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
2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Nitin K. Dhote ◽  
Jagdish B. Helonde

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.


2018 ◽  
Vol 216 ◽  
pp. 03011
Author(s):  
Sergey Barsukov ◽  
Sergey Pakhomov

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.


2014 ◽  
Vol 519-520 ◽  
pp. 98-101
Author(s):  
De Wen Wang ◽  
Zhi Wei Sun

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.


2018 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
A.A.N. Amrita ◽  
W.G. Ariastina ◽  
I.B.G. Manuaba

Power transformer is very important in electric power system due to its function to raise or lower the voltage according to its designation. On the power side, the power transformer serves to raise voltage to be transmitted to the transmission line. On the transmission side, the power transformer serves to distribute the voltage between the main substations or down to the distribution voltage. On the distribution side, the stresses are channeled to large customers or lowered to serve small and medium customers. As the power transformer is so importance, it is necessary to protect against disturbance, as well as routine and periodic maintenance, so that the power transformer can operate in accordance with the planned time. Some factors that affect the duration of the power transformer is the ambient temperature, transformer oil temperature, and the pattern of load. Load that exceeds the maximum efficiency of the transformer which is 80% of its capacity will cause an increase in transformer oil temperature. Transformer oil, other than as a cooling medium also serves as an insulator. Increasing the temperature of transformer oil will affect its ability as an isolator that is to isolate the parts that are held in the transformer, such as iron core and the coils. If this is prolonged and not handled properly, it will lead to failure / breakdown of insulation resulting in short circuit between parts so that the power transformer will be damaged. PLN data indicates that the power transformer is still burdened exceeding maximum efficiency especially operating in the work area of PLN South Bali Area. The results of this study, on distribution transformers with different loads, in DS 137, DS 263 and DS 363, show that DS 363 transformer with loading above 80% has the shortest residual life time compared to DS 263 and DS 137 which loading less than 80%.


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