Power System Fault Reasoning and Diagnosis Based on the Improved Temporal Constraint Network

Temporal information is fundamental in model-based fault diagnosis, and the alarm-processing problem is to interpret the alarm sequences to infer the type and time of fault event occurrences. There can be cycles or feedback loops in real power systems, but the fault reasoning methods for such cases are seldom considered in the literature. This paper provides an analytic model based on the improved temporal constraint network. The reasoning method is dependent on the time point and time distance information, with which the fault motivators (or actuators) and fault responders (or victims) can be identified. The system fault event reasoning and diagnosis are formulated as an optimization problem with the fault hypotheses being tested. The calculation process consists of three steps: 1) establishment of the objective function, 2) determination of the fault propagation paths, and 3) determination of the expected states under a given hypothesis. Finally, the proposed method is demonstrated via a power system example, which is verified to be successful in performance assessment and fault diagnosis.