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An AdaBoost-based tree augmented naive Bayesian classifier for transient stability assessment of power systems

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  • Huimin Wang
  • Zhaojun Steven Li

Abstract

By focusing on the accuracy limitations of the naive Bayesian classifier in the transient stability assessment of power systems, a tree augmented naive Bayesian (TAN) classifier is adopted for the power system transient stability assessment. The adaptive Boosting (AdaBoost) algorithm is used in the TAN classifier to form an AdaBoost-based tree augmented naive Bayesian (ATAN) classifier for further classification performance improvement. To construct the ATAN classifier, eight attributes that reasonably reflect the transient stability or transient instability of a power system are selected as inputs of the proposed classifier. In addition, the class-attribute interdependence maximization (CAIM) algorithm is used to discretize the attributes. Then, the operating mechanism of the power system is used to obtain the dependencies between the attributes, and the parameters of the ATAN classifier are learned according to the Bayes’ theorem and the criterion of maximizing a posterior estimation. Four evaluation indicators of the ATAN classifier are used, that is, the value of Kappa, the area under the receiver operating characteristic curve (AUC), F 1 score, and the average evaluation indicator. Lastly, experiments are implemented on the IEEE 3-generator 9-bus system and IEEE 10-generator 39-bus system. The simulation results show that the ATAN classifier can significantly improve the classification performance of the transient stability assessment of the power system.

Suggested Citation

  • Huimin Wang & Zhaojun Steven Li, 2022. "An AdaBoost-based tree augmented naive Bayesian classifier for transient stability assessment of power systems," Journal of Risk and Reliability, , vol. 236(3), pages 495-507, June.
    • Handle: RePEc:sae:risrel:v:236:y:2022:i:3:p:495-507
    DOI: 10.1177/1748006X211047308
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    References listed on IDEAS

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    1. Guo, Jian & (Steven) Li, Zhaojun & (Judy) Jin, Jionghua, 2018. "System reliability assessment with multilevel information using the Bayesian melding method," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 146-158.
    2. Huang, Xiaolin & Shi, Lei & Suykens, Johan A.K., 2014. "Asymmetric least squares support vector machine classifiers," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 395-405.
    3. Dan Huang & Qiyu Chen & Shiying Ma & Yichi Zhang & Shuyong Chen, 2018. "Wide-Area Measurement—Based Model-Free Approach for Online Power System Transient Stability Assessment," Energies, MDPI, vol. 11(4), pages 1-20, April.
    4. Ropero, R.F. & Renooij, S. & van der Gaag, L.C., 2018. "Discretizing environmental data for learning Bayesian-network classifiers," Ecological Modelling, Elsevier, vol. 368(C), pages 391-403.
    5. Shi, Zhongtuo & Yao, Wei & Zeng, Lingkang & Wen, Jianfeng & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu, 2020. "Convolutional neural network-based power system transient stability assessment and instability mode prediction," Applied Energy, Elsevier, vol. 263(C).
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