Adaptive Assessment of Power System Transient Stability Based on Active Transfer Learning With Deep Belief Network

Abstract

Transient stability assessment (TSA) based on deep learning has recently attracted broad attention. The offline training of deep learning model typically requires a large number of labeled samples, and the training process is very time-consuming. Moreover, it is difficult to ensure that the trained model is suitable for all the operation conditions of power systems. In order to dramatically reduce the computation cost, and further enhance the adaptability of the model, an innovative method is proposed based on the active transfer learning (TL). First, the deep belief network (DBN) is designed as the base model and trained with a large number of labeled samples to obtain better evaluation performance for TSA. Second, when the topologies or operation conditions change substantially, active learning with information entropy is exploited to select the minimum number of the most informative samples, which effectively reduces the time of samples generation. Third, Maximum Mean Discrepancy (MMD) is calculated to select different transfer schemes. Only the last BP layer needs to be fine-tuned for the scenarios with a low MMD, but for those with high MMD, the entire network needs to be fine-tuned. The transfer time is greatly shortened on the premise of ensuring the transfer effect. The comprehensive experimental results of three power systems illustrate the effectiveness of the proposed method. Note to Practitioners—This research is motivated by automatic adaptive assessment of power system transient stability. The main objective is to realize online fast and continuous prediction with limited samples. This kind of demand is very normal in the industrial systems where it is expensive, time-consuming and difficult to obtain labeled samples. This paper proposes an active transfer learning method to select critical samples and update the deep learning model, which greatly reduces the time of samples generation and improves the updating efficiency of model. The proposed method is also applicable to make classification prediction or to address regression problems of industrial scenarios based on deep learning models.