融合多类型深度迁移学习的电力系统暂态稳定自适应评估  

Adaptive assessment of transient stability for power system based on transfer multi-type of deep learning model

作　　者：李宝琴 吴俊勇[1] 张若愚 强子玥 覃柳芸 王春明 董向明 LI Baoqin;WU Junyong;ZHANG Ruoyu;QIANG Ziyue;QIN Liuyun;WANG Chunming;DONG Xiangming(School of Electrical Engineering,Beijing Jiaotong University,Beijing 100044,China;Institute of Science and Technology of China Three Gorges Corporation,Beijing 100038,China;Central China Branch of State Grid Corporation of China,Wuhan 430077,China)

机构地区：[1]北京交通大学电气工程学院,北京100044 [2]中国长江三峡集团有限公司科学技术研究院,北京100038 [3]国家电网公司华中分部,湖北武汉430077

出　　处：《电力自动化设备》2023年第1期184-192,共9页Electric Power Automation Equipment

基　　金：国家重点研发计划项目(2018YFB0904500);国家电网有限公司科技项目(SGLNDK00KJJS1800236)。

摘　　要：针对不同类型人工智能网络应用于电力系统暂态稳定评估时精度和泛化能力不稳定、运行方式或拓扑结构发生较大变化时评估精度下降、重新训练新模型费时费力等问题,提出一种融合多类型深度迁移学习模型(tmDLM)的自适应评估方法,该方法融合了深度置信网络、卷积神经网络以及长短期记忆网络3种不同的深度学习模型。将训练好的各类深度学习模型作为源域模型,当运行方式或拓扑结构发生较大变化时,采用少量目标域样本集微调预训练模型,使其快速跟踪系统当前的运行状态,并得到tmDLM。新英格兰10机39节点系统和华中电网的仿真结果表明:所提方法可以充分发挥各类深度学习方法的优势,具有良好的泛化能力;六分类模型能够在判稳的同时进行稳定裕度/失稳程度等级的评估;经过迁移后的深度学习模型具有良好的评估精度和时效性,大幅缩短了模型更新时间,实现了电力系统暂态稳定的自适应评估。Aiming at the problems that the accuracy and generalization ability are unstable when different types of artificial intelligence networks apply in transient stability assessment of power system,the assessment accuracy reduces when the operation mode or topological structure changes greatly,and the time and effort are wasted when retraining a new model,an adaptive assessment method integrating with a transfer multi-type of deep learning model(tmDLM) is proposed,which integrates with three different deep learning models of deep belief network,convolutional neural network and long short-term memory network. Each type of the trained deep learning model is taken as the source domain model,when the operation mode or the topological structure changes largely,a small number of the object domain sample sets are used to finetuning the pre-trained model,making it quickly track the current operation state of the system,then the tmDLM is obtained. The simulative results of New England 10-machine 39-bus system and the Central China Power Grid show that the proposed method can make full use of the advantages of each type of deep learning method with good generalization ability,the six-classification model can assess the stability margin/instability degree while judging the stable state,the deep learning model has good assessment accuracy and timeliness after transfer learning,which greatly reduces the update time of the model and realizes self-adaptive assessment of transient stability of power system.

关 键 词：深度学习 集成学习 迁移学习 电力系统 暂态稳定 

分 类 号：TM712[电气工程—电力系统及自动化]