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- W4212847233 abstract "The traditional fault diagnosis methods of SF <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>6</inf> circuit breakers lack systematicity, which are intricate and time-consuming in fault detection and maintenance. A fault diagnosis model of SF <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>6</inf> circuit breaker based on fuzzy neural network improved by genetic algorithm is established to solve the similar problems. In this paper, an online fault diagnosis method for SF <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>6</inf> circuit breaker based on fuzzy neural network improved by genetic algorithm is established. This method adopted the characteristic data of SF <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>6</inf> circuit breakers to build a fault diagnosis model which is trained by inputting parameters, including the decomposition products of SF <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>6</inf> gas in the circuit breaker and the temperature, pressure and density, to diagnose the corresponding faults. Finally, the fault diagnosis model of fuzzy neural network improved by genetic algorithm compared with other diagnosis models in terms of training time and accuracy. The simulation results show that the proposed fault diagnosis model is more efficient and accurate than other available methods. The fault diagnosis model has also been applied to the online monitoring technology of the smart substation with good operations." @default.
- W4212847233 created "2022-02-24" @default.
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- W4212847233 date "2021-10-15" @default.
- W4212847233 modified "2023-09-27" @default.
- W4212847233 title "Research on Fault Diagnosis Model of SF₆ Circuit Breaker Based on FNN Improved by GA" @default.
- W4212847233 doi "https://doi.org/10.1109/aeero52475.2021.9708230" @default.
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