Power transformer fault diagnosis using fuzzy logic technique based on dissolved gas analysis and furan analysis

2017 ◽  
Author(s):  
Maria-Cristina Nitu ◽  
Ancuta-Mihaela Aciu ◽  
Claudiu-Ionel Nicola ◽  
Marcel Nicola
Keyword(s):  
Fuzzy Logic ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Transformer Fault ◽  
Fuzzy Logic Technique

scholarly journals Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN

2020 ◽  
Vol 10 (13) ◽  
pp. 4440 ◽  
Author(s):  
Yongxin Liu ◽  
Bin Song ◽  
Linong Wang ◽  
Jiachen Gao ◽  
Rihong Xu
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Power Transformer ◽  
Gas Analysis ◽  
Gas Content ◽  
Amplification Coefficient ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Density Clustering ◽  
Transformer Fault

The transformers work in a complex environment, which makes them prone to failure. Dissolved gas analysis (DGA) is one of the most important methods for oil-immersed transformers’ internal insulation fault diagnosis. In view of the high correlation of the same fault data of transformers, this paper proposes a new method for transformers’ fault diagnosis based on correlation coefficient density clustering, which uses density clustering to extrapolate the correlation coefficient of DGA data. Firstly, we calculated the correlation coefficient of dissolved gas content in the fault transformers oil and enlarged the correlation of the same fault category by introducing the amplification coefficient, and finally we used the density clustering method to cluster diagnosis. The experimental results show that the accuracy of clustering is improved by 32.7% compared with the direct clustering judgment without using correlation coefficient, which can effectively cluster different types of transformers fault modes. This method provides a new idea for transformers fault identification, and has practical application value.

scholarly journals A Semi-Supervised Autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 178295-178310
Author(s):  
Sunuwe Kim ◽  
Soo-Ho Jo ◽  
Wongon Kim ◽  
Jongmin Park ◽  
Jingyo Jeong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Transformer Fault

Oil-Filled Power Transformers Fault Diagnosis Based on Fuzzy-DRNN

2013 ◽  
Vol 448-453 ◽  
pp. 2520-2523
Author(s):  
Ying Ping Fan ◽  
Hui Da Duan
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Power Transformers ◽  
Dissolved Gas ◽  
Dynamic Neural Network ◽  
The Neural Network ◽  
Transformer Fault

In recent years, improved three-ratio is an effective method for transformer fault diagnosis based on Dissolved Gas Analysis (DGA). In this paper, a simple dynamic neural network named as diagonal recurrent neural network (DRNN) is used to resolve the online fault diagnosis problems for oil-filled power transformer based on DGA. Because of the characteristic of improved three-ratio boundary is lack of matching, fuzzy logic in fault diagnosis is presented also to deal with the data of the neural network inputs. DRNN is used to model the fault diagnosis structure, the fuzzy logic is used to improve the faults diagnose reliability. In addition, some cases are used to show the capability of the suggested method in oil-filled power transformers fault diagnosis.

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