Research of Pattern Recognition of Partial Discharge in Power Transformer Based on Information Fusion

2012 ◽  
Vol 6 (1) ◽  
pp. 664-667
Author(s):  
Xingang Chen ◽  
Yangyang Zhao ◽  
Chaofeng Zhang ◽  
Xiaoxiao Tian
2011 ◽  
Vol 128-129 ◽  
pp. 933-937
Author(s):  
Xin Gang Chen ◽  
Yang Yang Zhao ◽  
Chao Feng Zhang ◽  
Xiao Xiao Tian

As one of the most important equipments in the power system, partial discharge (PD) affects the transformer’s properties in a long-term period and the partial discharge pattern recognition has most important sense. In the paper,3 kinds of experimental models simulating discharges were designed and model experiments were performed. Based on this, a transformer partial discharge pattern recognition system based on information fusion technology is developed. the finally experiments show that: information fusion have enough ability to recognize different types of partial discharge.


2012 ◽  
Vol 588-589 ◽  
pp. 384-387
Author(s):  
Jin Sha Yuan ◽  
Hai Kun Shang

Partial discharge diagnosis is an important tool for detecting insulation defects in power equipments. This paper presents a pattern recognition approach based on Least Squares Support Vector Machine (LS-SVM) for Ultra High Frequency (UHF) partial discharge diagnosis of power transformer. Six different feature parameters were extracted from the data obtained from Partial Discharge (PD) on-line monitoring system. LS-SVM was used to discriminate between 4 different PD sources. Experimental results demonstrate that the proposed approach has higher recognition accuracy compared with traditional BPNN recognition method under condition of small samples, and has great potential for use of field data.


2013 ◽  
Vol 694-697 ◽  
pp. 2710-2714 ◽  
Author(s):  
Hai Kun Shang ◽  
Jin Sha Yuan ◽  
Yu Wang ◽  
Song Jin

Partial discharge diagnosis plays an important role in condition monitoring of power transformer. Different discharge types cause varying degrees of effects on insulation degradation. Therefore pattern recognition for partial discharge is necessary for power transformer fault diagnosis. Probabilistic Neural Networks (PNN) is based on the theory of Bayesian minimum risk which perfectly fits for classification. In this paper PNN is employed for pattern recognition of partial discharge. 18 characteristic parameters are extracted from UHF partial discharge signals as the inputs of the PNN classifier. Results show that the PNN method gives fast and accurate classification performance.


Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2223 ◽  
Author(s):  
Sayed A. Ward ◽  
Adel El-Faraskoury ◽  
Mohamed Badawi ◽  
Shimaa A. Ibrahim ◽  
Karar Mahmoud ◽  
...  

Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV–40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.


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