To address the problem of early fault state identification of the nuclear rod control system and rod position system power equipment (PWE), this article proposes an identification method based on the fusion of fault features in time domain and time-frequency domain and extreme learning machine (ELM) of grey wolf optimizer (GWO). Firstly, according to the working principle of PWE and the driving current of control rod drive mechanism, the early waveform shape and early fault mode are analyzed by using the current rise time. Then, the fault time-frequency features are constructed, which are fused with current rise time, root mean square difference summation and wavelet packet singular entropy. The discriminability of the features is analyzed. Then, the GWO algorithm can optimize parameters of the ELM classifier. The GWO-ELM model is formulated to realize the identification of early fault states of PWE, which can improve the identification accuracy. Finally, through the comparison test of different feature combinations and identification models, the results show that the proposed method can effectively realize the early fault identification and diagnosis of rod control system power supply, and the average identification accuracy can reach 98. 86% .