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作 者:刘元煌 伍智鹏 李献 翁志强 刘俊峰[3] 黄春艳 LIU Yuan-huang;Wu Zhi-peng;LI Xian;WENG Zhi-qiang;Liu Jun-feng;HUANG Chun yan(Hainan Power Grid Co.,Ltd.Electric Power Science Research Institute,Haikou 570105;Key Laboratory of Physical and Chemical Analysis for Electrie Power of Hainan Province,Haikou 570105;School of Automation Science and Engineering,South China University of Tchnology,Cuangzhou 510641;Guangzhou Power Electrical Technology Co.,Ltd.,Guangzhou 510641)
机构地区:[1]海南电网有限责任公司电力科学研究院,海口570105 [2]海南省电网理化分析重点实验室,海口570106 [3]华南理工大学自动化科学与工程学院,广州510641 [4]广州市奔流电力科技有限公司,广州510641
出 处:《环境技术》2024年第11期104-113,共10页Environmental Technology
基 金:中国南方电网有限责任公司科技项目,项目编号:080036KK52210003。
摘 要:针对新能源大规模接入后,难以获取准确的谐波模型和噪声信息,以及以往算法在数据异常时估计容易产生误判的问题,提出了一种基于H∞滤波方法(H∞filter)的电力系统谐波状态估计算法。利用H∞滤波器对模型精度和噪声信息要求低的特点,设计H∞滤波器,在电力系统噪声条件难以掌握的条件下保持估计准确性。其次,利用H∞滤波器优越的抗干扰性,保证了有异常数据时谐波状态估计的精度,做到无误判。在IEEE14节点系统进行了仿真,表明所提出的方法与传统卡尔曼滤波方法相比,在噪声信息难以确定条件下与异常数据干扰情况下,提高了谐波状态估计的精确度。In rcsponsc to thc challengcs posed by thc large-scale intcgration of ncw cnergy sourccs,which makes it dirricult to obtain accuratc harmonic modecls and noisc information,as well as thc propensity of conventional algorithms to generate misjudgments in the presence of data anomalies,this papcr proposes a harmonic state estimation algorithm for powcr systcms based on thc Il o filtcring mothod(ll co rilter).Leveraging the Iloo rilter's characteristics or low model prccision and noisc information requirenents,a customized H co filter is designed to maintain estimation accuracy in conditions where noisc in power system mcasurcmcnts is challcnging to characterizc.Furthermore,thc superior robusiness of tho H o rilter is utilized lo ensurc the accuracy of harmonic state estimation in the presence of abnormal data,thereby achieving error-free judgments.Simulations conducted on the IEEE 14-bus systcm indicatc that tho proposed method,comparcd to traditional Kalman filtcring mcthods,cnhances the prccision or harmonic stato estimation under conditions of challenging noise determination and interfterence from abnormal data.
分 类 号:TM711[电气工程—电力系统及自动化]
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