移动端卷积神经网络开关柜局部放电样本生成与检测

Partial discharge sample generation and detection of convolutional neural network switchgear at mobile end

局部放电是造成高压电气设备最终发生绝缘击穿的重要原因,也是绝缘劣化的重要标征,针对目前开关柜局部放电常规检测手段存在检测信息量少,时效性性差,诊断准确率低等问题,本文提出了一种可集成在移动端设备的卷积神经网络检测方法,并针对实际情况中存在放电类别样本不均匀的问题,提出了一种故障样本生成方法。将采集到的超声波信号经过去噪和预处理后通过短时傅里叶变换转化为二维时频谱图,输入卷积神经网络中进行局部放电模式识别,针对实际场景中出现故障样本不均匀问题,使用AE-DCGAN生成对抗网络生成故障样本。实例实验表明,本文所提出方法在移动端t710算力条件下,其准确率达到97%以上,算力达到0.27 s,生成数据样本MSE误差均低于0.067。

Partial discharge is an important cause of insulation breakdown of high-voltage electrical equipment, but also an important indicator of insulation deterioration, in view of the current switchgear partial discharge conventional detection methods have a small amount of detection information, poor timeliness, low diagnostic accuracy and other issues, this paper proposes a convolutional neural network detection method that can be integrated in mobile devices, and for the actual situation there is a problem of uneven discharge class samples, a fault sample generation method is proposed. The collected ultrasonic signal is de-denoised and pre-processed into a two-dimensional temporal spectrogram by short-term Fourier transform, and the partial discharge pattern is identified in the input convolutional neural network, and the adversarial network is used to generate the fault sample for the problem of uneven fault sample in the actual scene. The example experiments show that the accuracy rate of the proposed method in this paper reaches more than 97%, the computing power reaches 0.27 seconds under the condition of t710 computing power of the mobile terminal, and the error of the MSE generated data sample is lower than 0.067.