Influence of Fuzzy Clustering on the Accuracy of Electrical Equipment Diagnostic Models

Eltyshev, Denis; Gnutova, Ksenia

doi:10.13142/kt10006.21

Proceedings of International Conference on Applied Innovation in IT · 2018/03/13 · Vol. 6 · Issue 1 · pp. 23–28

Influence of Fuzzy Clustering on the Accuracy of Electrical Equipment Diagnostic Models

Denis Eltyshev, Ksenia Gnutova

📄 Download PDF DOI: 10.13142/kt10006.21

Abstract

The development of electric power industry is oriented on high reliability, flexibility and efficiency of managing power grids of arbitrary configuration. For such grids an information infrastructure is required. It should consist of various software and hardware, including systems of electrical equipment monitoring and diagnostics for accumulating information about its parameters with controlling and managing its condition. As a rule, data about the electrical equipment is heterogeneous. Thus there is a necessity of certain mechanisms for data processing to provide a possibility of constructing diagnostic models in an automated mode and adapting them to power grid operating conditions. The aim of this work is to develop a mechanism for automated calculation of the electrical equipment diagnostic models parameters. It supposes using historical data analyzing algorithms that ensure high reliability of the diagnosis. Base on this the application of fuzzy clustering for constructing membership functions to assess the features of equipment condition change in fuzzy diagnostic models was considered. Different fuzzy clustering algorithms were analyzed, and a technique for processing data on the equipment operation with constructing membership functions based on fuzzy partition matrix and clusters centres was proposed. The technique allows to approximate the membership functions by typical curves and to choose the most effective variant of clustering in terms of electrical equipment diagnostic reliability. The testing of fuzzy models for assessing the condition of power transformer equipment using clustering results was performed. A high level of compliance of simulation data with the conclusions of specialized organizations performing monitoring of equipment in power supply systems for oil production facilities was obtained. The practical relevance of the results is in using the technique in the synthesis of intelligent expert-diagnostic systems for increasing the electrical equipment diagnostic reliability and reducing the duration of its unplanned downtime.

Keywords

Diagnostic Electrical Equipment Fuzzy Clustering Fuzzy logic Membership function

References

A.V. Kychkin, G.P. Mikriukov, “Applied data analysis in energy monitoring system,” Journal of Problems regional energy, vol. 2(31), pp. 84-92, 2016.
A.V. Kychkin, “Synthesizing A System For Remote Energy Monitoring In Manufacturing,” Metallurgist, vol. 59, № 9-10, pp. 752-760, 2016.
A.B. Petrochenkov, T. Frank, A.V. Romodin, A.V. Kychkin, “Hardware-in-the-loop Simulation of an Active-adaptive Power Grid,” Russian Electrical Engineering, vol. 84, № 11, pp. 652-658, 2013.
N.I. Khoroshev, V.P. Kazantsev, “Management Support of Electroengineering Equipment Servicing Based on the Actual Technical Condition,” Automation and Remote Control, vol. 76, № 6, pp. 1058-1069, 2015.
A.B. Petrochenkov, S.V. Bochkarev, A.V. Romodin, D.K. Eltyshev, “The planning operation process of electrotechnical equipment using the Markov process theory,” Russian Electrical Engineering, vol. 82, № 11, pp. 592–596, 2011.
D.K. Eltyshev, V.V. Boyarshinova, “Intelligent Decision Support in the Electrical Equipment Diagnostics,” Proceedings of the 19th International Conference on Soft Computing and Measurements, SCM 2016, pp. 157-160, 2016.
D.K. Eltyshev, N.I. Khoroshev, “Diagnostics of the Power Oil-filled Transformer Equipment of Thermal Power Plants,” Thermal Engineering, vol. 63, № 8, pp. 558-566, 2016.
N.I. Khoroshev, R.N. Pogorazdov, “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, 2016.
K.A. Gnutova, D.K. Eltyshev, “Using Cluster Analysis in the Synthesis of Electrical Equipment Diagnostic Models,” Proceedings of the 5th International Conference on Applied Innovations in IT 2017, pp. 119-124, 2017.
D.K. Eltyshev, K.A. Gnutova, “Identification of the parameters of electrical equipment condition assessment models using fuzzy clustering,” Proceedings of the 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017, pp. 142-144, 2017.
J.C. Bezdek, R. Ehrlich., W Full, “FCM: The fuzzy c-means clustering algorithm,” Computers & Geosciences, vol. 10, № 2–3, pp. 191-203, 1984,
R. Babuska, P.J. van der Veen, U. Kaymak, “Improved covariance estimation for Gustafson-Kessel clustering,” Proceedings of the 2002 IEEE international conference on fuzzy systems, Honolulu: Technische Universitet Eindhoven, 2002, pp. 1081-1085.
I. Gath, A.B. Geva, “Unsupervised optimal fuzzy clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, vol. 11, № 7, pp.773-781.
S.D. Shtovba, O.D. Pankevich, A.V. Nagorna, “Analyzing the Criteria for Fuzzy Classifier Learning,” Automatic Control and Computer Sciences. 2015, vol. 49. № 3, pp. 123–132, doi:10.3103/S0146411615030098.