Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Li, Enwen; Wang, Linong; Song, Bin; Siliang, Jian

doi:10.3390/en11092344

Cited by 22 publications

(8 citation statements)

References 32 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is difficult to obtain data classification that conforms to engineering practice with conventional FCM. In our previous work [17], the influence of monotonicity of the membership function on clustering analysis is examined. Accordingly, an improved membership function for FCM is constructed.…”

Section: The Reference Fault Set Obtained With Clusteringmentioning

confidence: 99%

“…Thus, the sensitivity of conventional FCM to initial value are relieved, which are essential factors for its practical application. The clustering method is as follows [17].…”

Section: The Reference Fault Set Obtained With Clusteringmentioning

confidence: 99%

“…Another data source [22] is an appendix to the China's power industry standards DL/T722-2014, including 17 filed cases. The last dataset [17] includes 28 field DGA, and these data were from the book-Typical Cases: Application of Grid Equipment Status Detection Technology (2011-2013) [26], published by the Operation and Maintenance Department of the Chinese State Grid Corporation, which details actual faults found after transformer disassembly. These DGA data have a total of 201, covering six faults classes.…”

Section: B the Dga Data Sourcesmentioning

confidence: 99%

See 2 more Smart Citations

Fault Diagnosis of Power Transformers With Membership Degree

Wang

Song

2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Power transformers are important equipment for power systems, and a dissolved gas analysis (DGA) is widely used to detect incipient faults in oil-pregnant transformers. The conventional methods are prone to misinterpreting the gas data near the boundaries and the correct rate is low. Though a high correct rate is reported with intelligent methods as artificial neural network, support vector machine, and so on, these methods are usually too complicated to be implemented practically on a wide range. Based on clustering techniques, this paper proposes a new method for fault diagnosis of transformers with the DGA. A reference fault set is provided, and the fault diagnosis is implemented by calculating the membership of the DGA data to the reference fault set. Test with credible DGA dataset (201 field cases) shows that the correct rate of the new method is 89%, while the David triangle method is 79% and the IEC ratio method is 59%, which demonstrate the superiority of the proposed method to the conventional ones. The new method is simple and highly accurate, indicating a good application prospect in engineering practice. INDEX TERMS Power transformer, fuzzy clustering, fault diagnosis, membership degree. I. INTRODUCTION The oil-paper insulation system in power transformers operates under the effects of high temperature and strong electromagnetic environment, and the insulation medium can slowly decompose into a number of small molecules. The decomposition gases dissolved in oil are H 2 , CH 4 , C 2 H 6 , C 2 H 4 , C 2 H 2 , CO 2 , CO and N 2. However, when a fault occurs, the insulation breaks down more quickly and the decomposition products will be different according to the type and severity of the fault [1], [2]. Dissolved gas analysis (DGA) is widely used to detect incipient faults in oil-pregnant transformers. This technique involves several steps, such as taking oil samples from a transformer, removing dissolved gases from oil, determining gas component content, and identifying fault types [3]. Fault identification is a decisive step in the internal fault state determination of the transformer in DGA analysis. Various computational and graphical methods employing gas ratios and proportions of gases dissolved in oil determined by gas chromatography have been worked out for recognizing the characteristic patterns of the dissolved gases that are associated with the main types of faults [4], [5]. These methods available to interpreted DGA data include The associate editor coordinating the review of this manuscript and approving it for publication was Chuan Li. Key Gas Method, Doernenburg Ratio Method, Rogers Ratio Method, IEC Ratio Method and Duval Triangle Method, and they have been developed and validated using large sets of data for equipment in service. In these methods, the multiple numeric thresholds and gas boundaries are commonly set to classify features of the dissolved gas data. However, these thresholds and boundaries do not physically exist, and the gas data near the ratio boundaries are prone to misinte...

show abstract

Section: The Reference Fault Set Obtained With Clusteringmentioning

confidence: 99%

“…Thus, the sensitivity of conventional FCM to initial value are relieved, which are essential factors for its practical application. The clustering method is as follows [17].…”

Section: The Reference Fault Set Obtained With Clusteringmentioning

confidence: 99%

Section: B the Dga Data Sourcesmentioning

confidence: 99%

See 1 more Smart Citation

Fault Diagnosis of Power Transformers With Membership Degree

Wang

Song

2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…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].…”

mentioning

confidence: 99%

A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis

Duan

2020

Applied Sciences

View full text Add to dashboard Cite

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...

show abstract

“…It is essential to explore the principles, methods and means from various disciplines that are helpful in the fault diagnosis of transformers. With the rapid development of computer science and the rise of machine learning, multiple intelligent approaches such as artificial neural network [17][18][19], support vector machine (SVM) [20][21][22], fuzzy theory [23][24][25], extreme learning machine [26], and Bayesian network [27] have been applied in practice. A smart fault diagnostic approach based on integrating five interpretation methods using neural networks is proposed in [28].…”

mentioning

confidence: 99%

A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers

Wang

Zhang

et al. 2019

Energies

View full text Add to dashboard Cite

This paper presents a Dynamic Adam and dropout based deep neural network (DADDNN) for fault diagnosis of oil-immersed power transformers. To solve the problem of incomplete extraction of hidden information with data driven, the gradient first-order moment estimate and second-order moment estimate are used to calculate the different learning rates for all parameters with stable gradient scaling. Meanwhile, the learning rate is dynamically attenuated according to the optimal interval. To prevent over-fitted, we exploit dropout technique to randomly reset some neurons and strengthen the information exchange between indirectly-linked neurons. Our proposed approach was utilized on four datasets to learn the faults diagnosis of oil-immersed power transformers. Besides, four benchmark cases in other fields were also utilized to illustrate its scalability. The simulation results show that the average diagnosis accuracies on the four datasets of our proposed method were 37.9%, 25.5%, 14.6%, 18.9%, and 11.2%, higher than international electro technical commission (IEC), Duval Triangle, stacked autoencoders (SAE), deep belief networks (DBN), and grid search support vector machines (GSSVM), respectively.Energies 2019, 12, 995 2 of 16 commonly used fault diagnosis methods. However, these traditional methods have limitations. They may not be able to provide an interpretation to every possible combination of various ratios and may have excessively absolute coding boundary. Due to the objective uncertainty of transformer fault itself and the boundaries of the subjective judgment, it is difficult to meet the requirements of engineering application with the above ratio methods.Since the transformer faults are complex and concealed, simple and crude methods have difficulty performing effective diagnosis. It is essential to explore the principles, methods and means from various disciplines that are helpful in the fault diagnosis of transformers. With the rapid development of computer science and the rise of machine learning, multiple intelligent approaches such as artificial neural network [17][18][19], support vector machine (SVM) [20][21][22], fuzzy theory [23][24][25], extreme learning machine [26], and Bayesian network [27] have been applied in practice. A smart fault diagnostic approach based on integrating five interpretation methods using neural networks is proposed in [28]. Ma et al. [29] presented an intelligent framework for transformer condition monitoring and assessment. Within their framework, different intelligent algorithms can be effectively deployed. Peimankar et al. [30] first used multi-objective particle swarm optimization (PSO) algorithm to select the best subset of features corresponding to each fault class of power transformers. Then, they used ensemble learning systems to classify actual faults of transformers. Sherif et al. [31] utilized the thermodynamic theory to evaluate the severity based on the energy associated with each transformer fault type. These intelligent methods remedy the disadvantages of t...

show abstract

Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Cited by 22 publications

References 32 publications

Fault Diagnosis of Power Transformers With Membership Degree

Fault Diagnosis of Power Transformers With Membership Degree

A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis

A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers

Contact Info

Product

Resources

About