System Strength Assessment Based on Multi-task Learning
作者机构：Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of EducationShandong UniversityJinan 250014China China Electric Power Research InstituteBeijing 100192China
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：2024年第10卷第1期
页 面：41-50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：Critical short circuit ratio hybrid intelligence enhancement multi-task learning system strength
摘 要：Increase in permeability of renewable energy sources(RESs)leads to the prominent problem of voltage stability in power system,so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable ***,a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven ***,calculation of critical short circuit ratio(CSCR)is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative ***,the construction process of CSCR dataset is proposed,and a batch simulation program of samples is developed accordingly,which provides a data basis for subsequent ***,a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point,which significantly reduces evaluation error caused by weak *** performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system,which provides strong technical support for real-time monitoring of system strength.
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