A Fault Diagnosis Method for Power Systems Based on Temporal Tissue-Like P Systems

To quickly and accurately identify faulty components based on the alarm information is critical for the fault diagnosis of power grids. To address this challenge, this paper proposes a novel fault diagnosis method based on temporal tissue-like P system (TTPS). In the proposed method, suspected faulty components are identified first via a network topology analysis method. An TTPS-based fault diagnosis model is then built for each suspected faulty component to perform fault reasoning, so as to accurately detect the faulty components. To take full advantage of the action signals and temporal information of protection devices, TTPS and its forward temporal reasoning algorithm are proposed. TTPS can synchronously model the action and temporal logics of protection devices in an intuitive and graphical way, while the reasoning algorithm can process the fault alarm information in parallel. To demonstrate the effectiveness and superiority of the proposed method, simulations are carried out on the IEEE 14-bus and 118-bus systems, while the results are compared to other two widely adopted methods.