Fault Diagnosis of Energy Networks Based on Improved Spatial–Temporal Graph Neural Network With Massive Missing Data

In order to ensure the safe and reliable operation of the energy system, real-time fault diagnosis technology is indispensable. Energy systems are typically complex systems consisting of multiple subsystems that are coupled with each other. Before and after the occurrence of a fault, the system is generally in an abnormal or even harsh environment, which may cause a large number of randomly missing measurement data and make the application of fault diagnosis technology extremely difficult. In this paper, the graph attention network (GAT) is improved by a Gaussian mixture model (GMM) for incomplete-data representation. The iteratively updated expectation of the GMM serves as the characterization of missing data, which significantly improves the ability to fill in missing data. The GAT fuses multi-source data according to the topology structure so as to comprehensively exploit the spatial information. The gated recurrent units (GRU) extract dynamic fault information from embedded spatial features and classify the time series into various fault types. Moreover, we propose a loss function in the form of weighted focal loss so that the fault-class imbalance issue brought by the data deficiency can be solved. The proposed uniform spatial-temporal graph neural network classification framework together with the GMM (GM-STGNN) can effectively improve fault diagnosis performance and is applied on an experimental platform of an authentic industrial estate. Results of comparative experiments under different conditions of both sufficient and deficient data illustrate the efficiency and advancement of the proposed method. Note to Practitioners—This paper presents a fault diagnosis method for large-scale energy systems with massive missing data. The proposed GM-STGNN framework can be applied in complex energy networks consisting of coupling subsystems, such as power grids, heating networks, and gas networks. With an incomplete-data representation mechanism, the proposed method utilizes topology information to comprehensively exploit spatial features, it also recurrently transmits historical embedded features and extracts dynamic fault characteristics. Therefore, it can effectively improve energy-network fault identification accuracy when more than half of the sample exists vacant values randomly. In the training procedure, after pre-setting the model scale, data acquired by multi-source sensors is put into the model according to the real topology structure, and corresponding fault labels serve as the supervision. The statistical characteristics of missing data are learned with neural-network parameters until the loss converges. In practical application, the sampling data is divided by a time window of a few seconds. The missing data is mitigated by the estimated expectation of the GMM. Therefore, real-time fault classification results can be obtained with high accuracy. The effectiveness of the proposed method is illustrated by fault diagnosis of a typical distributed heating network under the noise influence. Benefiting from the ability to learn fault knowledge, the proposed method can be easily applied to new scenarios where the process data and topology structure of the system are known.