Abstract
There are various types of transformer device defects and the formation reasons are complex. Exploring the influencing factors and occurrence of transformer devices defects is a focus in the field of power transmission and transformation devices state inspection and evaluation. This paper proposes an analysis method, multidimensional FP-Growth algorithm (MDFPG) to mine association rules from multidimensional transformer defect records. The method combines records from different system of power grid to construct multidimensional records first. Then, the records are preprocessed and encoded into single dimension form. The MDFPG method speeds up the mining process by adding a pruning step. Experiments show that MDFPG method has a better performance than FP-Growth algorithm on large data sets. Some conclusions can be learned from the experiment result, which has a certain value for making equipment maintenance plans and exploring defect occurrence regularity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang, T.F., Pei, L., Jing-Lu, L.I., et al.: New fault diagnosis method of power transformer by combination of FCM and IEC three-ratio method. High Volt. Eng. 33(8), 66–70 (2007)
Zhang, W., Yuan, J., Zhang, T., et al.: An improved three-ratio method for transformer fault diagnosis using B-spline theory. 34(24), 4129–4136 (2014)
Ling, R., Xie, Q., Gao, S., et al.: Application of artificial neural network and information fusion technology in power transformer condition assessment. High Voltage Eng. (2014)
Gong, R.K., Liang, M.A., Zhao Y.J., et al.: Fault diagnosis for power transformer based on quantum neural network information fusion. Power Syst. Prot. Control 39(23), 79–84+88 (2011)
Song, Z.J., Jian, W.: Transformer fault diagnosis based on BP neural network optimized by fuzzy clustering and LM Algorithm. High Volt. Apparatus 49(5), 54–59 (2013)
Wang, Y., Chen, B.: An integrated life estimation model of power transformer based on hierarchical architecture and health index. Power Syst. Technol. 38(10), 2845–2850 (2014)
Zhou, J.K., et al.: Research on optimal transformer maintenance scheme based on LS-SVM. Zhongguo Dianji Gongcheng Xuebao/Proc. Chin. Soc. Electr. Eng. 32(22), 94–103 (2012)
Li, L.I., Deng, Z., Xie, L., et al.: A condition assessment method of power transformers based on association rules and variable weight coefficients. Proc. Chin. Soc. Electr. Eng. 24 (2013)
Li, L., Cheng, Y., Xie, L.J., et al.: An integrated method of set pair analysis and association rule for fault diagnosis of power transformers. IEEE Trans. Dielectr. Electr. Insul. 22(4), 2368–2378 (2015)
Dai, K., Wang, Z.J., Zhang, R.P.: Research and improvement of Apriori algorithm for mining association rules. Comput. Appl. Soft. 43, 916–921 (2009)
Mao, Y.X., Chen, T.B., Shi, B.L.: Efficient method for mining multiple-level and generalized association rules. J. Soft. 22(12), 2965–2980 (2011)
Agrawal, R., Imielinski, T., Swami, A.: Database mining: a performance perspective. IEEE Trans. Knowl. Data Eng. 5(6), 914–925 (1993)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Data Mining Concepts Models Methods & Algorithms, 2nd edn. 5(4), 1–18 (2000)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8(1), 53–87 (2015)
Xiao-Dong, H.E., Liu, W.G.: Comparison of association rules mining methods in data mining. Comput. Eng. Des. (2005)
Feng, J., Tao, H.C.: Analysis and comparison of representative algorithms for mining association rules. Comput. Technol. Dev. (2007)
Qiong, H.E., Liu, T.R., Guo, P.: Multi-dimension and multi-level association rule mining algorithm on data cube. Comput. Appl. 24(3), 85–88 (2004)
Wang, M.L.: Statistic analysis of transformer′s faults and defects at voltage 110 kV and above. Distribution & Utilization (2007)
Sima, W., Sun, P., Wu, J., et al.: Accumulative effect characteristics of transformer oil impregnated paper under repeated lightning impulses. High Volta. Eng. 42(2), 589–597 (2016)
Chen, W.G., Su, X.P., Chen, X., et al.: Influence factor analysis and improvement of the thermal model for predicting transformer top oil temperature. High Volta. Eng. 37(6), 1329–1335 (2011)
Acknowledgments
This paper is supported by “National 863 project (No. 2015AA050204)” and “State Grid science and technology project (No. 520626170011)”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, Y., Geng, Y., Ju, Y., Zhao, X., Yan, D. (2018). Mining Association Rules from Multidimensional Transformer Defect Records. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 849. Springer, Singapore. https://doi.org/10.1007/978-981-13-0896-3_36
Download citation
DOI: https://doi.org/10.1007/978-981-13-0896-3_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0895-6
Online ISBN: 978-981-13-0896-3
eBook Packages: Computer ScienceComputer Science (R0)