The Fault Diagnosis Of Power Unit Of The Excited System Based On Rough Set Theory And Artificial Neural Network

The excited system is an important constitute unit for synchronous generator, which would have a direct effect on safety and reliability of the generator, once the excited system goes wrong. Because work current of the power unit of generator excited system is very big, the silicon controlled units of the rectifying circuit are easy to damage by over-voltage or over-current. Therefore, it is very important to diagnose the location and the characteristic rapidly and accurately for shortening stop time and repair time.In this thesis, the development of the generator excited system and the actuality of fault maintenance are summarized. Based on the up to date method of fault diagnosis currently, a fault diagnosis method based on rough-set neural network (RSNN) are bring forward. Firstly, the power unit of generator excited system is modeled and simulated with MATLAB, and educe the export wave shape when the silicon controlled unit is disconnect and direct short; Secondly, the fault of the power unit of generator excited system is diagnosed with BP artificial neural network, and the rough set and it's experiences that have achieved are introduced detailedly; Finally, RSNN method is adopted to diagnose the fault of the power unit of generator excited system. For the method of RSNN integration by step, first, based on rough set principle,a decision table is established, second, reduce the decision table, then the diagnosis rule of fault type is obtained and the fault type diagnosis of the power unit can be carried, last, according to the characteristic of nonlinear mapping of neural network and the trained neural network, the diagnosis of where the faults are can be carried. For the method of RSNN integration, data of the faulty voltage waveform is collected as a simple for training neural network, then, reduce the simple data, delete the redundancy faulty simple, and train the neural network whit the reduced simple data, finally, apply the trained neural network to fault diagnosis of power unit of the excited system.By simulink test verification, the two methods base on RSNN integration by step and RSNN integration all can diagnose where the faults of power units are, especially, for the RSNN integration, the training size of neural network with rough set become smaller for the sample data compared with single neural network. As a result, not only the accuracy rate of fault diagnosis is ensured, but also the speed of fault diagnosis is enhanced greatly.