Adaptive Assessment of Power System Transient Stability Based on Active Transfer Learning

Transient stability assessment models based on deep learning usually require a large number of labeled samples for offline training. Once the topologies or operation conditions of power grids change greatly, the pre-trained model deteriorates in performance and even becomes ineffective, resulting in a certain blank window during the online assessment. In order to solve this problem, taking deep belief network (DBN) as the research carrier, this paper combines deep learning, transfer learning and active learning to propose an active transfer learning method based on DBN model. Firstly, DBN is trained to mine the mapping relationship between input features and transient stability assessment results, so as to obtain better effect for transient stability assessment. Secondly, when the topologies or operation conditions change substantially, a large number of unlabeled samples are generated through short-term simulation, and active learning is exploited to select the minimum number of samples with the most information. Then, these selected samples are labeled by long-term simulation, which effectively reduces the time of sample generation. Finally, the maximum mean discrepancy (MMD) of the data distribution between the source domain and the target domain is calculated to select different transfer paths. The transfer time is further shortened on the premise of ensuring the transfer effect. The simulations on New England 10-unit 39-bus system, NPCC 48-unit 140-bus system and central China power grid are carried out, and the results verify that the proposed method has high accuracy, rapidity and robustness, which effectively shortens the blank window period of online application of the deep learning model. © 2023 Automation of Electric Power Systems Press. All rights reserved.