发电技术2023，Vol.44Issue(6)：865-874,10.DOI:10.12096/j.2096-4528.pgt.22165
堆栈式集成学习驱动的电力系统暂态稳定预防控制优化方法
Power System Transient Stability Preventive Control Optimization Method Driven by Stacking Ensemble Learning
摘要
Abstract
Aiming at the contradiction between the rapidity requirement of online calculation of transient stability preventive control and the computational complexity of time-domain equations,a stacking ensemable learning driven optimization method for power system transient stability preventive control was proposed.Firstly,a transient stability estimator based on a stacking ensemble deep belief network was constructed to replace the nonlinear differential algebraic equation solution process required for transient stability determination.Secondly,the trained transient stability estimator was used as a transient stability constraint discriminator,which was embedded in the iterative optimization process of the Aptenodytes Forsteri optimization algorithm.Finally,with the goal of minimizing the cost of preventive control,a stacking ensemble learning driven power system transient stability preventive control optimization algorithm was established.The algorithm realized the efficient judgment of transient stability constraints in preventive control,and improved the decision-making level of preventive control for power generation rescheduling.Based on the IEEE39 bus system,the proposed preventive control method was verified by experiments.The results show that the method has achieved good results in both evaluation accuracy and calculation efficiency.关键词
电力系统/堆栈式集成学习/帝企鹅启发式优化算法/暂态稳定/预防控制Key words
power system/stacking ensemble learning/Aptenodytes Forsteri optimization algorithm/transient stability/preventive control分类
信息技术与安全科学引用本文复制引用
潘晓杰,徐友平,解治军,王玉坤,张慕婕,石梦璇,马坤,胡伟..堆栈式集成学习驱动的电力系统暂态稳定预防控制优化方法[J].发电技术,2023,44(6):865-874,10.基金项目
国家电网公司华中分部科技项目(5214DK210014). Project Support by Science and Technology of Central China Branch of State Grid Corporation of China(5214DK210014). （5214DK210014）
