Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring. / Anagnostou, George; Boem, Francesca; Kuenzel, Stefanie; Pal, Bikash; Parisini, Thomas.

In: IEEE Transactions on Power Systems , Vol. 33, No. 4, 07.2018, p. 4228-4237.

Research output: Contribution to journalArticlepeer-review

Published

Standard

Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring. / Anagnostou, George; Boem, Francesca; Kuenzel, Stefanie; Pal, Bikash; Parisini, Thomas.

In: IEEE Transactions on Power Systems , Vol. 33, No. 4, 07.2018, p. 4228-4237.

Research output: Contribution to journalArticlepeer-review

Harvard

Anagnostou, G, Boem, F, Kuenzel, S, Pal, B & Parisini, T 2018, 'Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring', IEEE Transactions on Power Systems , vol. 33, no. 4, pp. 4228-4237. https://doi.org/10.1109/TPWRS.2017.2771278

APA

Anagnostou, G., Boem, F., Kuenzel, S., Pal, B., & Parisini, T. (2018). Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring. IEEE Transactions on Power Systems , 33(4), 4228-4237. https://doi.org/10.1109/TPWRS.2017.2771278

Vancouver

Anagnostou G, Boem F, Kuenzel S, Pal B, Parisini T. Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring. IEEE Transactions on Power Systems . 2018 Jul;33(4):4228-4237. https://doi.org/10.1109/TPWRS.2017.2771278

Author

Anagnostou, George ; Boem, Francesca ; Kuenzel, Stefanie ; Pal, Bikash ; Parisini, Thomas. / Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring. In: IEEE Transactions on Power Systems . 2018 ; Vol. 33, No. 4. pp. 4228-4237.

BibTeX

@article{8bba5ba4d11146899823b1da55ec9b36,
title = "Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring",
abstract = "This paper proposes a rigorous anomaly detection scheme, developed to spot power system operational changes which are inconsistent with the models used by operators. This novel technique relies on a state observer, with guaranteed estimation error convergence, suitable to be implemented in real time, and it has been developed to fully address this important issue in power systems. The proposed method is fitted to the highly nonlinear characteristics of the network, with the states of the nonlinear generator model being estimated by means of a linear time-varying estimation scheme. Given the reliance of the existing dynamic security assessment tools in industry on nominal power system models, the suggested methodology addresses cases when there is deviation from assumed system dynamics, enhancing operators{\textquoteright} awareness of system operation. It is based on a decision scheme relying on analytical computation of thresholds, not involving empirical criteria which are likely to introduce inaccurate outcomes. Since false-alarms are guaranteed to be absent, the proposed technique turns out to be very useful for system monitoring and control. The effectiveness of the anomaly detection algorithm is shown through detailed realistic case studies in two power system models.",
keywords = "fault detection , synchronous generators, power system monitoring , power system dynamics, observers",
author = "George Anagnostou and Francesca Boem and Stefanie Kuenzel and Bikash Pal and Thomas Parisini",
year = "2018",
month = jul,
doi = "10.1109/TPWRS.2017.2771278",
language = "English",
volume = "33",
pages = "4228--4237",
journal = "IEEE Transactions on Power Systems ",
issn = "0885-8950",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

RIS

TY - JOUR

T1 - Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring

AU - Anagnostou, George

AU - Boem, Francesca

AU - Kuenzel, Stefanie

AU - Pal, Bikash

AU - Parisini, Thomas

PY - 2018/7

Y1 - 2018/7

N2 - This paper proposes a rigorous anomaly detection scheme, developed to spot power system operational changes which are inconsistent with the models used by operators. This novel technique relies on a state observer, with guaranteed estimation error convergence, suitable to be implemented in real time, and it has been developed to fully address this important issue in power systems. The proposed method is fitted to the highly nonlinear characteristics of the network, with the states of the nonlinear generator model being estimated by means of a linear time-varying estimation scheme. Given the reliance of the existing dynamic security assessment tools in industry on nominal power system models, the suggested methodology addresses cases when there is deviation from assumed system dynamics, enhancing operators’ awareness of system operation. It is based on a decision scheme relying on analytical computation of thresholds, not involving empirical criteria which are likely to introduce inaccurate outcomes. Since false-alarms are guaranteed to be absent, the proposed technique turns out to be very useful for system monitoring and control. The effectiveness of the anomaly detection algorithm is shown through detailed realistic case studies in two power system models.

AB - This paper proposes a rigorous anomaly detection scheme, developed to spot power system operational changes which are inconsistent with the models used by operators. This novel technique relies on a state observer, with guaranteed estimation error convergence, suitable to be implemented in real time, and it has been developed to fully address this important issue in power systems. The proposed method is fitted to the highly nonlinear characteristics of the network, with the states of the nonlinear generator model being estimated by means of a linear time-varying estimation scheme. Given the reliance of the existing dynamic security assessment tools in industry on nominal power system models, the suggested methodology addresses cases when there is deviation from assumed system dynamics, enhancing operators’ awareness of system operation. It is based on a decision scheme relying on analytical computation of thresholds, not involving empirical criteria which are likely to introduce inaccurate outcomes. Since false-alarms are guaranteed to be absent, the proposed technique turns out to be very useful for system monitoring and control. The effectiveness of the anomaly detection algorithm is shown through detailed realistic case studies in two power system models.

KW - fault detection

KW - synchronous generators

KW - power system monitoring

KW - power system dynamics

KW - observers

U2 - 10.1109/TPWRS.2017.2771278

DO - 10.1109/TPWRS.2017.2771278

M3 - Article

VL - 33

SP - 4228

EP - 4237

JO - IEEE Transactions on Power Systems

JF - IEEE Transactions on Power Systems

SN - 0885-8950

IS - 4

ER -