Research On Technique Of On-line Monitoring And Fault Diagnosis For Large Turbo-generators

The elements of surveying temperature are fixed on the end of the stator bars and between the stator bars in large generators. how reasonably uses these temperature elements to monitor and diagnose the thermo fault is the topic which the present stage needs to study. In view of this, a thermo hydraulic model of the water-cooled stator bars in large generator is presented, through calculating and deducing, the formula reflecting the dynamic model is given, then, radial-basis function neural network(RBFNN) is used to identify the model parameters and calculate the transient time of the temperature which occurred to water-cooled stator bars from load-1 to load-2.and make the transient time as a tool to judge the generator working in normal condition or in thermal fault condition. an example which bases on a 600MW synchronous generator is used to prove this technique correctly. And the distinct advantage of this technique is that the judgment result is not affected by delay time which caused by temperature signal.Multi-resolution analysis and Wavelet Energy spectrum are used to analysis the signals of difference voltage and circular current when the rotor windings are faulty .and using wavelet packet to reduce noise of signals. Mallat's decomposition algorithm is applied to decompose the signals, in the end, the wavelet energy of the 6th detail is adopted to extract feature of the rotor wingding fault .