Compound-Fault Diagnosis of Integrated Energy Systems Based on Graph Embedded Recurrent Neural Networks

The feature of multienergy flows of the integrated energy system (IES) causes the relationship among different types of subsystems to be complex. In order to handle the compound-fault diagnosis problem of the IES with small sample sizes of compound faults, in this article, a novel multiscale spatial-temporal graph neural network (MSSTGN) is proposed for fault detection and compound-fault identification. The label-specific fault features are learned by multiscale graph operators and gated recurrent units in the spatial and temporal domains, respectively. The deficiency of limited compound fault samples is mitigated by fusing partial inferences by base MSSTGN classifiers trained for paired faults. Constant data features of each fault class are enhanced by the proposed loss functions with a center loss. The advantages of the proposed method are illustrated by comparative experiments exploiting the process data from an IES under multiple situations of missing data and noise influence.