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

• 作者: Amin KHODAEI Rozhin ESKANDARPOUR
• 来源期刊: 现代电力系统与清洁能源学报(英文)
• 学科: 工学
• 关键词: Hurricanes MACHINE learning Power system RESILIENCE PREDICTIVE ANALYTICS
• 年，卷（期）: 2019,（4）
• 页码范围: 665-675
• 页数: 11页
• 分类号: TM714

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 hurricane.In 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 events.The TWSVM model is specifically used as it is suitable for handling imbalanced datasets.In the second step,a posterior probability sigmoid model is trained on the obtained results to convert the deterministic results into probabilistic outage states.These outage states enable the formation of probability-weighted contingency scenarios.Finally,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 grid.This potential load curtailments in power grid.The simulation of the proposed method.