Prediction of Cryptocurrency Mining Load Tripping Through Learning-Based Fault Classification

Abstract

Globally, increasing amount of cryptocurrency mining demand presents both opportunities and challenges for electric energy systems. This research employs a data-driven method to predict cryptocurrency mining load-tripping events, specifically targeting the low-voltage ride-through (LVRT) problem. The study utilizes diverse low-voltage fault scenarios generated through electromagnetic transient program (EMTP) software as training data. For fault classification, a convolutional neural network (CNN) is employed to improve model accuracy. Additionally, model explainability is enhanced using a decision tree for forecasting tripping events. The proposed approach is validated on a 6-bus power system integrated with cryptocurrency mining facilities.