickly restore stability， and the noise effect generated by real-time monitoring of the obtained parameter data is very small. This method ensures the stability and reliability of the DC transmission system.