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

2019. Paper presented at IEEE PES General Meeting , Atlanta, United States.

Research output: Contribution to conferencePaperpeer-review

Published

Standard

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

2019. Paper presented at IEEE PES General Meeting , Atlanta, United States.

Research output: Contribution to conferencePaperpeer-review

Harvard

Anagnostou, G, Boem, F, Kuenzel, S, Pal, B & Parisini, T 2019, 'Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring', Paper presented at IEEE PES General Meeting , Atlanta, United States, 4/08/19 - 8/08/19.

APA

Anagnostou, G., Boem, F., Kuenzel, S., Pal, B., & Parisini, T. (2019). Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring. Paper presented at IEEE PES General Meeting , Atlanta, United States.

Vancouver

Anagnostou G, Boem F, Kuenzel S, Pal B, Parisini T. Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring. 2019. Paper presented at IEEE PES General Meeting , Atlanta, United States.

Author

Anagnostou, George ; Boem, Francesca ; Kuenzel, Stefanie ; Pal, Bikash ; Parisini, Thomas. / Observer-Based Anomaly Detection of Synchronous Generators for Power Systems Monitoring. Paper presented at IEEE PES General Meeting , Atlanta, United States.

BibTeX

@conference{7955e797b8574d7ebd2a27cfde3b8403,
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.",
author = "George Anagnostou and Francesca Boem and Stefanie Kuenzel and Bikash Pal and Thomas Parisini",
year = "2019",
month = aug,
day = "7",
language = "English",
note = "IEEE PES General Meeting ; Conference date: 04-08-2019 Through 08-08-2019",
url = "https://pes-gm.org/2019/",

}

RIS

TY - CONF

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 - 2019/8/7

Y1 - 2019/8/7

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.

M3 - Paper

T2 - IEEE PES General Meeting

Y2 - 4 August 2019 through 8 August 2019

ER -