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Ultrasonic method is a non-invasive partial discharge (PD) detection method for power equipment, which plays a more and more important role in partial discharge location. The propagation characteristics of ultrasonic in converter transformer can be effectively analyzed by using numerical method. In this paper, according to the actual size of a 110kV converter transformer, the 3-D finite element simulation model of converter transformer is established to simulate the propagation of ultrasonic signals in the tank. A 2-D model is then established to simulate the influence of windings on the propagation of ultrasonic signals. The simulation results show that when the partial discharge is generated, it will spread around in the form of a spherical wave, which spreads over the entire converter transformer space within 2000μs. The sound pressure amplitude is exponentially decreased with the increase of distance between PD sources and prbes, and the drastic attenuation is caused by the windings and core. When the discharge source is located between the winding and the core or between the windings, the wave arrival time from the partial discharge source to the ultrasonic sensor will be increased. The analysis can provide the basis for the detection and localization of partial discharge in converter transformer.

Deterioration of insulation materials caused by partial discharge is one of the important reasons for the failure of power equipment. The noise caused by the interference of the external electromagnetic environment seriously affects the accuracy of partial discharge detection. Aiming at the problem of partial discharge noise suppression, this paper proposed a partial discharge signal noise suppression method based on adaptive window singular value decomposition. This method first randomly generated a series of windows with different positions and lengths on the noisy partial discharge signal; Then singular value decomposition method was employed to denoise the signals in each window; Finally, the denoising index was chosen as the fitness function, through the particle swarm algorithm to obtain the optimal solution of the window position and length. The noise signals obtained by simulation and experiment were verified, and the method was compared with adaptive singular value decomposition, wavelet denoising, and other methods. The results showed that this method could achieve a better denoising effect.