Machine learning-based fast frequency response control for a VSC-HVDC system
作者机构：University of TennesseeKnoxvilleTN 37996USA University of TennesseeKnoxvilleTN 37996USA and also with the Oak Ridge National LaboratoryOak RidgeTN 37831USA
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4期
页 面：688-697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]
主 题：Frequency response control multivariate random forest regression VSC-HVDC system
摘 要：HVDC system can realize a very fast frequency response to the disturbed system under a contingency because its active power control is decoupled from the frequency ***,most of existing HVDC frequency control strategies are coupled with system primary frequency control and secondary frequency *** the traditional system frequency control is dominated by the thermal generators,the advantage of the fast response of the HVDC system is not made fully *** development of a frequency response estimation based on a machine learning algorithm provides another approach to improve the frequency response capability of the HVDC *** from other frequency deviation tracking strategies,a machine learning based HVDC frequency response control can directly increase the power flow of a HVDC system by estimation of the system generator or load *** this paper,a fast frequency response control using a HVDC system for a large power system disturbance based on the multivariate random forest regression(MRFR)algorithm is *** simulation is carried out with an integrated power system model based on the North American *** simulation results indicate that the proposed MRFR based frequency response control can significantly improve the frequency low point during an event,while stabilizing the frequency in advance.
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