基于GAN关键样本增强和迁移学习的暂态稳定自适应评估方法

胡杰祥; 刘君; 李岩松; 陈胜硕; 徐顾钰; 陈兴雷

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基于GAN关键样本增强和迁移学习的暂态稳定自适应评估方法

更新时间：2026-02-03

- 基于GAN关键样本增强和迁移学习的暂态稳定自适应评估方法
- Vol. 53, Issue 1, Pages: 57-72(2026)
- 作者机构：
  
  1. 华北电力大学电气与电子工程学院
  2. 中国电力科学研究院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM712;TP18
- Published：2026
- 稿件说明：
移动端阅览
[1]胡杰祥,刘君,李岩松,等.基于GAN关键样本增强和迁移学习的暂态稳定自适应评估方法[J].华北电力大学学报(自然科学版),2026,53(01):57-72.

胡杰祥, 刘君, 李岩松, et al. 基于GAN关键样本增强和迁移学习的暂态稳定自适应评估方法[J]. 2026, 53(1): 57-72.
[1]胡杰祥,刘君,李岩松,等.基于GAN关键样本增强和迁移学习的暂态稳定自适应评估方法[J].华北电力大学学报(自然科学版),2026,53(01):57-72. DOI：

胡杰祥, 刘君, 李岩松, et al. 基于GAN关键样本增强和迁移学习的暂态稳定自适应评估方法[J]. 2026, 53(1): 57-72. DOI：

摘要

目前基于深度学习的暂态稳定评估需要离线生成大量数据对预测模型进行训练，但当训练数据不足，或系统运行场景发生变化时，暂稳评估的准确率将显著下降。为此，本文提出了一种基于生成对抗网络(generative adversarial networks

GAN)关键样本增强和迁移学习的暂态稳定自适应评估方法，选择并增强关键样本，适应电网运行工况变化。首先，针对样本质量问题，量化分析样本与模型训练和暂稳边界的相关性，定义样本重要性指标，据此筛选出关键样本，应用GAN对其进行增强。然后，针对电力系统运行工况的变化问题，设计了暂态稳定自适应评估方案，应用高质量样本集实现自适应评估。当系统运行工况不变时，基于适应电网数据特点的CNN-LSTM并联特征提取器构建了暂稳评估模型。分别利用卷积神经网络(CNN)和长短期记忆网络(LSTM)提取一级局部特征与时序特征，在此基础上融合为二级系统特征，传入后级全连接网络实现暂稳预测。当系统运行工况变化时，设计了自适应最优迁移学习策略，根据电力系统运行场景变化自适应选择最优迁移策略，解决了随运行场景变化程度增大，特征的可迁移性降低的问题。将本文所提出的方法应用于新英格兰10机39节点系统以及我国某省级电网。测试结果表明，关键样本比例增强至10%

预测模型的判错率降低了86.54%

能显著提高少样本条件下模型预测能力。本文所提出的自适应暂态稳定评估方案在电网运行工况不改变时的预测准确率提升至99.26%

且具有一定的泛化能力。当电网运行工况改变时，在目标电力系统的预测准确率达到98%以上。

Abstract

Current deep learning-based transient stability assessment requires the offline generation of large datasets to train the prediction model. However

when the training data is insufficient or the system's operating condition changes

the accuracy of the transient stability assessment markedly declines. This paper introduces an adaptive transient stability assessment method based on generative adversarial networks(GAN) key sample enhancement and transfer learning. This method selectively enhances key samples to accommodate changes in power system operating conditions. To address the issue of sample quality

the correlation between samples and both model training and transient stability boundaries is quantitatively analyzed. A sample importance index is defined

based on which key samples are screened and augmented using GAN. A transient stability adaptive assessment scheme is designed to handle variations in power system operating conditions

using a high-quality sample set for adaptive assessment. When the operating conditions of the system remain unchanged

the transient stability assessment model is constructed based on the parallel CNN-LSTM feature extractor adapted to the characteristics of the grid data. The convolutional neural network(CNN) and long-short-term memory network(LSTM) are employed to extract first-level local features and time-series features

which are fused into second-level system features and passed into the subsequent fully connected network to achieve transient stability prediction. For changing operating conditions

an adaptive optimal transfer learning strategy is designed to select the optimal transfer strategy based on changes in the operating conditions of the power system. This addresses the issue of reduced feature transferability as the degree of changes in operating conditions increases. The proposed method is applied to the New England 10-machine 39-bus system and a Chinese provincial grid. Results indicate that the proportion of key samples to 10% reduces the prediction error rate by 86.54%

significantly improving the model's prediction ability with fewer samples. The proposed adaptive transient stability assessment scheme improves prediction accuracy to 99.26% when the operating conditions of the grid remain unchanged

demonstrating a certain generalization ability. For changed conditions

the prediction accuracy in the target power system exceeds 98%.

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references

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