Wenhua Guo
Power system, chain failure, multiple scenarios, power grid planning, fault identification
The study proposes a method for chained fault identification in power systems across various scenarios. It combines fault data and state search for fault identification, utilises a multi-scenario multi-objective optimisation method, and applies the fast non- dominated sorting genetic algorithm (NSGA-II) with elite strategy for optimal solution finding. This method enables a comprehensive analysis of chained fault identification and power grid planning in composite power systems. The simulation results demonstrated that 15,590 fault chains were obtained, updating the state fault network 29 times in a total time of 101.68 s. On average, each update took 0.197 s, while constructing the state fault network took 4,618.10 s. In comparison, the Monte Carlo sampling simulation completed 50,481 samples in 7,694.79 s, significantly less than the Monte Carlo simulation. The proposed method displays high computational efficiency and accuracy in identifying and analysing power system faults across multiple scenarios, which is crucial for security management.
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