Transformer Fault Diagnosis Based on Gene Expression Programming Classifier

Zhuo Dong; Yong Li Zhu

doi:10.4028/www.scientific.net/AMR.354-355.1022

Paper Titles

Improved Bacterial Colony Chemotaxis Algorithm for Distribution Network Planning
p.1002

Research on the Generation Expansion Planning in Carbon Market in China
p.1007

The Application of the Complex Modulation Zoom-FFT Based on Complex Analytical Bandpass Filter in Interhamonic Detection of Power System
p.1012

Simulating Research on a New Current Controlling Method
p.1016

Transformer Fault Diagnosis Based on Gene Expression Programming Classifier
p.1022

The Service Restoration in the Distribution System with Distributed Generators Based on the Heuristic Multi-Agent Evolutionary Algorithm
p.1027

CMA the Key Equipment for Condition Monitoring in Power Transmission and Transformation System
p.1031

The Setting Method of Matching Coefficient for PSS2B Model of IEEE
p.1035

Research on Models and Compensation Mechanism of Energy-Saving Generation Dispatching
p.1041

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 354-355Transformer Fault Diagnosis Based on Gene...

Transformer Fault Diagnosis Based on Gene Expression Programming Classifier

Abstract:

Gene Expression Programming (GEP), which is suitable for transformer fault diagnosis Classification, is combined with transformer oil dissolved gas analysis (DGA), and also a method of transformer fault diagnosis based on self-adaptive GEP classification algorithm is proposed. We choose 400 groups of DGA measured data which includes a variety of failure and does not redundant as the training samples and test samples of the GEP classifier. A large number of diagnostic examples show that the proposed self-adaptive classification GEP is suitable for transformer fault diagnosis, and its performance is better than using Naive Bayes (NB) classifier, BP network and Immune classification.

You might also be interested in these eBooks

Progress in Power and Electrical Engineering

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 354-355)

Pages:

1022-1026

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.354-355.1022

Citation:

Cite this paper

Online since:

October 2011

Authors:

Zhuo Dong, Yong Li Zhu

Keywords:

Data Normalization, DGA, Gene Expression Programming (GEP), Transformer Fault Diagnosis

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Ferreira C. "Gene expression programming: A new adaptive algorithm for solving problems," Complex Systems, vol. 13, pp.87-129, 2001.

Google Scholar

[2] LI Qu, CAI Zhihua, ZHU Li, et al. "Application of Gene Expression Programming in Predicting the Amount of Gas Emitted from Coal Face," JOURNAL OF BASIC SCIENCE AND ENGINEERING, vol. 12, pp.49-54, 2004.

Google Scholar

[3] Zhang Liechao, Cai Zhihua, Chen Ansheng. "A Survey of SGA, GP, GEP," Computer Information, vol. 22, p.185, 2006．

Google Scholar

[4] FAN Xin-qiao, HUO Li-min, HUANG Li-hua, et al. "Short-term load forecasting based on Genetic Programming," Journal of North China Electric Power University, vol. 34, pp.48-49, 2007.

Google Scholar

[5] Huo Limin, Yin Jinliang, Fan Yunfei, et al. "Short-term load forecasting of countryside distribution network based on improved gene expression programming," Transactions of the CSAE, vol. 25, pp.193-194, 2009.

DOI: 10.1109/wcica.2008.4593850

Google Scholar

[6] WU Zhong-li, YANG Jian, ZHU Yong-li, et al. "Power transformer fault diagnosis based on rough set theory and support vector machines," Power System Protection and Control, vol. 38, pp.582-83, 2010.

Google Scholar

[7] Li Jian, Sun Caixin. "STUDY ON THE MODELS OF FAULT DIAGNOSIS ABOUT POWER TRANSFORMER BASED ON DISSOLVED GASES ANALYSIS," Chongqing: Chongqing University, 2002.

Google Scholar

[8] Wei Gen, Caixin Sun. "Study on fault diagnoses methods of transformer DGA with fuzzy model hierarchy classification" ICMEP, pp.167-171, 2000.

Google Scholar

[9] ZHOU Zhi-Hua, YIN Xu-Ri, CHEN Zhao-Qian, et al. "APPLICATION OF NEURAL NETWORK TO THE DETECTION OF POWER TRANSFORMER RUNNING STATE". ACTA AUTOMATICA SINICA, vol. 28, pp.301-302, 2002.

Google Scholar

[10] LIU Xiao-tong. "Study on Data Normalization in BP Neural Network,". MECHANICAL ENGINEERING & AUTOMATION, vol. 3, pp.122-123, 2010.

Google Scholar

[11] LIANG Jia-zheng, XUE Zhi. "Standardized Processing Based on Network Data" Communications Technologies, vol. 7, pp.46-47, 2010.

Google Scholar