Fault diagnosis of power transformer based on support vector machine with genetic algorithm

Expert Systems with Applications

Volumn 36, Issue 8, 2009, Pages 11352-11357

  Fei, Sheng wei ^a   Zhang, Xiao bin ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b Guangxi Special Equipment Supervision and Inspection Institute   (China)

Author keywords

Fault diagnosis; Genetic algorithm; Power transformer; Support vector machine

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLASSIFICATION ACCURACY; DIAGNOSTIC ACCURACY; ELECTRIC POWER COMPANY; ELECTRICAL POWER SUPPLY; EXPERIMENTAL DATA; FAULT DIAGNOSIS; FREE PARAMETERS; HIGH DIMENSIONS; MACHINE LEARNING METHODS; NON-LINEARITY; POTENTIAL FAULTS; SMALL SAMPLING; STATISTICAL LEARNING THEORY; SVM CLASSIFIERS;

EDUCATION; ELECTRIC INSTRUMENT TRANSFORMERS; ELECTRIC POWER SYSTEMS; ELECTRIC UTILITIES; GENETIC ALGORITHMS; IMAGE RETRIEVAL; NEURAL NETWORKS; SAMPLING; SUPPORT VECTOR MACHINES; TRANSFORMER SUBSTATIONS; VECTORS;

POWER TRANSFORMERS;

EID: 67349288151     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.03.022     Document Type: Article

References (17)

1. Ancona N., Cicirelli G., Stella E., and Distante A. Ball detection in static images with support vector machines for classification. Image and Vision Computing 21 8 (2003) 675-692
2. Bernd H., Thomas S., Sam P., and Tomaso P. Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognition 36 9 (2003) 2007-2017
3. Davut, H., Ahmet, B., & Murat, B.(2008). Prediction of aeration efficiency on stepped cascades by using least square support vector machines. Expert Systems with Applications (Available online).
4. Duraisamy V., Devarajan N., Somasundareswari D., Antony Maria Vasanth A., and Sivanandam S.N. Neuro fuzzy schemes for fault detection in power transformer. Applied Soft Computing 7 2 (2007) 534-539
5. Engin, A. (2007). Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm - Support vector machines: HGASVM. Expert Systems with Applications (Available online).
6. Fabien L., Suen C.Y., and Bloch Ge. A trainable feature extractor for handwritten digit recognition. Pattern Recognition 40 6 (2007) 1816-1824
7. Hung C.-P., and Wang M.-H. Diagnosis of incipient faults in power transformers using CMAC neural network approach. Electric Power Systems Research 71 3 (2004) 235-244
8. Lee C.W., Jung K., and Kim H.J. Automatic text detection and removal in video sequences. Pattern Recognition Letters 24 15 (2003) 2607-2623
9. Lin, C.-H., Wu, C.-H., & Huang, P.-Z. (2008). Grey clustering analysis for incipient fault diagnosis in oil-immersed transformers. Expert Systems with Applications (Available online).
10. Lin C.E., Ling J.M., and Huang C.L. An expert system for transformer fault diagnosis using dissolved gas analysis. IEEE Transactions on Power Delivery 8 1 (1993) 231-238
11. Mohamed E.A., Abdelaziz A.Y., and Mostafa A.S. A neural network-based scheme for fault diagnosis of power transformers. Electric Power Systems Research 75 1 (2005) 29-39
12. Ren N., Liang J., Gu B., and Han H. Fault diagnosis strategy for incompletely described samples and its application to refrigeration system. Mechanical Systems and Signal Processing 22 2 (2008) 436-450
13. Shriberg E., Ferrer L., Kajarekar S., Venkataraman A., and Stolcke A. Modeling prosodic feature sequences for speaker recognition. Speech Communication 46 3-4 (2005) 455-472
14. Su Q., Mi C., Lai L.L., and Austin P. A fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer. IEEE Transactions on Power Systems 15 2 (2000) 593-598
15. Vapnik V. The nature of statistical learning theory (1995), Springer-Verlag, New York
16. Long X., Louis Cleveland W., and Lawrence Yao Y. Multiclass cell detection in bright field images of cell mixtures with ECOC probability estimation. Image and Vision Computing 26 4 (2008) 578-591
17. Yuan S.-F., and Chu F.-L. Support vector machines-based fault diagnosis for turbo-pump rotor. Mechanical Systems and Signal Processing 20 4 (2006) 939-952