摘要：Maintaining accurate and fast transient stability is essential for safe operation of the power system. With the development of wide-area measurement system, machine learning–based transient stability assessment has become the trend. However, in realistic application of the power system, the impacts on evaluation rules between critical samples and noncritical samples are different. Thus, an improved cost-sensitive coefficient assignment method based on fault severity is proposed. First, the fault severity of each unstable sample is calculated. Then, the correction coefficient of the loss function of the unstable sample is linearized according to different fault severities. The closer the sample is to the critical case, the higher the cost coefficient is. Finally, the improved cost-sensitive method is combined with the deep learning model and tested in the IEEE-39 bus system. As shown in the results, the improved cost-sensitive method, which gives different correction coefficients to samples according to different fault severities, has better performance.
