Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load Dataset

Power networks are vital to society, yet service outages and faults can have devastating consequences. This study introduces a novel integration of machine learning and data augmentation techniques for fault detection and classification, addressing gaps in data diversity and imbalance. Unlike traditional approaches, the research utilizes an Auxiliary Classifier Generative Adversarial Network (ACGAN) to generate synthetic data representative of underrepresented fault types, enhancing model training and performance. By extracting both spectral and statistical features from the Grid Event Signature Library (GESL) dataset, a comprehensive representation of power system signals is achieved. A comparative evaluation of models including Decision Trees (DT), Random Forest (RF), Extra Tree Classifier (ETC), Gradient Boosting Classifier (GBC), and K-Nearest Neighbors (KNN) revealed the Extra Tree Classifier achieved the highest testing accuracy of 93.85%. The methodologies demonstrated scalability by using a dataset augmented to 9,000 samples and validated robustness through 10-fold cross-validation with a standard deviation of 0.00659. These results highlight the proposed framework’s potential for real-world implementation in modern power grids, offering enhanced fault prediction and resilience. This research establishes a pathway for integrating advanced data augmentation and machine learning techniques into operational power grid systems, ensuring stability and reliability.