Structure optimization method for transient stability preventive control considering uncertainty of renewable energy

被引：0
作者：
Qie, Zhaohui [1 ,2 ]
Li, Wei [2 ]
Jiang, Tao [1 ]
Liu, Fusuo [2 ]
机构：
[1] School of Electrical Engineering, Northeast Electric Power University, Jilin
[2] NARI Group Corporation, State Grid Electric Power Research Institute, Nanjing
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2024年 / 44卷 / 09期
基金：
中国国家自然科学基金;
关键词：
example screening; preventive control; stable domain; transient stability; uncertainty;
D O I：
10.16081/j.epae.202312029
中图分类号：
学科分类号：
摘要：
The impact of renewable energy uncertainty on online preventive control of power grid has gradually shifted from quantitative change to qualitative change，and the complexity and computational quantity of preventive control optimization have significantly increased. In order to meet the need of solution quality and efficiency for online preventive control，the machine learning，structure optimization method and time-domain simulation method are combined. In order to reduce the online computational quantity，the stability function structure of power grid for typical system is analyzed，on this basis，the machine learning method is used to offline construct the stable domain. In order to solve the problem of conflict between control measures，a structure optimization method is introduced，and a quadratically constrained programming model for preventive control is constructed to achieve coordination of preventive control measures and rapid solution of the model. In order to ensure the reliability of control measures，the high-risk cases are selected based on credibility evaluation indicators，and the results of time domain verification for all high-risk scenarios are stable by iterative calculation. The feasibility and effectiveness of the proposed method are verified in an actual power grid. © 2024 Electric Power Automation Equipment Press. All rights reserved.
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页码：41 / 48
页数：7
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