Robotics & Machine Learning Daily News2024,Issue(Nov.22) :5-5.

Findings on Machine Learning Detailed by Investigators at Zhejiang University (A Machine Learning-based Svg Parameter Identification Framework Using Hardware-in -the-loop Testbed)

浙江大学研究者详细介绍的机器学习发现(基于机器学习的Svg参数识别框架,使用硬件在环测试床)

Robotics & Machine Learning Daily News2024,Issue(Nov.22) :5-5.

Findings on Machine Learning Detailed by Investigators at Zhejiang University (A Machine Learning-based Svg Parameter Identification Framework Using Hardware-in -the-loop Testbed)

浙江大学研究者详细介绍的机器学习发现(基于机器学习的Svg参数识别框架,使用硬件在环测试床)

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-研究人员详细介绍机器学习中的新数据。根据新闻报道来自中国人民共和国杭州,由NewsRx记者报道,研究称,“静态”无功发生器(SVG)能有效地补偿无功功率以维持可接受的母线电压大扰动前后的水平。准确的svg控制器模型参数是控制系统的关键为电力系统规划和运行决策提供可靠的SVG动态仿真气质

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Researchers detail new data in Machine Learning. According to news reporting originatingfrom Hangzhou, People’s Repub lic of China, by NewsRx correspondents, research stated, “StaticVar Generator ( SVG) can effectively compensate reactive power for maintaining acceptable bus vo ltagelevels before and after major disturbances. Accurate model parameters of S VG controllers are essential toensure reliable simulation of SVG dynamic behavi or for power system planning and operational decisionmakings.”

Key words

Hangzhou/People’s Republic of China/As ia/Cyborgs/Emerging Technologies/Machine Learning/Zhejiang University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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