| The large power generator is one of the main equipment in the electric station. The security and stability of the system directly affect the operation of the power network, and the insulation fault in a generator failure is in a high proportion with other faults. In the way of the development of regular maintenance of a turbo generator from timing off line to online maintenance, it is a great practical significance using online monitoring and fault diagnosis technology to test the large power generator winding insulation. Under the combined effects of the multiple factors, the generator windings is affected by the electricity, heat, mechanical, chemical and so on, therefore the performance of the insulation will gradually deteriorate. At the same time, a series of electrical properties will change accordingly, and the insulation accident will occur at the end. Hence, using online monitoring of the generator insulation condition for a long-time, and analyzing the changes of the insulation performance parameters is a very effective detection method for evaluating the insulation of a large power generator.On researching the structure and the aging mechanism of the insulation in the generator winding, this paper analyzed the process of thermal aging, mechanical aging and electrical aging of the generator winding during operation and discussed the feature of the single factor electrical aging, thermal aging, mechanical aging and multi-factor aging characteristics; and focused on the analysis of aging parameters on the insulation features. On this basis, the testing methods of the winding insulation were compared, analyzed and studied. This paper expressed the shortcomings and limitations of measuring bar discharge (pC) method to detect the partial discharge performance according to online testing methods at our country and abroad. Meanwhile, this paper proposed the advantages of measuring the current of the neutral point to detect partial discharge of the generator, and pointed out the specific requirements for the different testing programs.As to the research of partial discharge of the remote online monitoring system for a large generator, an online monitoring and diagnosis system for the generator insulation based on intelligent information processing was designed on the basis of the Radio Frequency Monitoring system (RFM). The system design, feature set, performance fix, system pre-alarm and fault alarm value define were completed, especially the high-frequency broad band current sensors, small signal large dynamic high-frequency broad band logarithmic amplifier and the quasi-peak detector were produced. This remote online monitoring system has the functions of large dynamic range, high-precision, remote communication monitoring and so on. The amplifier filter shape factor, the anti-interference ability and some other performance are better than the similar products.Concerning the winding insulation fault diagnosis in a generator, this paper studied the criterion based on the fuzzy state information processing of the generator winding insulation state and fault tree analysis theory; and discussed the corresponding relationship between the fault characteristics and the fuzzy state based on the analysis of the fault characteristics fuzzy; and the fuzzy inference mechanism, the fuzzy reasoning knowledge base, the fuzzy expressions and the fault tree structure of a generator winding insulation were designed. The expert diagnosis system can effectively determine the winding insulation failure based on the fuzzy theory.As to the application of the neural network fault diagnosis theory, this paper analyzed the principle which detecting the rotor fault by use of collecting neutral point signals of generator stator windings. In the discussion of RBF neural network fault diagnosis theory and iterative relationship between the weight coefficients, the rotor fault identification technologies and the solutions were proposed based on RBF neural network, and the solutions were confirmed validity by an example.Concerning the research of the mobile intelligent diagnostics, this paper discussed the construction of the generator intelligent diagnostic expert system based on the mobile agent; and proposed the protection scheme of the Mobile Agent System and the design process of the safety measures based on the analysis of the security and stability of mobile agent. The generator insulation fault diagnosis method was discussed based on the analysis of fault diagnosis theory of Bayes network. A very useful exploration has been done in the fault diagnosis with an information fusion technology.In conclusion, the main innovation points of the paper are as follows: 1. The large generator remote monitoring system applied to the winding insulation condition has a large dynamic range, precision, and remote communication and monitoring functions. The key components of the system such as high-frequency broad band current sensors, small signal high-frequency broad band logarithmic amplifier and the quasi-peak detector and other devices have been designed and completed. It has been applied to the large generators.2. A criteria for identifying state of generator winding insulation based on fuzzy diagnosis technology was proposed, combined with self-made insulation fault diagnosis system. Online monitoring and fault diagnosis for the insulation state of the generator with different capacities and structure was realized.3. The solution of the generator insulation monitoring and fault diagnosis system based on RBF neural network was proposed, the principle which detecting the rotor fault by use of collecting neutral point signals of the generator stator windings was analyzed, and the validity was approved by an ensample. It provided a theoretical basis for expert systems with remaining life prediction intelligent computer system based on state analysis.This thesis has a greater theoretical and practical significance in online monitoring and diagnosing for the generator winding insulation. The greater social and economic benefits have been produced. |
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