The fast enlargement of renewable vitality programs calls for dependable fault detection and prediction to make sure operational effectivity and grid stability. This examine presents a novel framework that integrates Prolonged Kalman Filter (EKF) state estimation with uncertainty-aware graph studying for photovoltaic (PV) array fault detection and localization. Uncooked sensor information are processed by the EKF to generate refined state estimates and uncertainty covariances for every PV module. These uncertainty measures dynamically modulate an attention-based graph building module, enabling adaptive edge weighting that down-weights unreliable connections throughout noisy or transient circumstances. The ensuing dynamic graphs are analyzed by a temporal graph consideration community to supply each node-level fault localization and international anomaly scores. The graph-construction, temporal-encoding, and prediction parts have been optimized collectively, whereas the EKF course of and commentary fashions and their noise covariances remained mounted after calibration. On the real-world dataset, it attains an AUC-ROC of 0.941 and F1-score of 0.918 for international detection, and a node-level F1-score of 0.865 with Actual Match Ratio of 0.738 for fault localization. The method demonstrates robust robustness to sensor noise and transient faults by leveraging bodily uncertainty to information graph topology. This work provides a promising course for dependable monitoring of large-scale PV programs and different sensor-rich vitality infrastructures.
Advanced data driven models based on machine learning for detection of faults and failures in solar based renewable energy systems
