A data-driven approach to microgrid fault detection and classification using Taguchi-optimized CNNs and wavelet transform

Abstract

The integration of microgrids into the bulk power system introduces inherent uncertainties that challenge conventional protection systems, encompassing factors such as low fault currents, operational modes, penetration levels of renewable sources, load variations, and network topology. These uncertainties significantly impact the overall reliability of the electrical system. In the event of a fault occurrence within or external to the microgrid, swift disconnection from the primary grid is imperative. This disconnection is facilitated through the immediate operation of a static switch positioned proximate to the common coupling point. Such rapid action is essential to mitigate potential damages and expedite the restoration of electrical services. To ensure the delivery of reliable and high-quality energy to end consumers while alleviating stress on the utility grid, this paper introduces a novel methodology for the efficient detection, classification, and localization of faults in a microgrid cluster connected to the external grid. The proposed system addresses diverse irregular conditions, including conventional faults, high-impedance faults, islanding scenarios, and adverse events, covering several zones within the microgrid cluster and the external electrical grid. The proposed approach is based on a fusion of the Taguchi methodology and the discrete Wavelet transform. This combination enables the optimization of convolutional neural network training using scalograms generated from the fault signals. The results demonstrate the model's high performance, achieving 99.25 % accuracy in fault localization and 99.13 % in fault detection and classification, all within less than 10 ms. In comparison, traditional methods like support vector machine and decision trees require over 16 ms with lower accuracy, underscoring the superior speed and precision of the proposed approach