IJAIEM

T. Sai Vaishnavi, CR. Ruchitha , R. Sai Chandra Shekar, K. Radha

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. Ea