The field of power systems research is moving towards the development of more sophisticated anomaly detection and fault classification methods. This is driven by the need for increased resilience and reliability in power grids, particularly with the integration of distributed energy resources. Recent work has focused on leveraging machine learning techniques, such as transformer-based architectures and Gaussian process approaches, to improve the accuracy and robustness of anomaly detection and fault classification. These methods have shown promising results in detecting subtle anomalies and faults, and in providing real-time monitoring capabilities. Notably, some papers have demonstrated the effectiveness of these approaches in detecting lithium plating in lithium-ion cells and in classifying and localizing faults in power systems. Noteworthy papers include: The Unsupervised Detection of Spatiotemporal Anomalies in PMU Data Using Transformer-Based BiGAN, which introduces a novel framework for anomaly detection in power grids. The Machine Learning Detection of Lithium Plating in Lithium-ion Cells: A Gaussian Process Approach, which proposes a Gaussian Process framework for detecting lithium plating. The Benchmarking Machine Learning Models for Fault Classification and Localization in Power System Protection, which presents a comparative benchmarking study of classical ML models for fault classification and localization.