The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

The rapid growth of sensor-generated data in Smart Grids (SGs) has been accompanied by a parallel surge in cyberattacks. This letter aims to uncover how machine learning (ML) models deployed in SGs are vulnerable to stealthy attack vectors, leading them to intentionally forget learned information-an emerging threat known as malicious machine unlearning (MU). We explore the increasing risks to ML-driven decision-making in SGs, given the evolving cyber-threat landscape. Our experiments, using publicly available datasets on various SG failures and simulated cascading outages, show that even subtle changes-altering just 10% of data in a single class can degrade model accuracy by 5-10%. These findings underscore the danger of MU, forcing models to forget and produce inaccurate, harmful predictions.