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For power system transient stability assessment (TSA), the consequences of misclassifications of unstable samples are more serious than those of stable samples. Therefore, the cost-sensitive method is introduced into TSA. A constant weight is set for all unstable training samples by the conventional cost-sensitive method. However, the fault severities of the training samples are different. Thus, the impacts of the samples with different fault severities on assessment rules are also different. Therefore, an improved cost-sensitive method based on the fault severity is proposed. Firstly, the fault severity of each sample is calculated by its fault clearing time. On this basis, different cost-sensitive coefficients are assigned to the samples with different fault severities. The weights of critical samples are larger than those of non-critical ones. Then, a stacked sparse auto-encoder model based on the improved cost-sensitive method is built through modifying the loss function. Finally, the proposed method is tested in an actual system. Compared with the traditional cost-sensitive method, the results show that the improved cost-sensitive method has better performance. Also, it has the advantages of the low false rate of unstable samples, high-assessment accuracy, and strong anti-noise ability. [ABSTRACT FROM AUTHOR]

For power system transient stability assessment (TSA), the consequences of misclassifications of unstable samples are more serious than those of stable samples. Therefore, the cost‐sensitive method is introduced into TSA. A constant weight is set for all unstable training samples by the conventional cost‐sensitive method. However, the fault severities of the training samples are different. Thus, the impacts of the samples with different fault severities on assessment rules are also different. Therefore, an improved cost‐sensitive method based on the fault severity is proposed. Firstly, the fault severity of each sample is calculated by its fault clearing time. On this basis, different cost‐sensitive coefficients are assigned to the samples with different fault severities. The weights of critical samples are larger than those of non‐critical ones. Then, a stacked sparse auto‐encoder model based on the improved cost‐sensitive method is built through modifying the loss function. Finally, the proposed method is tested in an actual system. Compared with the traditional cost‐sensitive method, the results show that the improved cost‐sensitive method has better performance. Also, it has the advantages of the low false rate of unstable samples, high‐assessment accuracy, and strong anti‐noise ability. [ABSTRACT FROM AUTHOR]