This paper explored the possibility of accurately predicting the classification of developing power swings. The notion of machine learning was employed, and tested the application of Decision Tree (DT) algorithms to wide area power system protection schemes. The novelty of the designed Wide Area Protection (WAP) scheme was portrayed by the WAP’s ability to adaptively and accurately predict the classification of developing successive power swings. DTs being a Data Mining (DM) technique, a transient stability analysis was performed on an IEEE 39 bus test system in Dig SILENT®. The learning sample from the Phasor Measurement Unit (PMU) data was organized and stored in a data base in Microsoft Excel® 2010. The CART analysis and DT model design was done using Salford Predictive Modeller-CART® v6, trial licence. The results of this investigation were quite accurate and gave DT algorithms ‘thumbs-up’ in terms of classification prediction.
| Published in |
International Journal of Energy and Power Engineering (Volume 4, Issue 2-1)
This article belongs to the Special Issue Electrical Power Systems Operation and Planning |
| DOI | 10.11648/j.ijepe.s.2015040201.16 |
| Page(s) | 63-72 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2014. Published by Science Publishing Group |
Decision Trees, Power Swing, Out-of-Step, Wide Area Protection
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APA Style
V. Siyoi, S. Kariuki, M. J. Saulo. (2014). Power Swing Prediction for Out-of-Step Mitigation. International Journal of Energy and Power Engineering, 4(2-1), 63-72. https://doi.org/10.11648/j.ijepe.s.2015040201.16
ACS Style
V. Siyoi; S. Kariuki; M. J. Saulo. Power Swing Prediction for Out-of-Step Mitigation. Int. J. Energy Power Eng. 2014, 4(2-1), 63-72. doi: 10.11648/j.ijepe.s.2015040201.16
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Download@article{10.11648/j.ijepe.s.2015040201.16,
author = {V. Siyoi and S. Kariuki and M. J. Saulo},
title = {Power Swing Prediction for Out-of-Step Mitigation},
journal = {International Journal of Energy and Power Engineering},
volume = {4},
number = {2-1},
pages = {63-72},
doi = {10.11648/j.ijepe.s.2015040201.16},
url = {https://doi.org/10.11648/j.ijepe.s.2015040201.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.s.2015040201.16},
abstract = {This paper explored the possibility of accurately predicting the classification of developing power swings. The notion of machine learning was employed, and tested the application of Decision Tree (DT) algorithms to wide area power system protection schemes. The novelty of the designed Wide Area Protection (WAP) scheme was portrayed by the WAP’s ability to adaptively and accurately predict the classification of developing successive power swings. DTs being a Data Mining (DM) technique, a transient stability analysis was performed on an IEEE 39 bus test system in Dig SILENT®. The learning sample from the Phasor Measurement Unit (PMU) data was organized and stored in a data base in Microsoft Excel® 2010. The CART analysis and DT model design was done using Salford Predictive Modeller-CART® v6, trial licence. The results of this investigation were quite accurate and gave DT algorithms ‘thumbs-up’ in terms of classification prediction.},
year = {2014}
}
TY - JOUR T1 - Power Swing Prediction for Out-of-Step Mitigation AU - V. Siyoi AU - S. Kariuki AU - M. J. Saulo Y1 - 2014/12/27 PY - 2014 N1 - https://doi.org/10.11648/j.ijepe.s.2015040201.16 DO - 10.11648/j.ijepe.s.2015040201.16 T2 - International Journal of Energy and Power Engineering JF - International Journal of Energy and Power Engineering JO - International Journal of Energy and Power Engineering SP - 63 EP - 72 PB - Science Publishing Group SN - 2326-960X UR - https://doi.org/10.11648/j.ijepe.s.2015040201.16 AB - This paper explored the possibility of accurately predicting the classification of developing power swings. The notion of machine learning was employed, and tested the application of Decision Tree (DT) algorithms to wide area power system protection schemes. The novelty of the designed Wide Area Protection (WAP) scheme was portrayed by the WAP’s ability to adaptively and accurately predict the classification of developing successive power swings. DTs being a Data Mining (DM) technique, a transient stability analysis was performed on an IEEE 39 bus test system in Dig SILENT®. The learning sample from the Phasor Measurement Unit (PMU) data was organized and stored in a data base in Microsoft Excel® 2010. The CART analysis and DT model design was done using Salford Predictive Modeller-CART® v6, trial licence. The results of this investigation were quite accurate and gave DT algorithms ‘thumbs-up’ in terms of classification prediction. VL - 4 IS - 2-1 ER -
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