Extended Gramian Angular Field: An Efficient Method for Encoding Time Series as Images and Its Application in Power Converter Open Circuit Fault Diagnosis

The data-driven-based fault diagnostic methods have continued to emerge in power electronics systems in recent years. Among them, though the methods that convert one-dimensional (1-D) electrical signals into two-dimensional (2-D) images for classification have shown promise, they often suffer from information loss or excessive computational burden. This article refines the Gramian angular field (GAF) theory and proposes a lossless and efficient method for transforming time series into images, termed extended Gramian angular field (EGAF). Building upon EGAF, an efficient and robust fault diagnostic method is proposed, with implementation demonstrated on a two-level (2L) three-phase (3P) voltage-source-inverter (VSI). The diagnostic method first extracts the current time series under different faults through Clarke transformation and zero-crossing detection, then converts the windowed time series into 2-D matrices using EGAF to construct the diagnostic dataset, and finally implements fault classification through a lightweight convolutional neural network (CNN). The proposed diagnostic method achieved an accuracy of 98.64%, and the experimental results demonstrate its high performance.