Article(id=1205158521613558746, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1205158521005384666, articleNumber=null, orderNo=null, doi=10.19457/j.1001-2095.dqcd24915, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1673712000000, receivedDateStr=2023-01-15, revisedDate=1683734400000, revisedDateStr=2023-05-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1765261782315, onlineDateStr=2025-12-09, pubDate=1724083200000, pubDateStr=2024-08-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1765261782315, onlineIssueDateStr=2025-12-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1765261782315, creator=13701087609, updateTime=1765261782315, updator=13701087609, issue=Issue{id=1205158521005384666, tenantId=1146029695717560320, journalId=1189987059142926344, year='2024', volume='54', issue='8', pageStart='3', pageEnd='96', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1765261782170, creator=13701087609, updateTime=1765261980641, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1205159353524400961, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1205158521005384666, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1205159353524400962, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1205158521005384666, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=48, endPage=56, ext={EN=ArticleExt(id=1205158521823273947, articleId=1205158521613558746, tenantId=1146029695717560320, journalId=1189987059142926344, language=EN, title=A Fast Current Protection Algorithm for Power Distribution Systems Based on Discrete Setting-value, columnId=null, journalTitle=Electric Drive, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In traditional current protection,the effective value of current is compared with the setting value to identify the fault which is the typical protection scheme for the transmission lines of the power distribution systems. The current is calculated by the fast Fourier transform algorithm. As the calculation of the effective value of current requires a long time,the speed of the current quick break protection is reduced. In this regard,a fast current protection algorithm based on the discrete setting-value was presented to solve this problem. The main emphasis was placed on the construction of the discrete sequence of setting-value and the protection criterion. The protection startup criterion and action criterion were constructed based on comparisons between discrete sequence of setting-value and sampling-value of the current fault component. The operation characteristics and reliability of the fast current protection were analyzed. The performance of the novel fast current protection algorithm was compared with that of the conventional current protection through simulations based on PSCAD/EMTDC. The results verify that the novel fast current protection algorithm has good operation characteristics under different fault conditions.

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传统的电流保护使用电流的有效值与整定值进行比较以识别故障,这是配电系统输电线路的典型保护方案。通过快速傅里叶变换(FFT)算法计算电流。由于电流有效值的计算需要很长时间,电流速断保护的速度降低。针对这一问题,提出了一种基于离散整定值的快速电流保护算法。重点介绍了整定值离散序列和保护判据的构造。通过对电流故障分量整定值和采样值离散序列的比较,建立了保护启动判据和动作判据。分析了快速电流保护的工作特性和可靠性。通过基于PSCAD/EMTDC的仿真,将新型快速电流保护算法的性能与传统电流保护算法进行了比较。结果表明,该快速电流保护算法在不同故障条件下具有良好的运行特性。

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张军(1978—),男,本科,工程师,主要研究方向为电气工程及其自动化,Email:

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张军(1978—),男,本科,工程师,主要研究方向为电气工程及其自动化,Email:

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张军(1978—),男,本科,工程师,主要研究方向为电气工程及其自动化,Email:

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orderNo=2, keyword=快速电流保护), Keyword(id=1205172815814177070, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1205158521613558746, language=CN, orderNo=3, keyword=离散整定值), Keyword(id=1205172815935811888, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1205158521613558746, language=CN, orderNo=4, keyword=电流故障分量)], refs=[Reference(id=1205172823493947827, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1205158521613558746, doi=null, pmid=null, pmcid=null, year=2021, volume=null, issue=7, pageStart=4633, pageEnd=4641, url=null, language=null, rfNumber=[1], rfOrder=0, authorNames=MA Hengrui, LIU Zhe, LI Min, journalName=Energy Reports, refType=null, unstructuredReference=MA Hengrui, LIU Zhe, LI Min, et al. 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Applied Energy, 2020, 272:115228., articleTitle=An effective stochastic framework for smart coordinated operation of wind park and energy storage unit, refAbstract=null)], funds=[Fund(id=1205172823259066802, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1205158521613558746, awardId=52167010, language=CN, fundingSource=国家自然科学基金(52167010), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1205172813381480637, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1205158521613558746, xref=null, ext=[AuthorCompanyExt(id=1205172813394063551, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1205158521613558746, companyId=1205172813381480637, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=State Grid Hubei Electric Power Co.,Ltd.Shennongjia Power Supply Company,Shennongjia 442400,Hubei,China), AuthorCompanyExt(id=1205172813402452161, 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journalId=1189987059142926344, articleId=1205158521613558746, language=EN, label=Fig.9, caption=Fault current waveform with a CT saturation, figureFileSmall=MlenIMmYWF5ILrQTvd+pQQ==, figureFileBig=lnCd8tnEcOekMhWqrPXJDQ==, tableContent=null), ArticleFig(id=1205172819106705778, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1205158521613558746, language=CN, label=图9, caption=CT饱和时的故障电流波形, figureFileSmall=MlenIMmYWF5ILrQTvd+pQQ==, figureFileBig=lnCd8tnEcOekMhWqrPXJDQ==, tableContent=null), ArticleFig(id=1205172819182203253, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1205158521613558746, language=EN, label=Fig.10, caption=Simulation model of 10 kV distribution network, figureFileSmall=/OYrXrFxDLCdi2RdnQ7sVg==, figureFileBig=6NHnMFz3lL/ufcsEYywhgg==, tableContent=null), ArticleFig(id=1205172819282866551, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1205158521613558746, language=CN, label=图10, caption=10 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caption=三种保护算法的比较分析, figureFileSmall=npKH95vV0zI7ycUoZPSM9A==, figureFileBig=T9qsbzkkV3PcI+0i0ACulw==, tableContent=null), ArticleFig(id=1205172822688641451, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1205158521613558746, language=EN, label=Tab.1, caption=

Effect of fault type

, figureFileSmall=null, figureFileBig=null, tableContent=
故障类型 故障位置 故障时刻 延迟时间/ms
传统 新型
Ag F1 0.100 20.9 3.0
F1 0.102 19.1 3.0
F1 0.104 18.4 3.0
ABg F1 0.100 12.5 3.0
F1 0.102 11.0 3.0
F1 0.104 10.4 3.0
AB F1 0.100 12.5 3.0
F1 0.102 11.2 3.0
F1 0.104 11.3 3.0
ABC F1 0.100 13.2 3.0
F1 0.102 11.3 3.0
F1 0.104 10.3 3.0
), ArticleFig(id=1205172822885773740, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1205158521613558746, language=CN, label=表1, caption=

故障类型的影响

, figureFileSmall=null, figureFileBig=null, tableContent=
故障类型 故障位置 故障时刻 延迟时间/ms
传统 新型
Ag F1 0.100 20.9 3.0
F1 0.102 19.1 3.0
F1 0.104 18.4 3.0
ABg F1 0.100 12.5 3.0
F1 0.102 11.0 3.0
F1 0.104 10.4 3.0
AB F1 0.100 12.5 3.0
F1 0.102 11.2 3.0
F1 0.104 11.3 3.0
ABC F1 0.100 13.2 3.0
F1 0.102 11.3 3.0
F1 0.104 10.3 3.0
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Effect of fault inception instant

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故障类型 故障位置 故障时刻 延迟时间/ms
传统 新型
ABC F1 0.100 13.2 5.1
ABC F1 0.102 11.3 5.0
ABC F1 0.104 10.3 5.2
ABC F1 0.106 12.2 5.0
ABC F1 0.108 15.4 5.1
ABC F1 0.110 13.7 5.0
ABC F1 0.112 11.9 5.0
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故障发生瞬间的影响

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故障类型 故障位置 故障时刻 延迟时间/ms
传统 新型
ABC F1 0.100 13.2 5.1
ABC F1 0.102 11.3 5.0
ABC F1 0.104 10.3 5.2
ABC F1 0.106 12.2 5.0
ABC F1 0.108 15.4 5.1
ABC F1 0.110 13.7 5.0
ABC F1 0.112 11.9 5.0
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基于离散整定值的配电系统快速电流保护算法
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张军 , 宫旻 , 周宜 , 谢俊娥 , 曹磊 , 刘盈盈
电气传动 | 电力电子 2024,54(8): 48-56
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电气传动 | 电力电子 2024, 54(8): 48-56
基于离散整定值的配电系统快速电流保护算法
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张军 , 宫旻, 周宜, 谢俊娥, 曹磊, 刘盈盈
作者信息
  • 国网湖北电力有限公司神农架供电公司,湖北 神农架 442400
  • 张军(1978—),男,本科,工程师,主要研究方向为电气工程及其自动化,Email:

A Fast Current Protection Algorithm for Power Distribution Systems Based on Discrete Setting-value
Jun ZHANG , Min GONG, Yi ZHOU, June XIE, Lei CAO, Yingying LIU
Affiliations
  • State Grid Hubei Electric Power Co.,Ltd.Shennongjia Power Supply Company,Shennongjia 442400,Hubei,China
出版时间: 2024-08-20 doi: 10.19457/j.1001-2095.dqcd24915
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传统的电流保护使用电流的有效值与整定值进行比较以识别故障,这是配电系统输电线路的典型保护方案。通过快速傅里叶变换(FFT)算法计算电流。由于电流有效值的计算需要很长时间,电流速断保护的速度降低。针对这一问题,提出了一种基于离散整定值的快速电流保护算法。重点介绍了整定值离散序列和保护判据的构造。通过对电流故障分量整定值和采样值离散序列的比较,建立了保护启动判据和动作判据。分析了快速电流保护的工作特性和可靠性。通过基于PSCAD/EMTDC的仿真,将新型快速电流保护算法的性能与传统电流保护算法进行了比较。结果表明,该快速电流保护算法在不同故障条件下具有良好的运行特性。

配电系统  /  快速电流保护  /  离散整定值  /  电流故障分量

In traditional current protection,the effective value of current is compared with the setting value to identify the fault which is the typical protection scheme for the transmission lines of the power distribution systems. The current is calculated by the fast Fourier transform algorithm. As the calculation of the effective value of current requires a long time,the speed of the current quick break protection is reduced. In this regard,a fast current protection algorithm based on the discrete setting-value was presented to solve this problem. The main emphasis was placed on the construction of the discrete sequence of setting-value and the protection criterion. The protection startup criterion and action criterion were constructed based on comparisons between discrete sequence of setting-value and sampling-value of the current fault component. The operation characteristics and reliability of the fast current protection were analyzed. The performance of the novel fast current protection algorithm was compared with that of the conventional current protection through simulations based on PSCAD/EMTDC. The results verify that the novel fast current protection algorithm has good operation characteristics under different fault conditions.

power distribution systems  /  fast current protection  /  discrete setting-value  /  current fault component
张军, 宫旻, 周宜, 谢俊娥, 曹磊, 刘盈盈. 基于离散整定值的配电系统快速电流保护算法. 电气传动, 2024 , 54 (8) : 48 -56 . DOI: 10.19457/j.1001-2095.dqcd24915
Jun ZHANG, Min GONG, Yi ZHOU, June XIE, Lei CAO, Yingying LIU. A Fast Current Protection Algorithm for Power Distribution Systems Based on Discrete Setting-value[J]. Electric Drive, 2024 , 54 (8) : 48 -56 . DOI: 10.19457/j.1001-2095.dqcd24915
随着城市配电网的快速发展,为了提高用户的供电可靠性,配电网中的输电线路数量逐渐增加。此外,两级之间的配电线路较短,多级线路保护的短路电流差较小,因此很难设计保护配置方案来实现上下游与电流整定的协调[1]。例如,传统过流区段Ⅰ和过流区段Ⅱ的组合甚至会导致10 kV故障过渡到35 kV跳闸。为了降低过步跳闸的风险,基于时间差协调的电流保护在实践中被广泛使用。差分时间通常设置为0.2~0.5 s,但传统电流保护算法的数据窗口长度为一个周期。考虑到保护判断和保护输出延迟时间,很难将保护动作时间减少到20 ms以下。受限于变压器输出保护动作时间的要求(通常小于0.5 s),多级配电网中基于时间差的电流保护的适应性严重降低[2-3]。因此,在配电网多层次趋势日益明显的情况下,缩短差分时间成为亟待解决的问题,也是扩大多层次保护配置的根本。目前,相关研究人员一直致力于研究差分协调问题。缩短差异时间主要从两个角度出发。一方面,缩短主要设备的动作时间。例如,真空断路器和磁性断路器等一次设备的发展缩短了断路器的动作时间;另一方面对保护算法进行了改进,以达到缩短保护装置差动的目的。为了提高保护的速度和可靠性,已经提出了多种先导保护方案[4-9]。文献[10]对高速飞行员保护进行了研究,保护算法依赖于暂态能量的方向比较,能够快速检测故障。文献[11]提出了一种自适应电流差动保护方案,以提高电力系统保护的可靠性和速度。在文献[12]中,作者提出了一种基于虚拟阻抗的新型导频保护方案。该方案不受不同故障条件的影响,并保持较高的可靠性。然而,上述保护算法需要用于数据传输的可靠通信信道[13]。通信信道的安装需要高的投资成本。行波保护利用故障电压和故障电流的行波特性实现最快的运行速度[14-17]。文献[18]为电网引入了一种新的行波保护算法。保护算法利用行波的传播特性实现超高速保护。在初始故障电流行波的基础上,提出了一种高速保护方案[19-20]。利用初始电流行波可以快速识别内部故障。尽管这些方案能够提高操作速度,但它们需要几百kHz的高采样率,这使得目前难以在配电系统中应用。
本文提出了一种基于采样值比较的配电线路快速保护新理论。所提出的快速电流保护需要更短的数据窗口,有助于更快的操作速度。
本文的其余部分安排如下:在第1节中,提出了快速电流保护的保护时间分析和一般思路。第2节给出了整定值模板曲线的构造。第3节描述了快速电流保护和可靠性分析的标准。第4节中,在PSCAD/EMTDC中开发了三相10 kV配电网仿真模型,以测试快速电流保护的性能。第5节给出了结论。
在传统保护算法中,保护装置通过A/D转换模块将电流信号转换为数字量。通过数字滤波器、快速傅里叶变换计算电流均方根值(root mean square,RMS)并和整定值比较,进一步识别故障。当通过快速傅里叶变换计算的电流的RMS值大于保护整定值时,保护立即发出动作信号。动作时间分析如图1所示。
图1所示,Iset为常规电流保护的整定值,iRMS为RMS电流,ik为故障电流。
系统故障发生的时间为t0,其对应的采样点为NF。此时,故障电流ik开始快速增加。然而,当保护装置计算全波快速傅里叶变换算法时,其数据窗口是当前时间前推的一个周期的采样数据。由于故障初始阶段的数据窗口包含大量非故障数据,计算的电流iRMS有效值不能立即反映实际故障电流。
图1中可以看出,当前iRMS的RMS并没有快速增加,而是随着数据窗口的移动在一定程度上呈现出爬升效应,这延迟了保护的实际动作时间。假设短路电流iRMS的有效值在时间t1大于保护整定值Iset,则此时对应的采样点为NDF。如果保护装置的采样频率为fs,则识别故障的延迟时间为 Δ t 1 = t 1 - t 0 = ( N D F - N F ) × 1 f s。延迟时间与保护整定值、故障电流、故障初始相角、故障类型等因素有关,一般超过0.5个周期,即大于10 ms。
通过对传统电流保护动作时间的分析可知,传统电流保护故障识别的延时主要与数据窗口长度、保护整定值等因素有关。如果可以缩短保护判断窗口的长度,并且可以将故障电流与反映多个点的整定值的序列进行动态比较,无疑将有效地解决保护算法固有延迟长的问题。因此,首次提出了一种新的快速电流保护思想:
1)在故障发生初期,直接选取含有大量故障信息的前半波采样数据进行比较,形成保护判据,可以大大加快保护动作的判断。
2)在故障发生的初始阶段,立即构建与保护装置具有相同采样频率的整定值模板曲线序列。整定值模板曲线序列主要显示保护整定值的特性。
3)采样值与低阈值的比较,构建了保护启动判据。通过将电流故障分量的采样值与整定值模板曲线序列进行比较,构建保护动作准则。
由于短路电流的故障分量幅值远大于负载突然增加时的故障分量幅值,因此可以通过设置电流故障分量的保护整定值来进行有效的区分。只有当电流的故障分量大于故障分量的整定值时,才能判断为内部故障,因此电流故障分量的采样值应同时大于故障分量的整定值模板曲线。传统电流故障分量保护的整定值为ΔIset,以 ( 0.85 0.95 ) 2 Δ I s e t  为幅值构造的模板曲线,既能避免负荷突然增大时的误判,又能始终保证在短路情况下,整定值模板曲线绝对值小于电流故障分量绝对值。分析过程如图2所示。
当前故障部件的初始相位将受到故障时间的影响。如果构建的整定值模板曲线不能与当前故障分量曲线相位对齐,则可能会影响保护比较的结果,从而降低保护辨别的速度和性能。理想情况下,整定值模板曲线和电流故障分量曲线应严格对齐,即过零点和极值点一一对应。
当前样本值是一系列离散数据。使用极值理论,以相等的间隔实时计算两个采样点之间的增量符号。增加表示为“+”,减少表示为“-”,所以“+”和“-”形成序列。初始预定峰值时间落在符号改变间隔内,并且与该间隔中的最大值相对应的时间是预定峰值时间tp。基于峰值时间tp将标准模板曲线的起点向前推T/4周期,然后以Iset2为振幅构建工频周期的整定值模板曲线:
i s ( t ) = I s e t 2 s i n ( 2 π f t ) t [ t p - T 4 , t p + 7 T 4 ]
其中 I s e t 2 = ( 0.85 0.95 ) 2 ( I s e t   - I e )
式中: I s e t 2为整定值模板曲线的振幅;Iset为常规电流速断保护算法的整定值;Ie为被保护线路的额定电流;T为标准正弦模板曲线周期。
在保护装置中,电流故障分量被转换为离散化的样本值,因此必须对整定值模板曲线进行离散化。为了确保可靠性,这里要求整定值模板曲线的离散化必须与当前故障分量采样相对应。通过离散化处理获得的离散值被设置为isk),计算过程如下式所示:
i s ( k + 1 ) = I s e t 2 s i n ( 2 π k N - α )         k = 0,1 , , N - 1
式中:isk+1)为对应于k+1点的采样值;k为整数;α为故障的初始相角。
考虑到电流故障分量的采样值易受高次谐波影响,可能存在1~2个分散的干扰点。如果由传统峰值算法搜索的预测峰值是干扰点,则根据干扰点时间构建的整定值模板曲线的相位将与当前故障分量的故障初始相位非常不同。比较多点样本值的结果将受到严重影响。
为了消除干扰点的影响,通过比较整定值模板曲线和故障分量样本值之间的相似度,使用欧几里德距离算法来确定峰值时间。欧几里德距离是两个或多个点之间最常见的距离表示算法。它在欧几里得空间中定义,其中两个n维向量a=(x11x12,…,x1n)和b=(x21x22,…,x2n)之间的欧几里德距离d12表示为
d 12 = k = 1 n ( x 1 k - x 2 k ) 2
欧氏距离相似性的具体步骤如下:
1)数据归一化。首先,通过“+”和“-”,初步预测峰值落在符号变化区间内。以预定极值ipeak作为归一化的准则,对故障分量采样值序列in)的每个点的幅度进行压缩,以获得归一化的故障分量采样序列inpu。同时,基于其预定极值的对应时间为峰值时间的工频正弦波形,建立具有相同采样频率、振幅为1的标准正弦序列yn)。
2)计算预测极值的欧氏距离相似度。标准正弦序列的起点是基于预测的极限时间tp向前推T/4周期。从起始点到预定极值之后的第3点的间隔构成比较间隔。
通过应用欧几里德距离算法,计算比较区间中故障分量和标准正弦序列的样本值之间的相似度B,如下式:
B = 1 / ( 1 + d )
其中
d = n = 1 k [ i ( n ) p u - y ( n ) ] 2
式中:d为欧几里得距离;k为比较区间中采样点的数量。
3)取最大相似度值与阈值Bset进行比较。当在比较区间中存在r个预定极值时,选择r个相似度中的最大相似度与阈值Bset进行比较。当满足下式时,预测极值被判断为正确的峰值。
m a x ( B 1 ,   B 2 , , B r ) > B s e t
考虑到裕度和精度,建议 B s e t值为0.8~0.9。
模板曲线离散化和欧氏相似性比较如图3所示。当故障分量采样过程中没有干扰点时,如图3a所示,通过“+”和“-” 序列,初步预测极值落在符号变化区间内。由于只有一个符号变化间隔(峰值ipeak),因此根据预定的极端时间构造振幅为1的标准正弦序列yn)。inpu是通过在峰值ipeak的基础上归一化样本值序列in)而获得的。使用欧氏距离相似度算法计算此时欧氏距离相似度B=1。因为它大于相似度阈值Bset,所以极值时间被称为峰值时间。
当采样过程中存在干扰点时,如图3b所示,通过“+”和“-’找到了对应于干扰点iobs和峰值ipeak的两个符号变化区间。使用iobsipeak作为预定极值,通过欧几里德相似度算法分别计算的欧几里德距离相似度为Bobs=0.215和Bpeak=1。最后,根据式(6)确定正确的极值时间。
对于给定的电流采样信号I,电流故障分量可以由下式给出[21]
I m ( k ) = I ( k ) - I ( k - 2 N )
式中:k为样本数;N为一个周期中的样本数;Ik)为当前样本;Ik-2N)为在当前样本前两个周期中的对应样本。
假设电流故障分量的初始M1采样值为Im(1),Im(2),…,ImM),保护启动标准如下:
1 M 1 i = 1 M 1 I m ( i ) > I s e t 1
式中:Iset1为低阈值,Iset1=0.05IeIe为受保护线路的额定电流;M1T/8中采样点的数量。
快速保护启动标准如图4所示。
在满足保护启动标准后,根据第2.3节所述的方法,根据欧氏距离相似性找到正确的极限时间tp,然后根据第2.2节构建整定值模板曲线Isk)的离散序列。
基于传统的电流保护,当故障发生在正保护范围内时,故障电流大于保护整定值。此时,对应的电流故障分量采样值Imk)必须大于整定值模板曲线Isk)的离散序列。保护动作标准如图5所示。
采样点k的比较结果为
r o c ( k ) = 1 | I m ( k ) | | I s ( k ) | 0
为了提高保护操作的可靠性,基于多点比较结果构建了保护操作准则,如下式所示:
R o c = k = 1 M 2 r o c ( k ) M 2 - m
式中:Roc为多点比较结果的总和;M2T/4中采样点的数量。
考虑到采样值中存在干扰数据,m=M2/3。如图6所示,多点采样值用于比较,可以在故障后的几个采样间隔内识别故障。根据保护标准和设置原则,保护装置可以在电源频率周期的1/4内识别故障。故障检测所需的时间为Δt2=t2-t0
图6中,t2为故障检测时刻,并且Δt2M2的大小有关。动作时间不超过0.5个工频周期。所提出算法的逻辑图如图7所示。
采样值差动保护还使用采样值构建故障识别的保护标准。它具有快速动作的特点,但也容易受到一些因素的影响。保护操作的主要负面因素包括样本值的干扰、衰减的DC分量、负载变化、电流互感器(current transformer,CT)饱和等[22]。因此,有必要讨论这些因素对本文提出的快速保护算法的影响。
对于采样值的干扰,在之前的分析中已经考虑了这个问题。采样值干扰对该方法的影响主要体现在模板曲线的构建和保护判断上。然而,结合欧氏距离相似性判断极值点是否被正确找到的新方法可以有效避免干扰点对模板曲线构建的影响。此外,保护判据中的m值可以有效避免对保护的误判。同时,当采样频率较高时,可以更好地解决这个问题[23]
衰减直流分量的存在将导致采样值偏移。如果采样值直接应用于保护标准,则会发生保护误操作。因此,当计算Imk)时,必须首先采用微分算法来消除DC分量。根据传统的微机保护装置,采用一点差分算法消除衰减的直流分量。
理论上,当断层的初始角度为0°,电流故障分量的第一波显著增加。当故障的初始角度为90°,电流故障分量的第一波增加不够明显。因此,考虑到故障初始角的影响,为确保所提保护算法的可靠性。将电流故障分量Iset2的保护整定值设置为 ( 0.85 0.95 ) 2 ( I s e t   - I e )
当负载电流增加时,将出现电流故障部件。考虑到重载电流较大,Imk)可能大于Iset1,并且满足保护启动标准。然而,传统电流保护的整定值考虑了负载变化的影响。因此,电流故障分量不会超过电流故障分量保护的整定值。Imk)<Isk),如图8所示。
CT饱和现象在常规电流保护中是不可避免的。配电网络中CT元件的阈值低于高压主网络中CT的阈值。当电流大于5~8 kA时,会出现CT饱和现象。这种现象导致保护动作时间增加。在这种情况下,CT将在电流通过零点时释放饱和磁通量,并输出正常的电流采样数据[24-25]。使用该正常电流采样数据可以识别故障,如图9所示。因此,本文提出的算法可以有效避免CT饱和的影响。
为了验证新型电流保护算法的有效性,在PSCAD/EMTDC平台上进行了大量仿真。10 kV配电网仿真模型如图10所示。系统频率为50 Hz,采样频率为10 kHz。根据仿真模型的系统参数,Iset1设置为0.025 kA,Iset设置为1.36 kA,Iset2设置为0.98 kA。在Matlab程序中使用继电器CB2位置的测量电流来实现新的保护方案。F1是内部故障点,F2是外部故障点。在模拟中,将各种故障条件设置为在F1F2处发生。
F1发生三相短路故障的情况下,传统电流保护和所提出算法的操作响应如图11所示。三相短路故障发生在60 ms,RMS故障电流IRMS从0.55 kA增加到1.94 kA,满足新型电流保护的动作标准,在63 ms时产生跳闸信号,而传统保护在75 ms时运行跳闸信号。在F1发生相间短路故障的情况下,传统电流保护和新型电流保护运行响应如图12所示。IRMS从0.55 kA增加到1.60 kA。满足新型电流保护的操作标准,在63 ms时产生跳闸信号,而传统保护在75.5 ms时产生跳闸信号。
分析了传统电流保护和新型电流保护在不同内部故障类型下的性能。不同故障类型对应的延迟时间如表1所示。表1表明,新型电流保护可以在故障开始后3 ms产生跳闸信号,而传统电流保护需要在故障开始后10~20 ms产生跳闸信号。因此,在不同内部故障类型的情况下,与传统算法相比,新型保护的时间延迟要短得多。
在不同的故障起始时刻,分析了传统电流保护和新型电流保护的性能。对应于不同故障起始时刻的操作响应如表2所示。从结果来看,在不同模拟条件下,新型电流保护可以在故障起始后5 ms识别故障。然而,常规电流保护需要10∼15 ms才能产生跳闸信号。因此,在不同内部故障起始时刻的条件下,与传统电流保护相比,新型电流保护的延迟时间要短得多。
F2发生三相短路故障的情况下,传统电流速断保护和新型电流保护的操作响应如图13所示。从结果来看,三相短路故障发生在60 ms时,IRMS从0.55 kA增加到0.99 kA。在这种情况下,由于|ΔI|和IRMS分别小于|Is|和Iset,所提出的算法和传统算法不会生成跳闸信号。在F2发生相间短路故障的情况下,保护操作结果如图14所示。从结果来看,在60 ms时发生相间短路,IRMS从0.55 kA增加到0.96 kA。类似地,所提出的算法和传统算法不会产生跳闸信号。
图15所示,在70 ms时,F1发生三相短路故障,通过改变电流互感器的参数实现CT饱和。参考图16,所提出的算法利用不饱和区中的采样点数据来识别故障,并在73 ms时产生跳闸信号。由于CT饱和的影响,传统电流保护在100 ms时产生一个跳闸信号,由于波形失真,IRMS降低,这导致了传统保护的延迟时间延长。因此,所提出的算法具有更好的抗CT饱和性能。
在负载变化的情况下,分析了两种保护算法的保护操作。如图17所示,负载电流在70 ms时变化,同时,IRMS从0.56 kA增加到0.87 kA,这比Iset小。如图18所示,两条曲线重合,所提出的算法和传统算法均不会产生跳闸信号。结果表明,所提出的算法和传统算法不受负载变化的影响。
图19所示,内部三相短路故障在60 ms时发生,并且高斯白噪声被添加到故障电流信号中。新型电流保护在故障开始后3 ms产生跳闸信号。然而,传统的电流保护在故障开始后需要12 ms才能产生跳闸信号。结果表明,该算法在噪声干扰条件下具有较好的工作特性。
为了缩短保护识别时间,一些专家和学者提出使用半波傅里叶算法和窄带滤波算法来计算RMS电流,以缩短故障识别时间。本文采用半波傅里叶算法和窄带滤波算法与改进的保护算法进行了比较。在F1处发生三相短路故障的情况下,半波傅里叶算法、窄带滤波算法和所提出算法的操作响应如图20所示。
三相短路故障发生在50 ms,RMS故障电流IRMS从0.55 kA增加到1.74 kA。所提出的算法在故障开始后5 ms产生跳闸信号,然而,半波傅里叶算法在故障开始后需要9.8 ms才能产生跳闸窄带滤波算法信号,窄带滤波算法需要6 ms才能产生跳闸信号。结果表明,与半波傅里叶算法和窄带滤波算法相比,所提出的保护算法的延迟时间更短。
提出了一种基于离散整定值的快速电流保护算法。所提出的算法利用整定值的离散序列与当前故障分量的采样值之间的比较来识别故障。通过多点比较结果的和,建立了保护启动判据和动作判据,提高了保护动作的可靠性。与传统的电流保护方案相比,新的保护算法具有更快的速度,并且具有良好的抗CT饱和、抗噪声干扰能力。仿真实验表明,该算法在不同故障条件下具有良好的运行特性。
  • 国家自然科学基金(52167010)
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2024年第54卷第8期
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doi: 10.19457/j.1001-2095.dqcd24915
  • 接收时间:2023-01-15
  • 首发时间:2025-12-09
  • 出版时间:2024-08-20
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  • 收稿日期:2023-01-15
  • 修回日期:2023-05-11
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国家自然科学基金(52167010)
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    国网湖北电力有限公司神农架供电公司,湖北 神农架 442400
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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