Voltage instability curtailment and quality improvement with phasor measurement unit in power system

Author:

Preeti Kabra,Rani Donepudi Sudha

Abstract

Purpose The earlier methods are more resilient to improvements such as load shift and path change. This results in problems such as a voltage drop and a high reactive flux. In addition, due to the delay, congestion or interruption of the transmission, the system cannot receive all phasor measurement unit (PMU) measurements at the relevant time as well as the presence of noise in the received data. Design/methodology/approach With the development of wide area measurement system technologies, it seems to be possible to track voltage stability online via time-stamped PMUs. As the voltage instability causes a voltage decomposition, voltage instability is one of the most important problems when monitoring the power supply. Findings This harmonic distortion significantly decreases the data quality in the grid. As a result, instability ascertainment based on PMU has been suggested as a method for detecting voltage instability in power systems monitored with PMU. In addition, a technique called instability amendment via load dropping has been proposed to keep the device from collapsing due to voltage failure. Originality/value To improve the power output, the power prominence melioration technique was developed. This proposed system has been implemented in MATLAB Simulink and compared with the recent researches.

Publisher

Emerald

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computational Theory and Mathematics,Computer Science Applications

Reference26 articles.

1. Power quality improvement using dynamic voltage restorer;IEEE Access,2020

2. Performance evaluation of voltage stability indices for a static voltage collapse prediction,2020

3. Real-time voltage stability monitoring using machine learning-based pmu measurements,2020

4. A novel multiobjective OPP for power system small signal stability assessment considering WAMS uncertainties;IEEE Transactions on Industrial Informatics,2019

5. Packet-data anomaly detection in PMU-based state estimator using convolutional neural network;International Journal of Electrical Power and Energy Systems,2019

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3