<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper presents an adaptive transmission line fault location method, which incorporates fault location devices at both line ends and utilises data import from a supervisory control and data acquisition system without strictly requiring data synchronisation. The developed method aims at achieving a higher degree of robustness, adaptiveness and accuracy. The adaptiveness is achieved by dynamic updating of mathematical models used on the basis of network-wide information, such as data on the state of circuit-breakers and apparent power at load and generation nodes. The robustness and accuracy are enhanced by incorporating two stages of identification of model parameters with the goal of reducing the decision variable space for the stage identifying fault parameters. Furthermore, in addition to utilisation of all measurements available at a particular substation, the developed method partially employs the measurements from the other end of the line by means of result cross-checks, but does not require a full data set, unlike deterministic-model-based methods. An optimisation-based approach, redundancy on the basis of extended measurement set, and cross-checks reduce the risk of fault location errors due to measurement errors or “voids” in the data available. Testing of the developed method demonstrates its accuracy and robustness in a wide range of pre-fault and fault regime scenarios, even when considering various pre-fault contingencies.</p>
</abstract>

This paper presents an adaptive transmission line fault location method, which incorporates fault location devices at both line ends and utilises data import from a supervisory control and data acquisition system without strictly requiring data synchronisation. The developed method aims at achieving a higher degree of robustness, adaptiveness and accuracy. The adaptiveness is achieved by dynamic updating of mathematical models used on the basis of network-wide information, such as data on the state of circuit-breakers and apparent power at load and generation nodes. The robustness and accuracy are enhanced by incorporating two stages of identification of model parameters with the goal of reducing the decision variable space for the stage identifying fault parameters. Furthermore, in addition to utilisation of all measurements available at a particular substation, the developed method partially employs the measurements from the other end of the line by means of result cross-checks, but does not require a full data set, unlike deterministic-model-based methods. An optimisation-based approach, redundancy on the basis of extended measurement set, and cross-checks reduce the risk of fault location errors due to measurement errors or “voids” in the data available. Testing of the developed method demonstrates its accuracy and robustness in a wide range of pre-fault and fault regime scenarios, even when considering various pre-fault contingencies.

Adaptive Fault Locator for High-Voltage Transmission Lines Based on the Estimation of Power System Model Parameters

Latvian Journal of Physics and Technical Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper presents an adaptive transmission line fault location method, which incorporates fault location devices at both line ends and utilises data import...

Adaptive Fault Locator for High-Voltage Transmission Lines Based on the Estimation of Power System Model Parameters

Published Online: Aug 08, 2023

Page range: 3 - 20

DOI: https://doi.org/10.2478/lpts-2023-0021

Keywords
Fault location, modelling, optimisation, transmission lines

© 2023 I. Zalitis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Adaptive Fault Locator for High-Voltage Transmission Lines Based on the Estimation of Power System Model Parameters

Published Online: Aug 08, 2023

Page range: 3 - 20

DOI: https://doi.org/10.2478/lpts-2023-0021

KeywordsFault location, modelling, optimisation, transmission lines

© 2023 I. Zalitis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Fault location, modelling, optimisation, transmission lines