基于同步监测和深度学习的电容器介损角辨识

电容器在线监测系统中,不同位置监测装置受导线电流的干扰不同,因此工程中使用谐波分析法计算介损角存在不稳定问题。该文提出了一种基于同步监测和深度学习的电容器介损角辨识方法。首先给出了电容器电流、电压信号无线同步监测方法,以及用于深度学习的介损角表示信号Dδ(t)的计算过程。然后仿真验证方法的有效性并与基于加汉宁窗的谐波分析法进行比较。最后对深度神经网络隐含层进行了可视化分析,结果显示,该方法的正确率主要受噪声、谐波幅值比、介损角变化量等影响,且在谐波幅值比小于10%的情况下,辨识结果受频率偏移、谐波与基波相角差的影响较小。

Dielectric Loss Angle Identification of Capacitor Based on Synchronous Monitoring and Deep Learning

In the capacitor online monitoring system,the disturbance of lines on different position monitoring device is different.Therefor, the use of harmonic analysis in the calculation of dielectric loss angle has instability problem in engineering.This paper proposes a capacitor dielectric loss angle identification algorithm based on the synchronous monitoring and deep learning.Firstly, we present the wireless synchronous monitoring method of capacitor current and voltage signal, and the computation process of dielectric loss angle identification signal Dδ(t) for deep learning.Then, we verify the effectiveness of the proposed method through simulation, and compare the results with the Hanning windowed harmonic analysis method.Finally, we analyze the visualization of deep neural networks hidden layer.The results show that the algorithm accuracy is affected by white-noise level, harmonic amplitude ratio and the variation level of dielectric loss angle.In situations when harmonic amplitude ratio less then 10%, the algorithm accuracy has been fewer affected by frequency deviation, phase difference of harmonics.