원문정보
초록
영어
Extenics and rough set theory are brought into transformer fault diagnosing procedure in this paper to get rid of abundant information data and to obtain more precise diagnosing result. Using the dissolved gas data as fault diagnosing attribution set, attributions which are needed for transformer fault diagnosis are predigested and preliminarily grouped by means of rough set method, and then matter element model for transformer’s fault diagnosing is built. With the transformer’s standard fault modes as the transformer’s fault diagnosing decision set, utilizing extenics association function to calculate each fault degree, acceptance and rejection rule is defined to diagnose transformer’s fault. 76 dissolved gas information data have been collected to verify the method proposed in this paper, the diagnosing results show that the correctness of diagnosing results got by this method is better than frequently used IEC three ratio methods.
목차
1. Introduction
2. Basic Theory of Transformer Fault Diagnosing based on Extenics and Attribute Predigesting of Rough Set
3. Attribution Predigesting based on Rough Set Theory
3.1. Attribution Predigesting
3.2. Accepting and Rejecting the Predigested Results
4. Extenics Fault Diagnosing
4.1. Determine the Weight Coefficient
4.2. Fault Diagnosing of Transformer using Association Function of Extenics and Attribution Predigesting of Rough Set Theory
5. Calculation Example
6. Conclusion
Acknowledgements
References