Dynamic phasor estimation using adaptive artificial neural network

A. V. Koteswara Rao (), K. M. Soni, Sanjay Kumar Sinha and Ibraheem Nasiruddin
Additional contact information
A. V. Koteswara Rao: Amity University Uttar Pradesh
K. M. Soni: Amity University Uttar Pradesh
Sanjay Kumar Sinha: Amity University Uttar Pradesh
Ibraheem Nasiruddin: Jamia Millia Islamia University

International Journal of System Assurance Engineering and Management, 2021, vol. 12, issue 2, No 8, 310-317

Abstract: Abstract The variation in amplitude and phase of voltage and current signals of a power system under dynamics is often the basis for inaccurate phasor estimation. Further, the frequency estimator derived from estimated phasor is being affected under such dynamic characteristics. This paper presents a method to compute the magnitude and phase angle of the signal during the dynamics using an artificial neural network. First the modeling of signal during dynamics is presented then a multi-layered feed-forward neural network algorithm is developed to estimate the unknown parameters of the model. Subsequently, the magnitude and phase angle and frequency of the signal are calculated using the estimated parameters. The performance of the proposed method is evaluated using standard test signals as per the synchrophasor standard.

Keywords: Dynamic phasor estimation; Feed forward neural network; Phasor measurement unit; Power system dynamics; Rate of change of frequency error; Total vector error (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-021-01082-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:12:y:2021:i:2:d:10.1007_s13198-021-01082-2

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-021-01082-2

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().