Using Cluster Analysis in the Synthesis of Electrical Equipment Diagnostic Models

Gnutova, Ksenia; Eltyshev, Denis

doi:10.13142/KT10005.42

Proceedings of International Conference on Applied Innovation in IT · 2017/03/16 · Vol. 5 · Issue 1 · pp. 119–124

Using Cluster Analysis in the Synthesis of Electrical Equipment Diagnostic Models

Ksenia Gnutova, Denis Eltyshev

📄 Download PDF DOI: 10.13142/KT10005.42

Abstract

The article investigates the issue of improving the methods of diagnostics of electrical equipment conditions to ensure the effective assessment of equipment needs for repairs and its trouble-free, safe and economical operation. The possibility of taking advantage of different cluster analysis methods enables us to form the structure of fuzzy models of electrical equipment diagnostics. The method of synthesis of this class of models takes into account various ways to implement clustering algorithms and criteria for assessing its effectiveness. The software, which we use to study the applicability of methods for the analysis of data on temperature parameters data of transformer equipment, utilises methods such as k-means and fuzzy cmeans.

Keywords

Electrical equipment Cluster analysis Quality criteria Fuzzy logic Diagnostics

References

Solodyankin A.A., Kolesnikov A.A., Grisha B.G., Nazarov A.A., Yanovich I.M. (2015), ‘Modern methods of diagnostics and technical state assessment of the high-voltage electric power equipment’, Journal of Science and Technology, 2, pp. 70-72.
Kychkin A.V., Mikriukov G.P. (2016), ‘Applied data analysis in energy monitoring system’, Journal of Problems regional energy, 2(31), pp. 84-92.
Semenov V.V. (2004). Diagnosis and monitoring of highvoltage oil-filled electrical equipment. Ufa. Ufa State Aviation Technical University.
Elizarov S.I., Bargesyan A.A., Kupriyanov M.S., Holod I.I., Tess M.D. (2009). Analysis of the data and processes. 3rd edition, Saint-Petersburg: BHVPetersburg.
Khoroshev N.I., Pogorazdov R.N. (2016), ‘Adaptive Clustering Method in Intelligent Automated Decision Support Systems’, Proceedings of the 19th International Conference on Soft Computing and Measurements. SCM`2016, pp. 296-298.
Eltyshev, D.K., Boyarshinova, V.V. (2016), ‘Intelligent Decision Support in the Electrical Equipment Diagnostics’, Proceedings of the 19th International Conference on Soft Computing and Measurements. SCM 2016, pp. 157-160.
Petrochenkov A.B. (2015), ‘Management of Effective Maintenance of the Electrotechnical Complexes of Mineral Resource Industry's Enterprises Based on Energy-information Model’, Proceedings of International Conference on Soft Computing and Measurements. SCM 2015, pp. 122-124.
Shtovba S.D. (2007). Design of fuzzy systems tools MATLAB // S.D. Shtovba., Moscow: Telecom. Tosei Hatori, Mika Sato-Ilic. (2014), ‘A fuzzy clustering method using the relative structure of the belongingness of objects to clusters’, Journal of Procedia Computer Science, 35, pp. 994 – 1002.