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Linking damping of electromechanical oscillations to system operating conditions using neural networks

Sulla, Francesco LU ; Måsbäck, Emil and Samuelsson, Olof LU (2015) 2014 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014
Abstract

This paper presents the application of Neural Networks to link the damping of electromechanical oscillations in the Nordic power system to the measured operating conditions. Different neural network topologies have already been presented in the literature for this application, but using exclusively data from simulations. The primary objective of the paper is to analyze how these topologies behave with data from a real power system. The damping of the 0.35 Hz electromechanical oscillation has been first estimated from a large amount of Phasor Measurements Units (PMU) measurements for a two years period. Three neural network models are trained with power system variables as generation, load and power flows over cross-border lines measured... (More)

This paper presents the application of Neural Networks to link the damping of electromechanical oscillations in the Nordic power system to the measured operating conditions. Different neural network topologies have already been presented in the literature for this application, but using exclusively data from simulations. The primary objective of the paper is to analyze how these topologies behave with data from a real power system. The damping of the 0.35 Hz electromechanical oscillation has been first estimated from a large amount of Phasor Measurements Units (PMU) measurements for a two years period. Three neural network models are trained with power system variables as generation, load and power flows over cross-border lines measured during year 2010, used as input, and the estimated damping from PMU measurements during the same year, used as target. The neural network models are then tested with the data from 2011 with the aim of estimating the damping. The results indicate that neural networks can correctly predict more than 80% of the operating conditions resulting in low damping during the entire year 2011. The presented method is purely measurement-based and it can be used in conjunction with other traditional model-based planning methods to predict oscillatory stability limits.

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author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
damping, neural networks, PMU measurements, power system oscillations
host publication
IEEE PES Innovative Smart Grid Technologies, Europe
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2014 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014
conference location
Istanbul, Turkey
conference dates
2014-10-12 - 2014-10-15
external identifiers
  • scopus:84936976771
ISBN
978-1-4799-7720-8
DOI
10.1109/ISGTEurope.2014.7028818
language
English
LU publication?
yes
id
9c6514d7-1638-47de-9b71-9f654f25d98a
date added to LUP
2019-06-20 17:12:12
date last changed
2020-05-10 06:32:37
@inproceedings{9c6514d7-1638-47de-9b71-9f654f25d98a,
  abstract     = {<p>This paper presents the application of Neural Networks to link the damping of electromechanical oscillations in the Nordic power system to the measured operating conditions. Different neural network topologies have already been presented in the literature for this application, but using exclusively data from simulations. The primary objective of the paper is to analyze how these topologies behave with data from a real power system. The damping of the 0.35 Hz electromechanical oscillation has been first estimated from a large amount of Phasor Measurements Units (PMU) measurements for a two years period. Three neural network models are trained with power system variables as generation, load and power flows over cross-border lines measured during year 2010, used as input, and the estimated damping from PMU measurements during the same year, used as target. The neural network models are then tested with the data from 2011 with the aim of estimating the damping. The results indicate that neural networks can correctly predict more than 80% of the operating conditions resulting in low damping during the entire year 2011. The presented method is purely measurement-based and it can be used in conjunction with other traditional model-based planning methods to predict oscillatory stability limits.</p>},
  author       = {Sulla, Francesco and Måsbäck, Emil and Samuelsson, Olof},
  booktitle    = {IEEE PES Innovative Smart Grid Technologies, Europe},
  isbn         = {978-1-4799-7720-8},
  language     = {eng},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  title        = {Linking damping of electromechanical oscillations to system operating conditions using neural networks},
  url          = {http://dx.doi.org/10.1109/ISGTEurope.2014.7028818},
  doi          = {10.1109/ISGTEurope.2014.7028818},
  year         = {2015},
}