scholarly journals Correction to: Study on Electrical Aging Characteristics of Fiber Sheath Materials in Power Transformer Oil

2020 ◽  
Vol 15 (5) ◽  
pp. 2417-2417
Author(s):  
Lu Sun ◽  
Xiaozhou Fan ◽  
Shuo Jiang ◽  
Bowen Wang ◽  
Yunpeng Liu ◽  
...  
Keyword(s):  
Power Transformer ◽  
Transformer Oil ◽  
Electrical Aging

Study on Electrical Aging Characteristics of Fiber Sheath Materials in Power Transformer Oil

2019 ◽  
Vol 14 (1) ◽  
pp. 323-330 ◽  
Author(s):  
Lu Sun ◽  
Xiaozhou Fan ◽  
Shuo Jiang ◽  
Bowen Wang ◽  
Yunpeng Liu ◽  
...  
Keyword(s):  
Power Transformer ◽  
Transformer Oil ◽  
Electrical Aging

FURTHER CONTRIBUTION FOR EVALUATING THE AGING OF TRANSFORMER OIL OF POWER TRANSFORMER

2015 ◽  
Vol 43 (2) ◽  
pp. 211-226
Author(s):  
Sobhy S. Dessouky ◽  
Adel El Faraskoury ◽  
Sherif Ghoneim ◽  
Ahmed Haassan
Keyword(s):  
Power Transformer ◽  
Transformer Oil

scholarly journals Fuzzy Algorithm for Power Transformer Diagnostics

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Nitin K. Dhote ◽  
Jagdish B. Helonde
Keyword(s):  
Gas Chromatography ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Inference System ◽  
Incipient Faults ◽  
The World

Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results fall outside conventional methods codes or when more than one fault exist in the transformer. To overcome these limitations, the fuzzy inference system (FIS) is proposed. Two hundred different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

scholarly journals Forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method

2018 ◽  
Vol 216 ◽  
pp. 03011
Author(s):  
Sergey Barsukov ◽  
Sergey Pakhomov
Keyword(s):  
Service Life ◽  
Power Transformer ◽  
Impurity Content ◽  
Forecast Model ◽  
Threshold Value ◽  
Technical Condition ◽  
Transformer Oil ◽  
Average Risk ◽  
Iron Impurity ◽  
Object Based

The paper is aimed at developing a forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method. It presents a procedure for selecting necessary and sufficient number of diagnostic indicators using the forecast model. The technique has been tested on the basis of a power transformer with a liquid dielectric. A condition-based operation strategy has been proposed for the transformer. According to this strategy, the iron impurity content in the dielectric liquid (oil) of the transformer should be measured every year of operation. Based on the forecast model, it is possible to calculate the variation of average risk (R) and a threshold value of iron impurity content in the transformer oil (k0) for each year of operation. Using these parameters, a reliable forecast model can be constructed to estimate the remaining service life of the transformer. The obtained relationships make it possible to identify a scientifically grounded stage in the service life of a diagnosed object, at which the number of measurable diagnostic indicators (indicators that are necessary for assessing the real technical condition of equipment) can be minimized.

Parallelization Analysis of Dissolved Gases in Transformer Oil Based on Random Forest Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 98-101
Author(s):  
De Wen Wang ◽  
Zhi Wei Sun
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gases ◽  
Random Forest Algorithm ◽  
Dissolved Gas ◽  
Important Method ◽  
Transformer Fault

Dissolved gas analysis (DGA) in oil is an important method for transformer fault diagnosis. This paper use random forest parallelization algorithm to analysis the dissolved gases in transformer oil. This method can achieve a fast parallel fault diagnosis for power equipment. Experimental results of the diagnosis of parallelization of random forest algorithm with DGA samples show that this algorithm not only can improve the accuracy of fault diagnosis, and more appropriate for dealing with huge amounts of data, but also can meet the smart grid requirements for fast fault diagnosis for power transformer. And this result also verifies the feasibility and effectiveness of the algorithm.

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