| Abstract: |
| 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. |
| Key words: Alarm signal, fault diagnosis, membrane
computing, power system, tissue-like P system. |
| DOI：10.23919/PCMP.2023.000106 |
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| Fund:This work is partially supported by the National Natural Science Foundation of China (No. 61703345), the Chunhui Project Foundation of the Education Department of China (No. Z201980), and the Open Research Subject of Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education (No. szjj2019-27). |
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