Pattern Recognition Method for Detecting Partial Discharge in Oil-Paper Insulation Equipment Using Optical F-P Sensor Array Based on KAN-CNN Algorithm
Hong Liu, Zhixian Zhang, Ruimin Song, Zhiqing Shu, Jianxin Wang, Haoyuan Tian, Yuxuan Song, Weigen Chen
Abstract
Ultrasound detection can promptly identify partial discharge (PD) faults inside power transformer by using fiber-optic Fabry-Perot (F-P) array sensors. This paper presents a new design scheme for a wideband acousto-optic direct-coupled F-P sensor. Indirect-coupled F-P sensors have a narrow, highly sensitive response region, and direct-coupled F-P sensors have a wide band response from 1 to 200 kHz. We constructed a PD database by simulating different PD signals in oil tank. We proposed learnable KAN kernel to replace traditional CNN kernel and introduced KAN as a replacement for the ReLU function. Furthermore, we conducted pattern recognition research with a convolutional depth of only 6 layers. The test accuracy on oil tank dataset reached 98.7% for KAN Convolution + ReLU and 99.5% for KAN Convolution + KAN. The validation accuracy achieved by inputting PD signals measured from a real 220 kV power transformer into the two models was 97.5% and 98.75%, respectively.