Parallelization Analysis of Dissolved Gases in Transformer Oil Based on Random Forest Algorithm

Abstract:

Dissolved gas analysis (DGA) in oil is an important method for transformer fault diagnosis. This paper use random forest parallelization algorithm to analysis the dissolved gases in transformer oil. This method can achieve a fast parallel fault diagnosis for power equipment. Experimental results of the diagnosis of parallelization of random forest algorithm with DGA samples show that this algorithm not only can improve the accuracy of fault diagnosis, and more appropriate for dealing with huge amounts of data, but also can meet the smart grid requirements for fast fault diagnosis for power transformer. And this result also verifies the feasibility and effectiveness of the algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 519-520)
• Pages: 98-101
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.519-520.98
• Online since: February 2014
• Authors: De Wen Wang, Zhi Wei Sun
• Keywords: DGA, Failure Diagnosis, Parallelization, Power Transformer, Random Forest (RF)

References

[1] Tiefeng Zhang, Jie Lu, Jie Lu, Qian Ding. Fault Diagnosis of Transformer using Association Rule Mining and Knowledge Base. Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on, Nov. 29 2010-Dec: 737 - 742.

DOI: 10.1109/isda.2010.5687177

[2] Yang, Z, Tang, W. H, Shintemirov, A, Wu, Q.H. Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on (Volume: 39 , Issue: 6 ), Nov. 2009: 597 – 610.

DOI: 10.1109/tsmcc.2009.2021989

[3] Hmood, S, Abu-Siada, A, Masoum, M.A. S, Islam, S.M. Standardization of DGA interpretation techniques using fuzzy logic approach. Condition Monitoring and Diagnosis (CMD), 2012 International Conference on, 23-27 Sept. 2012: 929 - 932.

DOI: 10.1109/cmd.2012.6416305

[4] Ying-Ying Cheng, Chan, P.P.K. , Zhi-Wei Qiu. Random forest based ensemble system for short term load forecasting. Machine Learning and Cybernetics (ICMLC), 2012 International Conference on (Volume: 1 ), 15-17 July 2012: 52 - 56.

DOI: 10.1109/icmlc.2012.6358885

[5] Kulkarni, V. Y , Sinha, P.K. Pruning of Random Forest classifiers: A survey and future directions. Data Science & Engineering (ICDSE), 2012 International Conference on, 18-20 July 2012: 64 – 68.

DOI: 10.1109/icdse.2012.6282329

[6] Iosup A, Ostermann S, Yigitbasi M. N, et al. Performance analysis of cloud computing services for many-tasks scientific computing[J]. IEEE Transactions on Parallel and Distributed Systems, 2011, 22(56): 931-945.

DOI: 10.1109/tpds.2011.66