Ferromagnetic resonance over-voltage identification method based on Gram angle field

Abstract

In neutral point non-grounding systems with 35 kV and below, ferromagnetic resonance over-voltage faults often occurs, which brings safety hazards to power systems. Ferromagnetic resonance occurs for a short time and is similar to other faults in the power system and is not easy to be detected by direct observation. Deep learning has obvious advantages in image recognition and classification, but it is difficult to deal with over-voltage one-dimensional time series monitored in power system, and a deep learning recognition model based on Gram angle field and improving Convolutional Neural Network (CNN) structure is proposed, the voltage time series is converted into two-dimensional image, and the voltage classification task is performed by extracting image features and entering them into two channels of CNN-SPP (Spatial Pyramid Pooling). The two-channel structure solves the problem of high-dimensional time series learning, SPP solves the problem that CNN requires the input over-voltage conversion image must be the same size, and optimizes the network structure and parameters to obtain the optimal identification model. Finally, the experimental comparison shows that the ferromagnetic resonant over-voltage voltage recognition based on Gram angle field and improving CNN has higher recognition accuracy than other time domain and frequency domain characteristic algorithms. The high frequency, division frequency and fundamental frequency ferroresonance identification accurate rate based on the proposed method is 94.2%, 93.8% and 90.5% respectively.