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

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Yan Wang ◽  
Liguo Zhang
Keyword(s):  
Big Data ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Support Vector ◽  
Diagnosis Method ◽  
Scope Of Application ◽  
Diagnosis Model ◽  
Data Environment ◽  
Transformer Fault

The fault diagnosis method based on dissolved gas analysis (DGA) is of great significance to detect the potential faults of the transformer and improve the security of the power system. The DGA data of transformer in smart grid have the characteristics of large quantity, multiple types, and low value density. In view of DGA big data’s characteristics, the paper first proposes a new combined fault diagnosis method for transformer, in which a variety of fault diagnosis models are used to make a preliminary diagnosis, and then the support vector machine is used to make the second diagnosis. The method adopts the intelligent complementary and blending thought, which overcomes the shortcomings of single diagnosis model in transformer fault diagnosis, and improves the diagnostic accuracy and the scope of application of the model. Then, the training and deployment strategy of the combined diagnosis model is designed based on Storm and Spark platform, which provides a solution for the transformer fault diagnosis in big data environment.

Transformer Fault Diagnosis with the Duval Triangle and Heuristic Techniques

2014 ◽  
Vol 535 ◽  
pp. 157-161
Author(s):  
Jeeng Min Ling ◽  
Ming Jong Lin ◽  
Chao Tang Yu
Keyword(s):  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Power Company ◽  
Triangle Method ◽  
Diagnosis Method ◽  
Combustible Gases ◽  
Transformer Fault

Dissolved gas analysis (DGA) is an effective tool for detecting incipient faults in power transformers. The ANSI/IEEE C57.104 standards, the most popular guides for the interpretation of gases generated in oil-immersed transformers, and the IEC-Duval triangle method are integrated to develop the proposed power transformer fault diagnosis method. The key dissolved gases, including H2, CH4, C2H2, C2H4, C2H6, and total combustible gases (TCG), suggested by ASTM D3612s instruction for DGA is investigated. The tested data of the transformer oil were taken from the substations of Taiwan Power Company. Diagnosis results with the text form called IEC-Duval triangle method show the validation and accuracy to detect the incipient fault in the power transformer.

Transformer Fault Diagnosis Method Based on Support Vector Machine and Ant Colony

2013 ◽  
Vol 659 ◽  
pp. 54-58 ◽  
Author(s):  
Li Li Mo
Keyword(s):  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Cross Validation ◽  
Gas Analysis ◽  
Ant Colony ◽  
Support Vector ◽  
Dissolved Gas ◽  
Wide Range ◽  
Diagnosis Method ◽  
Transformer Fault

For transformer fault diagnosis of the IEC three-ratio is an effective method in the dissolved gas analysis (DGA). But it does not offer completely objective, accurate diagnosis for all the faults. Aiming at parameters are confirmed by the cross validation, using the ant colony algorithm, the ACSVM-IEC method for the transformer fault diagnosis is proposed. Experimental results show that the proposed algorithm in this paper that can find out the optimum accurately in a wide range. The proposed approach is robust and practical for transformer fault diagnosis.

A Hybrid Optimization Technique for Fault Classification in Power Transformer Using Dissolved Gas Analysis

2014 ◽  
Vol 573 ◽  
pp. 708-715
Author(s):  
V. Gomathy ◽  
S. Sumathi
Keyword(s):  
Fault Diagnosis ◽  
Power Supply ◽  
Power Transformer ◽  
Gas Analysis ◽  
Fault Classification ◽  
Support Vector ◽  
Dissolved Gas ◽  
Swarm Optimization ◽  
Dissolved Gas Analysis ◽  
Transformer Fault

To allow utilities to fulfill self-imposed and regulative performance targets the demand for new optimized tools and techniques to Estimate the performance of modern Transformers has increased. The modern power transformers has subjected to different types of faults, which affect the continuity of power supply which in turn causes serious economic losses. To avoid the interruption of power supply, various fault diagnosis approaches are adopted to detect faults in the power transformer and has to eliminate the impacts of the faults at the initial stage. Among the fault diagnosis methods, the hybrid technique of Particle Swarm Optimization (PSO) with Support Vector Machine (SVM) learning algorithm is simple conceptually derived and its implementation process is faster with better scaling properties for complex problems with non linearity and load variations but performance factor related to accuracy has a declined value in case of correlations implicit . In order to obtain better fault diagnosis to improve the service of the power transformer, SVM is optimized with Improved PSO technique to achieve high interpretation accuracy for Dissolved Gas Analysis (DGA) of power transformer through the extracting positive features from both the techniques. Primary SVM is applied to establish classification features for faults in the transformer through DGA. The features are applied as input data to Autonomous optimized Technique for faults analysis. The proposed methodology obtains the DGA data set from diagnostic gas in oil of 500 KV main transformers of Pingguo Substation in South China Electric Power Company. The simulations are carried out in MATLAB software with an Intel core 3 processor with speed of 3 GHZ and 4 GB RAM PC. The result obtained by Autonomous optimized Technique (IPSO-SVM) is compared against PSO-SVM to estimate the performance of the classifiers in terms of execution time and quality of classification for precision. The test results indicate that the Autonomous optimization of IPSO-SVM approach has significantly improved the classification accuracy and computational time for power transformer fault classification. Keywords: Transformer Fault Analysis, Improved Particle Swarm Optimization, Hybrid Optimization, Dissolved Gas Analysis, Support Vector Machine

scholarly journals Improved Genetic Algorithm and XGBoost Classifier for Power Transformer Fault Diagnosis

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhanhong Wu ◽  
Mingbiao Zhou ◽  
Zhenheng Lin ◽  
Xuejun Chen ◽  
Yonghua Huang
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Diagnostic Methods ◽  
Identification Accuracy ◽  
Basic Unit ◽  
Support Vector ◽  
Improved Genetic Algorithm ◽  
Transformer Fault

Power transformer is an essential component for the stable and reliable operation of electrical power grid. The traditional transformer fault diagnostic methods based on dissolved gas analysis are limited due to the low accuracy of fault identification. In this study, an effective transformer fault diagnosis system is proposed to improve identification accuracy. The proposed approach combines an improved genetic algorithm (IGA) with the XGBoost to form a hybrid diagnosis network. The combination of the improved genetic algorithm and the XGBoost (IGA-XGBoost) forms the basic unit of the proposed method, which decomposes and reconstructs the transformer fault recognition problem into several minor problems IGA-XGBoosts can solve. The results of simulation experiments show that the IGA performs excellently in the combined optimization of input feature selection and the XGBoost parameter, and the proposed method can accurately identify the transformer fault types with an average accuracy of 99.2%. Compared to IEC ratios, dual triangle, support vector machine and common vector approach the diagnostic accuracy of the proposed method is improved by 30.2, 47.2, 11.2, and 3.6%, respectively. The proposed method can be a potential solution to identify the transformer fault types.

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