## 1 Introduction  
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]. Rapid and accurate recognition of transient stability status is important for being aware of the imminent unstable risk so that enough time is available for applying the appropriate control strategies to prevent catastrophic outage [2]. Classical transient stability analysis methods can be categorized into two branches, time-domain simulation (TDS) and transient energy function (TEF) [3,4]. Although the TDS method is straightforward and reliable, its high time complexity hinders real-time decision-making applications. As for the TEF method, it has low computational cost and provides the transient stability margin, but it is normally difficult to construct an available energy function when considering detailed system models. With the wide deployment of phasor measurement unit devices, tremendous synchrophasor data is accessible for monitoring the stability of power systems [5]. As a key tool for power system data analysis, machine learning shows great potential for real-time transient stability status prediction (TSSP) applications. Generally, TSSP itself can be regarded as a binary classification problem [6], i.e., stable/unstable status. In the offline, the nonlinear relationship between the selected features and the corresponding stability status can be established via machine learning methods. When online, transient stability status can be predicted immediately after feeding the collected features to the classification model. Up to now, a variety of machine learning methods have been applied for TSSP, e.g., neural networks [7; 8], support vector machines [9; 10], decision trees [11], and ensemble learning [12; 13]. In general, practical power systems can remain at transient stable status when subjected to most disturbances. That is to say, 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]. It is known that the unstable class is more important than the stable class for power system operators, and poor recognition of unstable samples would dramatically deteriorate the practical utility of the TSSP classification model. 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. The latter handles this problem via enhanced classification algorithms, e.g., cost-sensitive learning [17; 18]. However, few studies attempted to counteract the negative effects of imbalanced data for TSSP. Specifically, the unstable samples are duplicated to balance the sample number of different classes in Reference [14]. Although this method could be simply and directly conducted, it is prone to overfitting [18]. An adaptive synthetic sampling (ADASYN) algorithm is adopted in Reference [15] to generate more unstable samples, but the generated unstable samples by linear interpolation are hard related to the actual operating conditions of power systems, which may affect the rationality of the classification model. In this paper, a novel data segmentation-based ensemble classification (DSEC) method is proposed to better handle the imbalanced data problem of TSSP. The DSEC method consists of three steps.