Transformer Fault Diagnosis Method Based on Improved ANFIS

2011 ◽  
pp. 271-280
Author(s):  
Hongsheng Su
Keyword(s):  
Fault Diagnosis ◽  
Diagnosis Method ◽  
Transformer Fault

Transformer Fault Diagnosis with the Duval Triangle and Heuristic Techniques

2014 ◽  
Vol 535 ◽  
pp. 157-161
Author(s):  
Jeeng Min Ling ◽  
Ming Jong Lin ◽  
Chao Tang Yu
Keyword(s):  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Power Company ◽  
Triangle Method ◽  
Diagnosis Method ◽  
Combustible Gases ◽  
Transformer Fault

Dissolved gas analysis (DGA) is an effective tool for detecting incipient faults in power transformers. The ANSI/IEEE C57.104 standards, the most popular guides for the interpretation of gases generated in oil-immersed transformers, and the IEC-Duval triangle method are integrated to develop the proposed power transformer fault diagnosis method. The key dissolved gases, including H2, CH4, C2H2, C2H4, C2H6, and total combustible gases (TCG), suggested by ASTM D3612s instruction for DGA is investigated. The tested data of the transformer oil were taken from the substations of Taiwan Power Company. Diagnosis results with the text form called IEC-Duval triangle method show the validation and accuracy to detect the incipient fault in the power transformer.

Transformer Fault Diagnosis Method Based on Self-Powered RFID Sensor Tag, DBN, and MKSVM

2019 ◽  
Vol 19 (18) ◽  
pp. 8202-8214 ◽  
Author(s):  
Chaolong Zhang ◽  
Yigang He ◽  
Shanhe Jiang ◽  
Tao Wang ◽  
Lifen Yuan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Diagnosis Method ◽  
Self Powered ◽  
Transformer Fault

Research on Transformer Fault Diagnosis Based on Online Sequential Extreme Learning Machine

2019 ◽  
Vol 12 (5) ◽  
pp. 408-413
Author(s):  
Yuancheng Li ◽  
Xiaohan Wang ◽  
Yingying Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Experimental Result ◽  
Dissolved Gas ◽  
Traditional Methods ◽  
Electric System ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Transformer Fault

Background: Transformer is one of the most important pivot equipment in an electric system which undertakes major responsibility. Therefore, it is very important to identify the fault of the transformer accurately and transformer fault diagnosis technology becomes one topic with great research value. Methods: In this paper, after analyzing the shortcomings of traditional methods, we have proposed a transformer fault diagnosis method based on Online Sequential Extreme Learning Machine (OS-ELM) and dissolved gas-in-oil analysis. This method has better precision than some commonly used methods at present. Furthermore, OS-ELM is more efficient than ELM. In addition, we analyze the effect of different parameter selection on the performance of the model by contrast experiments. Results: The experimental result shows that OS-ELM has certain promotion in precision than some traditional methods and can obviously improve the speed of training than ELM. Besides, it is known that the number of neurons in the hidden layer and the size of dataset have a great effect on the model. Conclusion: The transformer fault diagnosis method based on OS-ELM can effectively identify the faults and more efficient than ELM.

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