Transient Stability Prediction with Time-Adaptive Method Based on Spatial Distribution

For power system transient stability assessment (TSA), the differences of characteristics between critical situations are not obvious in the early post-fault stage, so high accuracy can’t be achieved. With the development of fault, the differences are more obvious, and the evaluation accuracy is improved. To balance the trade-off between accuracy and timeliness, a spatial distribution-based time-adaptive (SDTA) method is proposed. Firstly, several long and short-term memory (LSTM) classifiers of different cycles construct a time-adaptive framework. Then, the overlapping region where misclassification is likely to occur is selected according to the spatial distribution of training samples. The TSA results of samples in non-overlapping regions are directly output, and the reliability of samples in overlapping regions is evaluated. Finally, the distance ratio-based trust score (DRTS) is proposed as a reliability index. The reliability of overlapping samples is evaluated by employing the changing trend of DRTS. If the classification at the current cycle is assessed as reliable, the TSA results are output. Otherwise, the system will wait for the feature of the next cycle for prediction. The proposed method is tested in the IEEE-39 bus system and a realistic system. The case studies demonstrate that the scheme meets the requirements of TSA