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Self-tuning Kalman filter and machine learning algorithms for voltage dips upstream or downstream origin detection

H. Shadmehr, R. Chiumeo, L. Tenti

2016/5/20

Abstract

In this paper, self-tuning Kalman Filter (KF) is applied to a significant sample of full waveforms associated to the voltage dips monitored in the Italian distribution network by the QuEEN system, with the aim of events detection and waveforms segmentation. Segmentation is done in order to extract more features and information from the original voltage waveforms, to make easier voltage dips classification, based on the events source location (upstream/downstream from the point of measurement). The aforementioned classification is achieved by Machine Learning algorithms. The evaluation of the obtained results is based on the computation of a “confusion matrix”.

Published in: Renewable Energy & Power Quality Journal (RE&PQJ, Nº. 14)
Pages:409-413 Date of Publication: 2016/5/20
ISSN: 2172-038X Date of Current Version:2016/05/04
REF:346-16 Issue Date: May 2016
DOI:10.24084/repqj14.346 Publisher: EA4EPQ

Authors and affiliations

H. Shadmehr, R. Chiumeo, L. Tenti
Ricerca sul sistema energetico RSE SpA Milano. Italy

Key words

Self-tuning Kalman Filter, Machine Learning, voltage dips, waveform segmentation.

References

[1] CIGRE/CIRED JWG C4.112, “Guidelines For Power Quality Monitoring – Measurement Locations, Processing And Presentation Of Data”, TB, October 2014.
[2] V. Barrera, “Automatic Diagnosis of Voltage Disturbances in Power Distribution Networks”, PhD Thesis, Universitat de Girona, Spain, Feb-2012.
[3] E. Styvaktakis, “Automating Power Quality Analysis”, PhD Thesis, Chalmers University of Technology, Sweden, 2002.
[4] R. Chiumeo, L. Garbero, F. Malegori, L. Tenti, “Feasible methods to evaluate voltage dips origin”, ISN 2172-038X Renewable Energy & Power Quality Journal, No.13, March 2015, Paper 325, 1-5.
[5] C. D. Le, I. Y. H. Gu, M. Bollen, “Joint Causal and Anti-Causal Segmentation and Location of Transitions in Power Disturbances”, in IEEE PES General Meeting, 2010, pp.1-6.
[6] R. Chiumeo, A. Porrino, L. Garbero, L. Tenti, M. de Nigris, “The Italian Power Quality Monitoring System Of The MV Network: Results Of The Measurements Of Voltage Dips After 3 Years Campaign”, 20th International Conference on Electricity Distribution, CIRED 2009, Prague, paper 0737l
[7] I. Moreno, A. Castro, I. GU. M. Bollen, “Test and Analysis of a Novel Segmentation Method Using Measurement Data”, 23th International Conference on Electricity Distribution, CIRED 2015, Lyon, 15-18June-2015, paper 0836.
[8] S. Ortiz L, H. Torres S, V. Barrera, C. Duarte, G. Ordònez, S. Herraiz, “Analysis of the Voltage Event Segmentation Using Kalman Filter and Wavelet Transform”, IEEE Andean Conference, Exhibition and Industry Forum, Colombia, 15-17Sep-2010.
[9] M. Bollen, I. Y. H. Gu, “Signal Processing of Power Quality Disturbances”, John Wiley & Sons, INC., 2006.

 
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