Maintaining transient stability is a core requirement for ensuring safe operation of power systems. Hence, quick and accurate assessment of the transient stability of power systems is particularly critical. As the deployment of wide area measurement systems (WAMS) expands, transient stability assessment (TSA) based on machine learning with data of phasors measurement units (PMUs) also develops rapidly. However, unstable samples of the power system are rarely seen in practice which affects greatly the effectiveness of transient instability recognition. To address this problem, we propose a deep imbalanced learning-based TSA framework. First, an improved denoising autoencoder (DAE) is constructed to map the training set to hidden space for dimension reduction. Then, adaptive synthetic sampling (ADASYN) is further used to synthesize unstable samples in hidden space to balance the proportion of different classes. Third, the synthesized data are decoded into the original space to enhance the training set. Finally, an ensemble cost-sensitive classifier based on a stacked denoising autoencoder (SDAE) is designed to extract different feature patterns, and the SDAEs are merged with a fusion layer to classify the status of the power system. The simulation results of two benchmark power systems indicate that the proposed method can effectively improve the recognition accuracy of unstable cases by combining nonlinear data synthesis with ensemble cost-sensitive learning methods. Compared with other imbalanced learning methods, the proposed framework enjoys superiority both in accuracy and G-mean. INDEX TERMS Deep imbalanced learning, transient stability of power system, denoising autoencoder (DAE), ensemble cost-sensitive SDAE, feature patterns, G-mean.

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With the interconnection between large power grids, the issue of security and stability has become increasingly prominent. At present, data-driven power system adaptive transient stability assessment methods have achieved excellent performances by balancing speed and accuracy, but the complicated construction and parameters are difficult to obtain. This paper proposes a stacked-GRU (Gated Recurrent Unit)-based transient stability intelligent assessment method, which builds a stacked-GRU model based on time-dependent parameter sharing and spatial stacking. By using the time series data after power system failure, the offline training is performed to obtain the optimal parameters of stacked-GRU. When the application is online, it is assessed by framework of confidence. Basing on New England power system, the performance of proposed adaptive transient stability assessment method is investigated. Simulation results show that the proposed model realizes reliable and accurate assessment of transient stability and it has the advantages of short assessment time with less complex model structure to leave time for emergency control.In order to achieve rapid TSA, a large number of machine learning algorithms such as the BP neural network [12], support vector machine [13,14], decision tree [15], random forest [16], and K-nearest neighbor [17] are applied in intelligent systems, such algorithms mainly use the key features of the power system after feature selection to establish functional relationship with transient stability.Once the fault is cleared, the transient stability assessment can be performed, but there is no guarantee of 100% accuracy of assessment. Therefore, an adaptive assessment method [9,18] has emerged in recent years. By determining the time window of the fixed length after the fault, m data sampling points in the time window is used for training; the assessment is performed in chronological order until the assessment confidence meets the requirements. Such approaches guarantee the correctness of assessment, but the assessment time is long and the construction of the model is very cumbersome: m data sampling point means that m classifiers need to be trained. Although the authors of a past paper have [18] considered the influence of historical information on the current time assessment in the time series, it reduces the assessment time to a certain extent, but still needs to train a large number of classifiers.Aiming at solving the problems existing in the above adaptive assessment method, the main contribution of this paper is to proposes a stacked-GRU based intelligent TSA model, which realizes the parameter sharing at each moment through stacked-GRU and utilizes the time series data after the power system failure to train with less complex model structure. The offline training is performed to obtain the optimal parameters. When the application is online, it is judged whether the current assessment result satisfies the confidence level at the time of fault clearing, otherwise measurements will continue to ...