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- W4311979511 abstract "Aiming at the actual situation that the current detection methods are difficult to achieve rapid diagnosis of different IGBT fault states and real-time synchronous detection, an IGBT fault detection method based on genetic algorithm optimized neural network is proposed. Methods Using wavelet packet analysis to collect fault samples, construct feature vectors, use feature vectors as the input of the neural network, and use the fault results as the output of the neural network, construct a BP neural network model, and further use genetic algorithms to optimize the fault diagnosis model. The relationship between the fault characteristics and the fault results is established through the neural network to realize the fault location, solve the problem that the IGBT is difficult to accurately and quickly diagnose when the fault occurs, effectively improve the safety of the IGBT, and reduce the maintenance cost of the IGBT. Time guarantees the stability of production operations. The feasibility of the proposed method is verified by modelling and simulation of the inverter, which has practical reference value." @default.
- W4311979511 created "2023-01-03" @default.
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- W4311979511 date "2022-12-06" @default.
- W4311979511 modified "2023-10-14" @default.
- W4311979511 title "Research on IGBT Fault Diagnosis Method Based on Neural Network Optimization Algorithm" @default.
- W4311979511 doi "https://doi.org/10.3233/atde221090" @default.
- W4311979511 hasPublicationYear "2022" @default.
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