A Combined Fault Diagnosis Method for Power Transformer in Big Data Environment

Yan, Wenjun; Zhang, Liguo

doi:10.1155/2017/9670290

Cited by 8 publications

(9 citation statements)

References 24 publications

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“…For example, if the rate of change of TDCG> 30 ppm, this is considered a high severity thermal failure (T3). To achieve maximum inclusion in the work, the framework uses 9 gas concentration ratios, namely H [9]. The main advantage of gas ratio analysis is "it is independent of the amount of oil involved and depends only on the proportions of the gases involved"…”

Section: Methodsmentioning

confidence: 99%

DGA Method Implementation for Incipient Fault Analysis using Gas Concentrations

Singh¹,

Nigam²,

Malviya³

2021

IJOSCIENCE

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Power transformers are essential devices for the durable and reliable performance of an electrical system. the main objective of this study is to analyze three classical diagnosis techniques to identify incipient faults in Transformer oil using Rogers’s Ratio Method, Doernenburg Ratio Method, and ANN which is a type of artificial intelligence learning method. Implementation of the system in MATLAB software for each diagnosis method and compare their accuracy and efficiency and hence design three diagnosis methods of DGA for condition assessment of Power Transformer. And the analysis on the MATLAB software shall be carried so as to detect the best method for detection of a certain type of fault and the best suited method for overall fault analysis for a certain data sets out of the three methods. This technique utilizes the learning capacity of that artificial neural network has been shown to be more efficient in detecting different mistakes. The overall error detection accuracy of such gas neural network study was found to be 73.8 percent.

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Section: Methodsmentioning

confidence: 99%

DGA Method Implementation for Incipient Fault Analysis using Gas Concentrations

Singh¹,

Nigam²,

Malviya³

2021

IJOSCIENCE

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“…Each DGA data was normalized according to Formula (1) into fault characteristic gas index as shown in Formula (2).…”

Section: Of 18mentioning

confidence: 99%

“…Therefore, it is of great significance to study the fault diagnostic method of a transformer [1]. With the continuous development of computer storage and sensor technology, the online monitoring DGA data of power transformers will show an explosive growth trend [2,3], which has put forward higher requirements on the learning ability, feature extraction ability, and adaptability of transformer diagnostic methods.…”

Section: Introductionmentioning

confidence: 99%

A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis

Duan

2020

Applied Sciences

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With the development of Industry 4.0, as a pivotal part of the power system, large-capacity power transformers are requiring fault diagnostic methods with higher intelligence, accuracy and anti-interference ability. Considering the powerful capability for extracting non-linear features and the sensitivity differences to features of deep learning methods, this paper proposes a deep parallel diagnostic method for transformer dissolved gas analysis (DGA). In view of the insufficient and imbalanced dataset of transformers, adaptive synthetic oversampling (ADASYN) was implemented to augment the fault dataset. Then, the newly constructed dataset was normalized and input into the LSTM-based diagnostic framework. Then, the dataset was converted into images as the input of the CNN-based diagnostic framework. At the same time, the problem of still insufficient data was compensated by the introduction of transfer learning technology. Finally, the diagnostic models were trained and tested respectively, and the Dempster-Shafer (DS) evidence theory was introduced to fuse the diagnostic confidence matrices of the two models to achieve deep parallel diagnosis. The results of the proposed deep parallel diagnostic method show that without complex feature extraction, the diagnostic accuracy rate could reach 96.9%. Even when the dataset was superimposed with 3% random noises, the rate only decreased by 0.62%.Appl. Sci. 2020, 10, 1329 2 of 18 their shortcomings in learning ability, processing efficiency, and feature extraction ability. For example, the learning ability of the fuzzy methods are not satisfying [7,8]. Neural networks (NN) tend to fall into local optimal solutions [9,10]. The K-nearest neighbor (KNN) method is inefficient in high-dimensional space [11]. The support vector machine (SVM) is essentially a two-classifier, which makes it more troublesome to deal with multi-classification problems [12,13]. There are also some relevant diagnosis studies on combining traditional ratio methods and intelligent methods, which are combinations of previous researches, and have achieved certain effects [14][15][16]. However, the features extracted by the ratio methods are still limited. And the anti-interference ability of the combined models need further testing. In recent years, due to the strong ability of deep learning to extract complex non-linear features, some papers have tried to introduce it into the field of transformer fault diagnosis. And the fault diagnostic accuracy of deep learning methods has been significantly improved compared to traditional machine learning diagnostic models'. The authors in [17] proposed a DBN-based fault diagnostic method that used the uncoded ratio of DGA data as the model input. Compared with the traditional methods', the accuracy was significantly improved. The authors in [18] introduced a method for identifying and locating winding faults based on CNN and transformer impulse tests. And the results showed that the method was effective. The authors in [19] studied the internal fault diagnosis of...

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“…Nonlinear regression necessitates the kernel function in the SVR model [16]. The kernel function can be expressed as follows:…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Probability Distribution and Deviation Information Fusion Driven Support Vector Regression Model and Its Application

Fan

Yan

2017

Mathematical Problems in Engineering

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In modeling, only information from the deviation between the output of the support vector regression (SVR) model and the training sample is considered, whereas the other prior information of the training sample, such as probability distribution information, is ignored. Probabilistic distribution information describes the overall distribution of sample data in a training sample that contains different degrees of noise and potential outliers, as well as helping develop a high-accuracy model. To mine and use the probability distribution information of a training sample, a new support vector regression model that incorporates probability distribution information weight SVR (PDISVR) is proposed. In the PDISVR model, the probability distribution of each sample is considered as the weight and is then introduced into the error coefficient and slack variables of SVR. Thus, the deviation and probability distribution information of the training sample are both used in the PDISVR model to eliminate the influence of noise and outliers in the training sample and to improve predictive performance. Furthermore, examples with different degrees of noise were employed to demonstrate the performance of PDISVR, which was then compared with those of three SVR-based methods. The results showed that PDISVR performs better than the three other methods.

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A Combined Fault Diagnosis Method for Power Transformer in Big Data Environment

Cited by 8 publications

References 24 publications

DGA Method Implementation for Incipient Fault Analysis using Gas Concentrations

DGA Method Implementation for Incipient Fault Analysis using Gas Concentrations

A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis

Probability Distribution and Deviation Information Fusion Driven Support Vector Regression Model and Its Application

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