Probabilistic load curtailment estimation using posterior probability model and twin support vector machine View Full Text


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Article Info

DATE

2019-03-21

AUTHORS

Rozhin Eskandarpour, Amin Khodaei

ABSTRACT

Estimating the potential load curtailments as a result of hurricane is of great significance in improving the emergency response and recovery of power grid. This paper proposes a three-step sequential method in identifying such load curtailments prior to hurricane. In the first step, a twin support vector machine (TWSVM) model is trained on path/intensity information of previous hurricanes to enable a deterministic outage state assessment of the grid components in response to upcoming events. The TWSVM model is specifically used as it is suitable for handling imbalanced datasets. In the second step, a posterior probability sigmoid model is trained on the obtained results to convert the deterministic results into probabilistic outage states. These outage states enable the formation of probability-weighted contingency scenarios. Finally, the obtained component outages are integrated into a load curtailment estimation model to determine the expected potential load curtailments in the grid. The simulation results, tested on the standard IEEE 118-bus system and based on synthetic datasets, illustrate the high accuracy performance of the proposed method. More... »

PAGES

665-675

References to SciGraph publications

  • 2012-03-13. An overview on twin support vector machines in ARTIFICIAL INTELLIGENCE REVIEW
  • 2009-05-27. TWin support tensor machines for MCs detection in JOURNAL OF ELECTRONICS (CHINA)
  • 2009. Several SVM Ensemble Methods Integrated with Under-Sampling for Imbalanced Data Learning in ADVANCED DATA MINING AND APPLICATIONS
  • 2006. z-SVM: An SVM for Improved Classification of Imbalanced Data in AI 2006: ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 2001-09-20. Machine Learning Applications to Power Systems in MACHINE LEARNING AND ITS APPLICATIONS
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    http://scigraph.springernature.com/pub.10.1007/s40565-019-0514-9

    DOI

    http://dx.doi.org/10.1007/s40565-019-0514-9

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