원문정보
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초록
영어
DGA (Dissolved Gas Analysis) is the traditional transformer fault diagnosis method, but it mainly depends on the experience of operators. In order to solve the limitations of traditional method, this paper introduces intelligent method for fault diagnosis of transformer. The intelligent method made fusion of various data, including SCADA data, oil dissolved gas sensor data, related electrical test data, operation maintenance records, and so on, employed space-time weighting fusion method based on BP neural network, and put forward the model of transformer fault diagnosis based on multi-source information fusion, which improved the accuracy of the transformer fault diagnosis dramatically.
목차
Abstract
1. Introduction
2. Related Works
2.1. Multi-source information fusion model
2.2. Transformer fault diagnosis method
3. Transformer Fault Diagnosis Based on Multi-source Information Fusion
3.1. Model of transformer fault diagnosis based on Multi-source information fusion
3.2 Space-time fusion of multi-sensor based on BP neural network
3.3 Procedure of transformer fault diagnosis based on BP neural network
4. Comparison of Transformer Fault Diagnosis Result
5. Conclusion
References
1. Introduction
2. Related Works
2.1. Multi-source information fusion model
2.2. Transformer fault diagnosis method
3. Transformer Fault Diagnosis Based on Multi-source Information Fusion
3.1. Model of transformer fault diagnosis based on Multi-source information fusion
3.2 Space-time fusion of multi-sensor based on BP neural network
3.3 Procedure of transformer fault diagnosis based on BP neural network
4. Comparison of Transformer Fault Diagnosis Result
5. Conclusion
References
키워드
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자료제공 : 네이버학술정보