All-DC wind power systems can effectively address issues such as reactive power and voltage fluctuations caused by AC cable capacitance

making them a growing focus of research. To tackle challenges such as the large number of collection branches

small differences in fault characteristics between adjacent lines

and difficulties in threshold setting

this paper proposes a fault line selection method based on the convolutional power energy ratio for all-DC wind power systems. First

the characteristics of transient power amplitude in the frequency domain for faulted and non-faulted lines are analyzed

revealing that within a characteristic frequency band

the transient power of faulted lines is greater than that of the non-faulted lines. Second

convolution power

as a time-domain fault characteristic quantity

is constructed to effectively extract the frequency domain power characteristics. Additionally

to improve the ability to withstand transition resistance

a time-domain convolutional power energy ratio is proposed between each collection line and the outlet of the collection bus. Analysis shows that the time-domain convolutional power energy ratio can effectively identify the faulty line within the characteristic frequency band. Combined with fault initiation and fault pole selection criteria

a complete fault line selection scheme is developed. Finally

PSCAD/EMTDC simulation results show that the proposed method can correctly identify faulty lines in all-DC wind power systems under fault resistance of 80 Ω and noise interference of 20 dB

without the need for simulation-based threshold tuning.