Transient stability evaluation model based on SSDAE with imbalanced correction
Huaiyuan Wang, Weitao Ye
Abstract
With the rapid development of machine learning technology, a new tool is provided for real‐time stability evaluation in power systems. The training of a machine learning‐based model is inseparable from a large number of training samples. However, compared with stable samples, unstable samples in power systems are infrequent. The results of the model evaluation are biased due to the imbalance of training samples. Faced with such a problem, a framework based on deep imbalanced learning is proposed. Firstly, for each sample, the samples nearby in the opposite class are applied to calculate its space information. Based on the space information of all samples, the spatial distribution characteristics of the training samples are obtained. And then, in order to obtain balanced training samples, unstable samples are generated according to their spatial distribution characteristics. Finally, stacked sparse denoising auto‐encoder (SSDAE) based model, which has the ability of anti‐noise, is established as the classifier. Simulation results in IEEE 39‐bus system show the high performance of the proposed imbalanced correction scheme and evaluation scheme.