Abstract
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.
Highlights
With the emergence of the large-scale interconnected power grid and the high penetration of distributed generation, modern power systems are faced with severe challenges for stable operation.Transient stability is a crucial and complex issue in modern power systems [1]
This paper proposes the data segmentation-based ensemble classification (DSEC) method to deal with the imbalanced data problem of transient stability status prediction (TSSP)
The effects of the imbalanced data problem on the decision boundary and the classification performance of TSSP are analyzed in detail
Summary
With the emergence of the large-scale interconnected power grid and the high penetration of distributed generation, modern power systems are faced with severe challenges for stable operation. An unstable status is detected only in a few situations, which results in the imbalanced data problem in the training database, i.e., stable samples significantly outnumber unstable samples [14,15] Faced with this issue, conventional machine learning methods aiming to minimize the overall error rate tend to classify the samples as the stable class and show ineffectiveness in identifying unstable samples [14]. In the machine learning community, the imbalanced data problem in classification tasks is a hot topic of research and effective solutions can mainly be divided into data-level and algorithm-level approaches [16] The former achieves data rebalance by adding samples of minority class, namely oversampling, or reducing samples of majority class, namely undersampling.
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