电网技术2025，Vol.49Issue(10)：4114-4124,11.DOI:10.13335/j.1000-3673.pst.2024.0971
基于DANN特征映射校准和改进主动学习的电力系统暂态稳定性评估
Transient Stability Assessment of Power System Based on DANN Feature Mapping Calibration and Improved Active Learning
摘要
Abstract
Data-driven transient stability assessment models are usually trained offline using many samples from preset conditions,but the performance of the pre-trained models usually fails to meet the requirements when the operation conditions of power grids change greatly.To solve this problem,this paper proposes a transient stability assessment method of power systems based on domain adversarial neural networks(DANN)feature mapping calibration and improved active learning.Firstly,the feature extractor of DANN is used to map the data of the original condition and the new condition into a high-dimensional feature space to reduce the distributional difference between the two;secondly,a pair of stable and unstable samples in the new condition are labeled,and the distributional calibration is used to transfer the distributional information of the original condition to expand the sample set of the new condition,to train a high-performance base model of the new condition.Then,an improved active learning algorithm is utilized to select high-value and class-balanced samples to fine-tune the base model,to improve the model's performance quickly.Finally,the effectiveness and time efficiency of the proposed method are verified on the IEEE-39 node system and the 10,000-node test system.关键词
暂态稳定评估/迁移学习/域对抗神经网络/分布校准/主动学习Key words
transient stability assessment/transfer learning/DANN/distribution calibration/active learning分类
信息技术与安全科学引用本文复制引用
林凯威,刘俊,刘嘉诚,李雨婷,默天啸,李宗翰,徐式蕴..基于DANN特征映射校准和改进主动学习的电力系统暂态稳定性评估[J].电网技术,2025,49(10):4114-4124,11.基金项目
国家重点研发计划项目(2021YFB2400800).Project Supported by National Key Research and Development Program of China(2021YFB2400800). （2021YFB2400800）
