Power quality disturbances (PQD) have a negative impact on power quality-sensitive equipment, often resulting in great financial losses. To prevent these losses, besides detecting a PQD on time, it is important to classify it, so that appropriate recovery procedures are employed. The majority of research employs machine learning model PQD classifiers on manually extracted features from simulated or real-world signals. This paper presents an end-to-end approach that circumvents the manual feature extraction and uses signals generated from mathematical voltage sag type formulas. We developed a configurable voltage sag generator that was used to form training and validation datasets. Based on the synthetic three-phase voltage signals, we trained several end-to-end LSTM classifiers that classify voltage sags according to ABC classification. The best-performing model achieved an accuracy of over 90% in the real-world dataset.

Thermal power plants are essential for maintaining a stable electricity supply but face high operational costs and potential economic losses due to unexpected equipment failures. Predictive maintenance (PM) aims to reduce these risks by detecting early anomalies, but existing anomaly detection methods lack statistical control over the false positive rate (FPR), leading to frequent false alarms and inefficient maintenance interventions. We hypothesize that a conformal anomaly detection (CAD)-based approach, incorporating calibration-conditional p-value adjustments, can effectively mitigate the issue of uncontrolled FPR by ensuring statistically valid anomaly detection for power plant PM, even when multiple dependent hypothesis tests are performed on operational data. We introduce a CAD framework for power plant PM, which integrates calibration-conditional validity adjustments to systematically control FPR while maintaining sensitivity to operational faults. The evaluation results of the proposed CAD framework for PM in thermal power plants show that it ensures statistically valid FPR control, significantly reducing false alarms while maintaining high sensitivity to faults and strong discriminative ability between normal and faulty operations. By providing automatic and statistically valid FPR control without manual threshold tuning, the proposed framework offers a robust and scalable solution for anomaly detection in thermal power plant PM, improving maintenance efficiency and reducing unnecessary interventions.

Recent methods for automatic blood vessel segmentation from fundus images have been commonly implemented as convolutional neural networks. While these networks report high values for objective metrics, the clinical viability of recovered segmentation masks remains unexplored. In this paper, we perform a pilot study to assess the clinical viability of automatically generated segmentation masks in the diagnosis of diseases affecting retinal vascularization. Five ophthalmologists with clinical experience were asked to participate in the study. The results demonstrate low classification accuracy, inferring that generated segmentation masks cannot be used as a standalone resource in general clinical practice. The results also hint at possible clinical infeasibility in experimental design. In the follow-up experiment, we evaluate the clinical quality of masks by having ophthalmologists rank generation methods. The ranking is established with high intra-observer consistency, indicating better subjective performance for a subset of tested networks. The study also demonstrates that objective metrics are not correlated with subjective metrics in retinal segmentation tasks for the methods involved, suggesting that objective metrics commonly used in scientific papers to measure the method’s performance are not plausible criteria for choosing clinically robust solutions.