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Open AccessArticle

A Data Segmentation-Based Ensemble Classification Method for Power System Transient Stability Status Prediction with Imbalanced Data

1
State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China
2
State Grid Sichuan Electric Power Company, Chengdu 610041, China
3
School of Electrical Engineering, Chongqing University, Chongqing 400044, China
4
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4216; https://doi.org/10.3390/app9204216
Received: 15 September 2019 / Revised: 28 September 2019 / Accepted: 3 October 2019 / Published: 10 October 2019
In recent years, machine learning methods have shown the great potential for real-time transient stability status prediction (TSSP) application. However, most existing studies overlook the imbalanced data problem in TSSP. To address this issue, a novel data segmentation-based ensemble classification (DSEC) method for TSSP is proposed in this paper. Firstly, the effects of the imbalanced data problem on the decision boundary and classification performance of TSSP are investigated in detail. Then, a three-step DSEC method is presented. In the first step, the data segmentation strategy is utilized for dividing the stable samples into multiple non-overlapping stable subsets, ensuring that the samples in each stable subset are not more than the unstable ones, then each stable subset is combined with the unstable set into a training subset. For the second step, an AdaBoost classifier is built based on each training subset. In the final step, decision values from each AdaBoost classifier are aggregated for determining the transient stability status. The experiments are conducted on the Northeast Power Coordinating Council 140-bus system and the simulation results indicate that the proposed approach can significantly improve the classification performance of TSSP with imbalanced data. View Full-Text
Keywords: imbalanced data; data segmentation; ensemble classification; transient stability status imbalanced data; data segmentation; ensemble classification; transient stability status
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MDPI and ACS Style

Chen, Z.; Han, X.; Fan, C.; He, Z.; Su, X.; Mei, S. A Data Segmentation-Based Ensemble Classification Method for Power System Transient Stability Status Prediction with Imbalanced Data. Appl. Sci. 2019, 9, 4216.

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