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

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Linking damping of electromechanical oscillations to system operating conditions using neural networks. / Sulla, Francesco; Måsbäck, Emil; Samuelsson, Olof.

IEEE PES Innovative Smart Grid Technologies, Europe. IEEE - Institute of Electrical and Electronics Engineers Inc., 2015.

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceeding

Harvard

Sulla, F, Måsbäck, E & Samuelsson, O 2015, Linking damping of electromechanical oscillations to system operating conditions using neural networks. i IEEE PES Innovative Smart Grid Technologies, Europe. IEEE - Institute of Electrical and Electronics Engineers Inc., 2014 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014, Istanbul, Turkiet, 2014/10/12. https://doi.org/10.1109/ISGTEurope.2014.7028818

APA

Sulla, F., Måsbäck, E., & Samuelsson, O. (2015). Linking damping of electromechanical oscillations to system operating conditions using neural networks. I IEEE PES Innovative Smart Grid Technologies, Europe IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISGTEurope.2014.7028818

CBE

Sulla F, Måsbäck E, Samuelsson O. 2015. Linking damping of electromechanical oscillations to system operating conditions using neural networks. I IEEE PES Innovative Smart Grid Technologies, Europe. IEEE - Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ISGTEurope.2014.7028818

MLA

Sulla, Francesco, Emil Måsbäck, och Olof Samuelsson "Linking damping of electromechanical oscillations to system operating conditions using neural networks". IEEE PES Innovative Smart Grid Technologies, Europe. IEEE - Institute of Electrical and Electronics Engineers Inc. 2015. https://doi.org/10.1109/ISGTEurope.2014.7028818

Vancouver

Sulla F, Måsbäck E, Samuelsson O. Linking damping of electromechanical oscillations to system operating conditions using neural networks. I IEEE PES Innovative Smart Grid Technologies, Europe. IEEE - Institute of Electrical and Electronics Engineers Inc. 2015 https://doi.org/10.1109/ISGTEurope.2014.7028818

Author

Sulla, Francesco ; Måsbäck, Emil ; Samuelsson, Olof. / Linking damping of electromechanical oscillations to system operating conditions using neural networks. IEEE PES Innovative Smart Grid Technologies, Europe. IEEE - Institute of Electrical and Electronics Engineers Inc., 2015.

RIS

TY - GEN

T1 - Linking damping of electromechanical oscillations to system operating conditions using neural networks

AU - Sulla, Francesco

AU - Måsbäck, Emil

AU - Samuelsson, Olof

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - damping

KW - neural networks

KW - PMU measurements

KW - power system oscillations

U2 - 10.1109/ISGTEurope.2014.7028818

DO - 10.1109/ISGTEurope.2014.7028818

M3 - Paper in conference proceeding

BT - IEEE PES Innovative Smart Grid Technologies, Europe

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014

Y2 - 12 October 2014 through 15 October 2014

ER -