(EN) The invention relates to a power distribution network partial discharge ultrasonic detection method and system based on deep learning. The method comprises the steps of training a neural network model; converting the ultrasonic signal of the partial discharge defect of the to-be-detected power distribution network equipment into Mel Frequency Cepstral data; inputting the Mel Frequency Cepstral data into a periodic neural network layer for learning to obtain a first feature; inputting the image of the partial discharge defect of the to-be-tested power distribution network equipment into the convolutional neural network layer for learning to obtain a second feature; linearly splicing the first feature and the second feature to obtain a third feature; and inputting the third feature into themulti-layer full connection layer to obtain a detection result of the to-be-detected power distribution network equipment. Compared with the existing manual detection, the detection method and systemprovided by the invention are more efficient and accurate.
(ZH) 本发明涉及一种基于深度学习的配电网局部放电超声波检测方法及系统，方法包括：训练神经网络模型；将待测配电网设备的局部放电缺陷的超声波信号转换成梅氏倒频谱数据；将梅氏倒频谱数据输入周期神经网络层进行学习得到第一特征；将待测配电网设备的局部放电缺陷的图像输入卷积神经网络层进行学习得到第二特征；将第一特征和第二特征进行线性拼接后得到第三特征；将第三特征输入多层全连接层，得到待测配电网设备的检测结果。本发明提出的检测方法及系统相对于现有的人工检测更高效、更准确。
