Fault Diagnosis of Energy Networks: A Graph Embedding Learning Approach

For industrial parks containing energy systems, fault diagnosis technology is of great significance for their safe operation. In recent years, the topology of energy systems has become more complex due to the use of technologies such as cogeneration, leading to multienergy coupling. Critical equipment and user nodes in these complex energy networks are vulnerable to a lack of sensor data or non-idealities in the measurement environment. There is an urgent need for a unified and robust fault-diagnosis framework for the overall system to identify faults even under non-ideal data conditions. In this article, to address the problem of fault identification and state prediction, a novel deep learning model is constructed based on graph-embedded recurrent neural networks (RNNs) with self-attentional layers. Unstructured data are put into the graph neural network to extract common spatial features. An additive attention mechanism is implemented in the graph attention network (GAT) to integrate multiscale node information. The graph operator is computed within a gated recurrent unit (GRU) that captures the full range of temporal features. In addition, loss functions are introduced for fault identification and state prediction. Data from an industrial park experiment platform are used for fault identification experiments. The advantages of the proposed approach are illustrated by comparative experiments with different levels of missing data.