Abstract
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.
Published Version
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