Abstract
Predictable and reliable power supply is crucial for safe and efficient operation in the energy sector. Hence, digital twins with real-time monitoring, simulation, and optimization capabilities receive increasing attention. In particular, early detection of faults and other operational issues based on real-time processing of sensor data can greatly reduce downtime and maintenance costs. In this work, we compare the performance of conventional and state-of-the-art unsupervised anomaly detection methods to detect tube leaks in the steam generator that recovers heat from a combined-cycle steam and gas turbine (CCGT). Since real CCGT operational data with known faults is not available, the comparison is performed using data from a high-fidelity dynamic model based on a CCGT installed on an offshore oil and gas platform. Specifically, we evaluate Local Outlier Factor (LOF), One-Class SVM (OCSVM), Principal Component Analysis (PCA), Low-Rank and Sparse (LRS) decomposition, and a Transformer Autoencoder (TAE) using ROC–AUC, G-Mean, and TPR at fixed FPR. For small-leak detection using the full sensor set, ROC–AUCs are PCA =0.77, LOF =0.87, OCSVM =0.95, LRS =0.98, and TAE =0.99, while all methods approach near-perfect AUC under large leaks. Under reduced data availability (e.g., no real-time makeup-water flow data), LRS remains the only method that sustains high detection performance. In general, LRS delivers consistently strong accuracy and robustness across leak sizes and noise levels, offering a practical accuracy–complexity trade-off for deployment.