Information Entropy Based Prioritization Strategy for Data-driven Transient Stability Batch Assessment
作者机构：College of Electrical EngineeringZhejiang UniversityHangzhouZhejiang 310027China Department of Electrical and Computer EngineeringIowa State UniversityAmesIA 50011USA
出 版 物：《CSEE Journal of Power and Energy Systems》 (中国电机工程学会电力与能源系统学报（英文）)
年 卷 期：2021年第7卷第3期
页 面：443-455页
核心收录：
学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学]
基 金：This work was supported by China scholarship council under Grant 201906320221
主 题：Cascaded convolutional neural networks(CNNs) dynamic task queue information entropy based prioritization strategy time-domain simulation(TDS) transient stability batch assessment(TSBA)
摘 要：Transient stability batch assessment(TSBA)is es-sential for dynamic security check in both power system planning and day-ahead *** is also a necessary technique to generate sufficient training data for data-driven online transient stability assessment(TSA).However,most existing work suffers from various problems including high computational burden,low model adaptability,and low performance ***,it is still a significant challenge in modern power systems,with numerous scenarios(e.g.,operating conditions andN-kcontin-gencies)to be assessed at the same *** purpose of this work is to construct a data-driven method to early terminate time-domain simulation(TDS)and dynamically schedule TSBA task queue a prior,in order to reduce computational burden without compromising *** achieve this goal,a time-adaptive cas-caded convolutional neural networks(CNNs)model is developed to predict stability and early terminate ***,an information entropy based prioritization strategy is designed to distinguish informative samples,dynamically schedule TSBA task queue and timely update model,thus further reducing simulation *** study in IEEE 39-bus system validates the effectiveness of the proposed method.
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