Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification

TL;DR

This study explores using image embedding techniques and deep learning models like CNN and RNN to classify power grid disturbances from synchrophasor data, demonstrating their superior performance over traditional data mining methods.

Contribution

It introduces the application of Gramian Angular Field image embedding with deep learning models for power disturbance classification, showing improved accuracy over conventional methods.

Findings

Deep learning models outperform traditional data mining tools.

CNN and RNN achieve higher classification accuracy.

Gramian Angular Field effectively transforms time series into images.

Abstract

This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.