Abstract：Most of the algorithm of calculating dielectric loss factor are positive solving process, which include sampling capacitor current and voltage, and calculating the dielectric loss factor of these signals by harmonic analysis. These methods have poor robustness when there are unidentified distortions in the sampling signal. This paper proposes a capacitor dielectric loss factor identification algorithm based on the deep learning. The algorithm proposed in this paper trains a feed-forward multilayer artificial neural network with a period of online sampling signals, and identify the dielectric loss angle from new monitoring data with resolution of 0.001%. The computation of dielectric loss factor identification signal Dδ(t) is proposed, and verify the amplitude of Dδ(t) is the dielectric loss angle. And the shape of its waveform includes the interference of the monitoring device. The validity of the method has been proved by simulation. The method can achieve better ability to resist noise than hanning windows harmonic analysis method. The calculation results based on actual online monitoring data shows better robustness than hanning windows harmonic analysis method, its results is also not affected by the angle error of potential transformer.
王晓辉,朱永利,王艳,郭丰娟. 基于深度学习的电容器介损角在线辨识[J]. 电工技术学报, 2017, 32(15): 145-152.
Wang Xiaohui, Zhu Yongli, Wang Yan, Guo Fengjuan. Online Identification Method of Power Capacitor Dielectric Loss Angle Based on Deep Learning. Transactions of China Electrotechnical Society, 2017, 32(15): 145-152.
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2017, Vol. 32 
