基于去噪扩散概率模型不平衡样本增强的暂态稳定评估  

Transient Stability Assessment Based on Imbalanced Sample Enhancement of Denoising Diffusion Probabilistic Model

作　　者：李雨婷 刘俊[1] 刘嘉诚 王光耀 默天啸 林凯威 LI Yuting;LIU Jun;LIU Jiacheng;WANG Guangyao;MO Tianxiao;LIN Kaiwei(School of Electrical Engineering,Xi’an Jiaotong University,Xi’an 710049,China)

机构地区：[1]西安交通大学电气工程学院,陕西省西安市710049

出　　处：《电力系统自动化》2024年第21期148-157,共10页Automation of Electric Power Systems

基　　金：国家重点研发计划资助项目(响应驱动的大电网稳定性智能增强分析与控制技术,2021YFB2400800)。

摘　　要：准确有效的电力系统暂态稳定评估对电力系统安全稳定运行具有重要意义。目前,基于深度学习的暂态稳定评估方法面临着时序特征空间表征困难、样本类别严重不平衡等问题,影响到评估结果的可信度。为了弥补现有研究的不足,提出了一种基于去噪扩散概率模型(DDPM)不平衡样本增强的电力系统暂态稳定评估方法。首先,构建改进HSV颜色模型对高维数据进行二维图像化处理,从而直观表征高维数据,便于后续训练;然后,基于DDPM算法对不平衡失稳样本空间进行表征学习,规模化生成概率同分布的增强样本,进而解决类别不平衡问题;最后,提出梯度加权类激活映射卷积神经网络以构建暂态稳定评估模型,提升模型的可信度与可解释性。IEEE 39节点系统测试的仿真结果表明,所构建的模型相较于其他方法具备更高的稳定性判别精度,且对失稳样本的识别率显著提高,验证了所提方法的有效性。The accurate and effective transient stability assessment for power systems is of great significance for the safe and stable operation of power systems.At present,transient stability assessment methods based on deep learning are faced with problems such as difficulty in time-series feature space representation and serious imbalance of sample categories,which affect the reliability of assessment results.In order to make up for the shortcomings of existing studies,a transient stability assessment method for power systems based on the imbalanced sample enhancement of denoising diffusion probabilistic model(DDPM)is proposed.First,an improved HSV colour model is constructed to process the high-dimensional data in two-dimensional image,so as to visually represent the high-dimensional data and facilitate subsequent training.Then,based on DDPM algorithm,the imbalanced unstable sample space is characterized and learned,and the enhanced samples with the same probability distribution are generated on a large scale to solve the category imbalance problem.Finally,a gradient-weighted class activation mapping convolutional neural network is proposed to construct a transient stability assessment model to improve the reliability and interpretability of the model.The simulation results of IEEE 39-bus system test show that compared with other methods,the proposed model has higher stability discrimination accuracy,and the recognition rate of unstable samples is significantly improved,which verify the effectiveness of the proposed method.

关 键 词：暂态稳定评估 去噪扩散概率模型 HSV颜色模型 样本不平衡 可解释性 

分 类 号：TM712[电气工程—电力系统及自动化]