research-article

An Enhanced Prony Algorithm for On-line Detection of Small Signal Oscillations for Synchrophasor Application

Authors:

Javed M. Borbora,

Syed Rounak NisharAuthors Info & Claims

ICECC '20: Proceedings of the 3rd International Conference on Electronics, Communications and Control Engineering

Pages 36 - 40

https://doi.org/10.1145/3396730.3396742

Published: 29 May 2020 Publication History

Abstract

This paper presents an online detection method of small signal oscillations using an enhanced Prony algorithm. The proposed method has considered the affect of missing measure-ments of phasor measurement units (PMUs) which occurs as a result of network congestion or defect in PMUs or phasor data concentrators (PDCs). In this context, at first, a sequential K nearest neighbours (SKNN) classifier is utilized to provide a robust data set to address such issue. In the second step, improved Prony algorithm is used to identify the oscillatory modes. The proposed approach has been compared to Matrix Pencil, Eigen Realization algorithm (ERA) and improved Prony for generated test signals with missing data at different noise levels. The suitability of the proposed monitoring scheme is further demonstrated on two area network and real PMU measurements derived from the Western Electricity Coordinating Council (WECC).

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Cited By

Sahoo MRai S(2024)A partitioning around medoids (PAM) based sequential clustering approach for model order estimation of low-frequency oscillations in wide area measurement systemSādhanā10.1007/s12046-023-02408-549:1Online publication date: 2-Mar-2024
https://doi.org/10.1007/s12046-023-02408-5

Index Terms

An Enhanced Prony Algorithm for On-line Detection of Small Signal Oscillations for Synchrophasor Application
1. General and reference
  1. Cross-computing tools and techniques
    1. Estimation

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ICECC '20: Proceedings of the 3rd International Conference on Electronics, Communications and Control Engineering

April 2020

73 pages

ISBN:9781450374996

DOI:10.1145/3396730

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 29 May 2020

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ICECC 2020

ICECC 2020: The 3rd International Conference on Electronics, Communications and Control Engineering

April 8 - 10, 2020

Bali, Indonesia

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Cited By

Sahoo MRai S(2024)A partitioning around medoids (PAM) based sequential clustering approach for model order estimation of low-frequency oscillations in wide area measurement systemSādhanā10.1007/s12046-023-02408-549:1Online publication date: 2-Mar-2024
https://doi.org/10.1007/s12046-023-02408-5

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