Methods of Signal to Image Transformation in Photovoltaic Fault Diagnosis in Preparation for Machine Learning Applications

Abstract

This study explores various techniques for transforming 1-dimensional time-series data into 2-dimensional images, preparing for the application of machine learning models designed for 2D data. Eight distinct methods are introduced, including recurrence plots, Markov transition, Gramian angular field, spectrogram, heatmap, direct plot, phase space transformation, and Poincaré plots. These methods are tested using data from a modeled photovoltaic (PV) grid-connected system, specifically simulating a shorted string fault and a no-fault condition. The fault and no-fault responses are captured with a fixed window size of 256 sample points, consistently applied across all methods. All transformation method is tested through python 3 programming using a laptop with minimal computing capability. The generated image of each transformation may contain 1-channel image in grayscale or 3-channel RGB image. Dimension of the generated image can be increase or decrease during saving process. Each method produces a unique visual representation of the shorted string fault and a no-fault, demonstrating diverse perspectives in transforming 1D time-series data into 2D images for subsequent machine learning applications.