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Using an Input Data Segregation Algorithm to Minimise the Error of the Fuzzy Controller in the Metrological Correction System of Electric Energy Meters

  • Bartosz DominikowskiEmail author
  • Krzysztof Pacholski
  • Piotr Woźniak
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 548)

Abstract

The authors of this paper presented the possibility of using a fuzzy controller in the conversion factor correction system associated with the energy meter’s current channel. The accuracy of non-adaptive fuzzy controllers is significantly affected by the relevant expert knowledge in the form of rules stored in the database. In order to increase fuzzy controller accuracy, the k-means clustering method was used to group the input data of the controller (peak value of the output signal of the energy meter’s current transducer and its derivative). This analysis can be conducted to extract central points that represent particular input data groups. Based on computer testing of fuzzy controller output signals performed by the authors, the assignment of membership functions to the central points of the input data groups should be done by the expert at the beginning while designing the rules. Additionally, this paper presents the possibilities of tuning the fuzzy controller by changing its parameters.

Keywords

K-means clustering Fuzzy controller Gain corrector 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bartosz Dominikowski
    • 1
    Email author
  • Krzysztof Pacholski
    • 1
  • Piotr Woźniak
    • 1
  1. 1.Lodz University of TechnologyLodzPoland

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