The growing use of gas-fired power generators and electricity-driven gas compressors and storage has increased the interdependence between electric power infrastructure and natural gas infrastructure. However, the increasing interdependence may spread the failures from one system to the other, causing subsequent failures in an integrated power and gas system (IPGS). This paper investigates the structural vulnerability of a realistic IPGS based on complex network theory. Different from the existing works with a focus on the static vulnerability analysis for an IPGS, this paper considers both static and dynamic vulnerability analysis. The former focuses on vulnerability analysis under random and selective failures without flow redistribution, while the latter concentrates on vulnerability analysis under cascading failures caused by flow redistribution. Also, different from the existing works with a focus on the IPGS as a whole, we not only analyze the vulnerability of the IPGS but also analyze the vulnerability of the power subsystem (PS) and gas subsystem (GS), in order to understand how the vulnerability of the IPGS is affected by its PS and GS. The analysis results show that (1) if the PS and GS are more susceptible to cascading failures than selective and random failures, the IPGS as a whole is also more vulnerable to cascading failures. (2) There are different dominant factors affecting the IPGS vulnerability under cascading failures and selective failures. Under cascading failures, the GS has a more significant impact on the IPGS vulnerability; under selective failures, the PS has a more important impact on the IPGS vulnerability. (3) The IPGS is more vulnerable to failures on the critical nodes, which are identified from the IPGS as a whole rather than from the individual PS or GS. The results provide insights into the design and planning of IPGSs to improve their overall reliability.

Phasor measurement units (PMUs) are being installed at an unprecedented rate on power systems, offering unique situation awareness capability. This paper presents a graph learning-based method for detecting and locating anomalies using PMU data. In this method, the graph learning technique is used to characterize the spatiotemporal relationship of distributed PMU data by constructing a spatiotemporal graph. Then, graph analysis is used to detect and locate anomalies by evaluating the global connectivity of spatiotemporal graphs at different times and the local connectivity of nodes in the relevant spatiotemporal graphs. The proposed method was verified using the IEEE-39 bus system and realistic PMU data. The method accurately identifies anomalies with an accuracy of 97% with a precision and recall of 80% and 100%, respectively. The results show the superiority and robustness of the proposed method as a powerful tool for detecting and locating anomalies using PMU data.