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Alternative TitleResearch on Transformer Fault Diagnosis Based on Multi-source Information Fusion
Thesis Advisor曹云侠
Keyword变压器 故障诊断 Dga 多源信息融合
Call NumberTM407/D58/2016
Degree Discipline控制理论与控制工程
Degree Name硕士
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Other AbstractAt first, this paper introduced the research status of large oil-immersed transformer fault diagnosis and then analyzed the common diagnostic method for transformer and its shortcomings. Then this paper introduce the basic structure of oil-immersed power transformer and the relationship between internal fault of transformer and fault characteristics. Finally, this paper introduces two methods of fault diagnosis under the framework of multi-source information fusion in detail. Fault diagnosis framework of multi-source information fusion is composed of fault diagnosis method based on DGA and fault diagnosis method based on the electrical and related quantity. In this framework, it treats the internal transformer as a whole, which can avoid limitations and sidedness by a single method of diagnosis or only a particular characteristic. The input variable of fault diagnosis method based on DGA is the five types of gas, and the diagnosis principle is based on ICSBP algorithm, which can diagnose the fault type and degree according to the relationship between different characteristics of the gas (ie., composition &content) and the different faults. It is noted that ICSBP algorithm is the improved cuckoo search algorithm, which can be used to optimize BP neural network. In detail, this algorithm realized three improvements in terms of ω, Pa and ?. Also, the further validation proved ICSBP algorithm can do better than the previous one in terms of convergence speed and classification accuracy, thus it can be better applied to transformer fault diagnfosis. Besides, fault diagnosis method based on the electrical and related quantity is made up of various types of sensors, conditioning circuit, AD, DSP etc. The method can realize real-time monitoring for internal key objects of transformer, such as transformer core and winding, micro water, oil temperature, etc, which can find unexpected exception. The combination of this two fault diagnosis results can help make more accurate and comprehensive internal fault diagnosis for the transformer.
Contribution Rank1
Document Type学位论文
Recommended Citation
GB/T 7714
丁腾飞. 基于多源信息融合的变压器故障诊断方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2016.
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