Candidate Fault Scenario Generation in Substations by Integrating Multi-Source Time-Series Data

Following the occurrence of a substation fault, alarm systems frequently exhibit issues such as high-density redundancy, spurious alerts, undetected alarms, and temporal inconsistencies. In addition, the effective integration of heterogeneous multi-source data—including fault waveform records and phasor measurement unit (PMU) streams—remains a nontrivial challenge, further impeding the accuracy of fault diagnosis. To address these limitations, this paper proposes an enhanced framework for multi-source fault event modeling and candidate scenario generation based on an extended temporal constraint network. First, protection operations, circuit breaker status transitions, transient waveform features, and PMU-derived dynamic parameters are abstracted as fault event nodes. On this basis, an event representation model is constructed, incorporating event type, associated equipment, temporal attributes, data source identifiers, and reliability coefficients. Second, the conventional temporal constraint network is refined by jointly embedding temporal constraints, distance constraints, and event confidence metrics, thereby enabling the characterization of uncertain causal-temporal dependencies between fault origins and multi-source observations. Third, a dynamic time-window mechanism is introduced to perform online event clustering, followed by reverse temporal reasoning to generate a set of candidate fault scenarios. These scenarios are subsequently verified, merged, and ranked with reference to waveform and PMU data, facilitating the identification of false alarms, missing alarms, and timestamp anomalies. Finally, case studies conducted on a 220 kV substation and its interconnected transmission network demonstrate that the proposed method achieves a correct scenario coverage rate of 96.8%, a candidate scenario compression rate of 68.7%, and an abnormal alarm recognition accuracy of 89.5%, while sustaining an average scenario generation time of 0.28 s.