skip to main content
10.1145/3483207.3483234acmotherconferencesArticle/Chapter ViewAbstractPublication PagesspmlConference Proceedingsconference-collections
research-article

Research on Correction Rate Prediction for High-Precision Measurement of Electrical Power

Published: 28 October 2021 Publication History

Abstract

Precise measurement is vital in many domains. This depends strongly on instrumentation calibration. In recent years, the calibration of electrical measuring devices is carried out manually. This paper presents the research on using classification models to predict the correction rate for calibration process of electrical power measurement at the national metrology institute of Vietnam. Four classification models are evaluated to find the most appropriate. Obtained results show that this is a promising method for saving time and manpower for calibration.

References

[1]
E. Z. Shapiro, Y. T. Park, N. Budovsky and A. M. Cibbes. 1997. A new power transfer standard, its investigation and intercomparison. in IEEE Transactions on Instrumentation and Measurement, vol. 46, no. 2, pp. 412-415, April 1997.
[2]
I. Budovsky, A. M. Gibbes and D. C. Arthur. 1999. A high-frequency thermal power comparator. in IEEE Transactions on Instrumentation and Measurement, vol. 48, no. 2, pp. 427-430, April 1999.
[3]
G. Ramm, H. Moser and A. Braun. 1999. A new scheme for generating and measuring active, reactive, and apparent power at power frequencies with uncertainties of 2.5/spl times/10/sup -6/. IEEE Transactions on Instrumentation and Measurement, vol. 48, no. 2, pp. 422-426, April 1999.
[4]
U. Pogliano. 2001. Use of integrative analog-to-digital converters for high-precision measurement of electrical power. IEEE Transactions on Instrumentation and Measurement, vol. 50, no. 5, pp. 1315-1318, Oct. 2001.
[5]
I. Budovsky. 2009. Standard of Electrical Power at Frequencies Up to 200 kHz. IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 4, pp. 1010-1016, April 2009.
[6]
R. Bickford, E. Davis, R. Rusaw, and R. Shankar. 2004. Development of an online predictive monitoring system for power generating plants. Proceeding of Instrumentation, Control, and Automation in the Power Industry, Vol. 45, No. 421, pp. 137 – 146, 2004.
[7]
Asli G. Bulutsuz, Kaan Yetilmezsoy, Numan M. Durakbasa. 2015. Application of fuzzy logic methodology for predicting dynamic measurement errors related to process parameters of coordinate measuring machines. J. Intell. Fuzzy Syst. 29(4): 1619-1633.
[8]
Anton V. Milov, Vladislav V. Kukartsev, Vadim S. Tynchenko, Valeriya V. Tynchenko, Oleslav A. Antamoshkin. 2018. Classification of non-normative errors in measuring instruments based on data mining. Proceedings of Aviamechanical engineering and transport (AVENT 2018) https://doi.org/10.2991/avent-18.2018.83
[9]
Fernando Patlan-Cardoso, Suemi Rodríguez-Romo, Oscar Ibáñez-Orozco, Katya Rodríguez-Vázquez, and Francisco Javier Vergara-Martínez. 2021. Estimation of the centralaxis-reference percent depth dose in a water phantom using artificial intelligence, Journal of Radiation Research and Applied Sciences, 14:1, 91-104.
[10]
Budovsky, I. and G. Gubler. 2016. Comparison of two realisations of a power standard. Conference on Precision Electromagnetic Measurements (CPEM 2016), 1-2, 10.1109/CPEM.2016.7540542.
[11]
Christopher M. Bishop. 1995. Neural Networks for Pattern Recognition. Oxford University Press.
[12]
Leo Breiman, Jerome H. Friedman, Richard A. Olshen, and Charles J. Stone. 1984. Classification and Regression Trees. Belmont, USA.
[13]
Roger J. Jang. 1993. ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Trans. on Systems, Man, and Cybernetics, vol. 23(3), 665–685.
[14]
Eric Scheirer and Malcolm Slaney. 1997. Construction and evaluation of a robust multifeature music/speech discriminator. In Proceedings of ICASSP' 97, vol. II, 1331–1334

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
SPML '21: Proceedings of the 2021 4th International Conference on Signal Processing and Machine Learning
August 2021
183 pages
ISBN:9781450390170
DOI:10.1145/3483207
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Classification
  2. Correction rate
  3. Electrical power calibration
  4. Machine Learning

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

SPML 2021

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 38
    Total Downloads
  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 11 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media