Distributed Air-Gap Flux and Rotor-Current Fusion for Operating-Regime Identification in a 10-MW Kaplan Hydrogenerator

2026-06-26

AI Summary

This study addresses the challenge of accurately identifying operating conditions in large Kaplan hydrogenerators by proposing a novel multimodal approach that fuses synchronized measurements of stator distributed air-gap magnetic flux and rotor current. The method constructs spatial Fourier descriptors, time-domain flux metrics, and rotor current RMS features, which are subsequently reduced via principal component analysis. Machine learning models, particularly a support vector classifier with a radial basis function kernel (RBF-SVC), are employed to classify wicket gate opening states. Experimental results demonstrate that the fused-feature RBF-SVC achieves both test accuracy and macro F1-score of 99.5%, substantially outperforming single-modality approaches. This integration not only enhances classification performance but also improves interpretability regarding spatial flux imbalance and waveform distortion.

Abstract

Reliable monitoring of hydroelectric generators requires descriptors that capture both electrical loading and electromagnetic field behavior. This work investigates operating-regime identification in the Porjus U9 10-MW Kaplan hydrogenerator using synchronized measurements from ten stator-mounted Hall probes and six rotor-current channels. Seven steady guide-vane-opening settings are considered, and each 300s record is divided into 1s windows. The resulting windows are represented by spatial Fourier descriptors of the circumferential air-gap field, probe-wise temporal flux indicators, and channel-wise RMS rotor-current features. Correlation analysis and principal component analysis are used to examine how the feature groups vary with the operating point, and Random Forest, radial-basis-function support vector classification, and multilayer perceptron models are evaluated for supervised identification of the guide-vane-opening state. The analysis shows that RMS rotor-current features mainly track the loading axis, while the magnetic-flux features reveal complementary information associated with spatial imbalance, waveform distortion, and weak low-frequency modulation. Spatial descriptors alone provide limited separability, yielding test accuracies below 27%, whereas rotor-current features alone reach about 84-85%. Combining flux and current information gives the most discriminative representation; the SVC-RBF model achieves 99.5% test accuracy and macro-F1 score. The results indicate that distributed air-gap magnetic sensing, when fused with rotor-current measurements, can support accurate and interpretable data-driven monitoring of Kaplan hydrogenerator operating regimes.

Problem

• operating-regime identification
• Kaplan hydrogenerator
• air-gap flux
• rotor current
• condition monitoring

Innovation

• distributed air-gap flux
• rotor-current fusion
• operating-regime identification
• Kaplan hydrogenerator
• multi-sensor data fusion