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- W2348181234 abstract "Excitation Control System (ECS) of the generator is a key control unit that can adjust the reactive power and bus voltage for power network. If the faults in ECS could not be detected,the security,quality and economy of the power system will be affected. Therefore,fast and precise fault diagnosis for ECS is very important. This thesis makes a conclusion that artificial neural networks (ANN) possess special advantages for the excitation control system fault diagnosis (ECSFD) and has set up a new four-layer (including the input layer) model for ECSFD. The key of ECSFD is learning of ANN,and the learning of ANN is depended on the correct input/output (I/O) patterns. For this reason,this paper collects a number of real fault patterns and plentiful expert knowledge patterns. Through analyzing the model effect causing from different ANN parameters,this paper discovered one group of parameters that can diagnose the fault from the Generator Excitation Control System exactly and speedily. Since the research can be applied to practice directly,this paper will show the important and useful value in the application." @default.
- W2348181234 created "2016-06-24" @default.
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- W2348181234 date "2008-01-01" @default.
- W2348181234 modified "2023-09-23" @default.
- W2348181234 title "Generator Excitation Control System Fault Diagnosis with ANN" @default.
- W2348181234 hasPublicationYear "2008" @default.
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