Transient Stability Assessment of Power Systems Based on the Transformer and Neighborhood Rough Set

Abstract

Modern power systems are large in size and complex in features; the data collected by Phasor Measurement Units (PMUs) are often noisy and contaminated; and the machine learning models that have been applied to the transient stability assessment (TSA) of power systems are not sufficiently capable of capturing long-distance dependencies. All these issues make it difficult for data mining-based power system TSA methods to have sufficient accuracy, timeliness, and robustness. To solve this problem, this paper proposes a power system TSA model based on the transformer and neighborhood rough set. The model first uses the neighborhood rough set to deal with the redundant features of the power system trend data and then uses the transformer model to train the TSA model, in which various normalization methods such as Batch Normalization and Layer Normalization are introduced in the process to obtain better evaluation performance and speed up the convergence rate of the model. Finally, the model is evaluated by two evaluation indicators, F1−measure and accuracy, with values of 99.61% for accuracy and 0.9972 for F1−measure, as soon as the tests on noise contamination and missing data test results on the IEEE39 system show that the NRS-Transformer model proposed in this paper is superior in terms of prediction accuracy, training speed, and robustness.