Probabilistic load curtailment estimation using posterior probability model and twin support vector machine

作     者：Rozhin ESKANDARPOUR Amin KHODAEI 

作者机构：Department of Electrical and Computer EngineeringUniversity of DenverDenverCO 80208USA 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2019年第7卷第4期

页      面：665-675页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0808[工学-电气工程] 080802[工学-电力系统及其自动化] 08[工学] 0807[工学-动力工程及工程热物理] 

主　　题：Hurricanes Machine learning Power system resilience Predictive analytics 

摘      要：Estimating the potential load curtailments as a result of hurricane is of great significance in improving the paper proposes a three-step sequential method in identifying such load curtailments prior to *** the first step,a twin support vector machine(TWSVM)model is trained on path/intensity information of previous hurricanes to enable a deterministic outage state assessment of the grid components in response to upcoming *** TWSVM model is specifically used as it is suitable for handling imbalanced *** the second step,a posterior probability sigmoid model is trained on the obtained results to convert the deterministic results into probabilistic outage *** outage states enable the formation of probability-weighted contingency ***,the obtained component outages are integrated into a load curtailment estimation model to determine the expected results,tested on the standard IEEE 118-bus system and based on synthetic datasets,illustrate the high accuracy emergency response and the recovery of power *** potential load curtailments in power *** simulation of the proposed method.