Critical situations are difficult to predict reliably by the machine learning‐based transient stability assessment (TSA) methods. Therefore, the practicality of the data‐driven TSA is limited. A parallel TSA framework constructed by two basic predictors and a comprehensive decider (CD) is proposed to achieve fast and reliable real‐time transient stability assessment (RTSA). A cost‐sensitive method is utilized for stacked sparse auto‐encoders to establish two basic predictors with opposite evaluation biases. Then, the outputs of the two basic predictors are sent to the CD. Finally, the stability of the non‐critical cases can be judged directly, and the critical cases are suggested to be analyzed by other methods. Besides, in order to enhance the reliability of the parallel predictor, a simple data augmentation approach with Gaussian white noise is employed to expand the classification boundaries. A fault severity factor is introduced to filter basic critical samples for data augmentation to improve the performance of the proposed framework. The effect of the proposed strategy is verified on the IEEE‐39 bus system and a realistic regional system.

中文翻译：

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具有并行结构和数据扩充的暂态稳定评估模型

关键情况很难通过基于机器学习的瞬态稳定性评估（TSA）方法可靠地预测。因此，数据驱动的TSA的实用性受到限制。提出了一个由两个基本预测变量和一个综合决策程序（CD）构成的并行TSA框架，以实现快速，可靠的实时暂态稳定性评估（RTSA）。堆叠式稀疏自动编码器使用一种对成本敏感的方法来建立两个具有相反评估偏差的基本预测器。然后，将两个基本预测变量的输出发送到CD。最后，可以直接判断非关键案例的稳定性，并建议通过其他方法对关键案例进行分析。此外，为了提高并行预测器的可靠性，采用具有高斯白噪声的简单数据增强方法来扩展分类边界。引入故障严重性因子以过滤基本关键样本以进行数据扩充，以改善所提出框架的性能。该提议策略的效果已在IEEE-39总线系统和实际的区域系统上得到验证。