When the pulse current method is used for partial discharge (PD) monitoring of mining cables, the detected PD signals are seriously disturbed by the field noise, which are easily submerged in the noise and cannot be extracted. In order to realize the effective separation of the PD signal and the interference signal of the mining cable and improve the signal-to-noise ratio of the PD signal, a denoising method for the PD signal of the mining cable based on genetic algorithm optimization of variational mode decomposition (VMD) and wavelet threshold is proposed in this paper. Firstly, the genetic algorithm is used to optimize the VMD, and the optimal value of the number of modal components K and the quadratic penalty factor α is determined; secondly, the PD signal is decomposed by the VMD algorithm to obtain K intrinsic mode functions (IMF). Then, wavelet threshold denoising is applied to each IMF, and the denoised IMFs are reconstructed. Finally, the feasibility of the denoising method proposed in this paper is verified by simulation and experiment.

The dielectric loss angle can better reflect the overall insulation level of mining cables, so it is necessary to implement reliable and effective online monitoring of the dielectric loss angle of mining cables. In order to improve the monitoring accuracy of the dielectric loss angle tan δ of mining cables, a low-frequency dielectric loss angle online monitoring method combining signal injection method and double-end synchronous measurement method is proposed in this paper. Firstly, the superiority of the low-frequency signal in improving the detection accuracy of dielectric loss angle is explained, and the feasibility of the low-frequency signal injection method is analyzed. Secondly, the cable leakage is calculated using the double-terminal synchronous measurement method to measure the core current at the first and last ends of the cable, and the phase sum of the voltage at the first and last ends is selected as the reference phase quantity to realize the effective calculation of the dielectric loss angle tan δ of the cable. Then, the simulation model for online monitoring of dielectric loss angle of mining cable is built, and the feasibility of the online monitoring method proposed in this paper is verified by combining the simulation results. Finally, the theoretical and simulation analysis of the monitoring error of dielectric loss angle of mining cable is carried out.