Article(id=1200482404403114992, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1200482403828495344, articleNumber=null, orderNo=null, doi=10.19457/j.1001-2095.dqcd25121, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1683216000000, receivedDateStr=2023-05-05, revisedDate=1685462400000, revisedDateStr=2023-05-31, acceptedDate=null, acceptedDateStr=null, onlineDate=1764146909094, onlineDateStr=2025-11-26, pubDate=1718812800000, pubDateStr=2024-06-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1764146909094, onlineIssueDateStr=2025-11-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1764146909094, creator=13701087609, updateTime=1764146909094, updator=13701087609, issue=Issue{id=1200482403828495344, tenantId=1146029695717560320, journalId=1189987059142926344, year='2024', volume='54', issue='6', 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=1764146908957, creator=13701087609, updateTime=1764224882080, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1200809446868898278, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1200482403828495344, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1200809446868898279, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1200482403828495344, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=83, endPage=88, ext={EN=ArticleExt(id=1200482404604441586, articleId=1200482404403114992, tenantId=1146029695717560320, journalId=1189987059142926344, language=EN, title=Identification Technology of Bipolar Short Circuit Fault in High Voltage DC Power Grid Considering Instantaneous Power Characteristics, columnId=null, journalTitle=Electric Drive, columnName=null, runingTitle=null, highlight=null, articleAbstract=

During the operation of high-voltage DC power grid,due to factors such as load characteristics and voltage fluctuations,the instantaneous power characteristics such as capacitor current and harmonics are more complex,which increases the difficulty of identifying faults in the power grid system. Therefore,a bipolar short circuit fault identification technology for high-voltage DC power grids considering instantaneous power characteristics was proposed. By establishing a topology model of the high-voltage DC power grid,the bipolar short circuit fault of the high-voltage DC power grid was anlyzed. Based on the instantaneous power charac-teristics of the endpoints of the high-voltage DC power grid,the area where the bipolar short circuit fault occurs was determined. Using the maximum value positioning method of dual terminal fault current to obtain bipolar short circuit fault location,the fault identification of bipolar short circuit in high-voltage DC power grid was completed. The experimental results show that the proposed method has high accuracy in locating and identifying bipolar short circuit faults in high-voltage DC power grids,and the efficiency of fault identification is improved.

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高压直流电网运行过程中,由于受到负载特性和电压波动等因素影响,电容电流及谐波等瞬时功率特性较为复杂,增加了对电网系统故障的识别难度。为此,提出考虑瞬时功率特性的高压直流电网双极短路故障识别技术。通过建立高压直流电网拓扑结构模型,分析高压直流电网的双极短路故障;结合高压直流电网端点的瞬时功率特性判断出双极短路故障发生区域。利用双端故障电流最大值定位法获取双极短路故障定位,完成对高压直流电网双极短路的故障识别。实验结果表明,所提方法对高压直流电网双极短路故障定位识别精度较高,提升了故障识别效率。

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覃刚(1997—),男,本科,助理工程师,Email:

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覃刚(1997—),男,本科,助理工程师,Email:

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考虑瞬时功率特性的高压直流电网双极短路故障识别技术
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覃刚 , 张昌庆 , 姜雪娇 , 钟磊 , 周朝俊 , 吴民
电气传动 | 可靠性与诊断 2024,54(6): 83-88
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电气传动 | 可靠性与诊断 2024, 54(6): 83-88
考虑瞬时功率特性的高压直流电网双极短路故障识别技术
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覃刚 , 张昌庆, 姜雪娇, 钟磊, 周朝俊, 吴民
作者信息
  • 海南电网有限责任公司,海南 海口 570100
  • 覃刚(1997—),男,本科,助理工程师,Email:

Identification Technology of Bipolar Short Circuit Fault in High Voltage DC Power Grid Considering Instantaneous Power Characteristics
Gang QIN , Changqing ZHANG, Xuejiao JIANG, Lei ZHONG, Chaojun ZHOU, Min WU
Affiliations
  • Hainan Power Grid Corporation Ltd.,Haikou 570100,Hainan,China
出版时间: 2024-06-20 doi: 10.19457/j.1001-2095.dqcd25121
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高压直流电网运行过程中,由于受到负载特性和电压波动等因素影响,电容电流及谐波等瞬时功率特性较为复杂,增加了对电网系统故障的识别难度。为此,提出考虑瞬时功率特性的高压直流电网双极短路故障识别技术。通过建立高压直流电网拓扑结构模型,分析高压直流电网的双极短路故障;结合高压直流电网端点的瞬时功率特性判断出双极短路故障发生区域。利用双端故障电流最大值定位法获取双极短路故障定位,完成对高压直流电网双极短路的故障识别。实验结果表明,所提方法对高压直流电网双极短路故障定位识别精度较高,提升了故障识别效率。

高压直流电网  /  瞬时功率  /  双极短路  /  电流最大值定位法  /  模块化多电平换流器  /  故障识别

During the operation of high-voltage DC power grid,due to factors such as load characteristics and voltage fluctuations,the instantaneous power characteristics such as capacitor current and harmonics are more complex,which increases the difficulty of identifying faults in the power grid system. Therefore,a bipolar short circuit fault identification technology for high-voltage DC power grids considering instantaneous power characteristics was proposed. By establishing a topology model of the high-voltage DC power grid,the bipolar short circuit fault of the high-voltage DC power grid was anlyzed. Based on the instantaneous power charac-teristics of the endpoints of the high-voltage DC power grid,the area where the bipolar short circuit fault occurs was determined. Using the maximum value positioning method of dual terminal fault current to obtain bipolar short circuit fault location,the fault identification of bipolar short circuit in high-voltage DC power grid was completed. The experimental results show that the proposed method has high accuracy in locating and identifying bipolar short circuit faults in high-voltage DC power grids,and the efficiency of fault identification is improved.

high-voltage DC power grid  /  instantaneous power  /  bipolar short circuit  /  maximum current positioning method  /  modular multilevel converter(MMC)  /  fault identification
覃刚, 张昌庆, 姜雪娇, 钟磊, 周朝俊, 吴民. 考虑瞬时功率特性的高压直流电网双极短路故障识别技术. 电气传动, 2024 , 54 (6) : 83 -88 . DOI: 10.19457/j.1001-2095.dqcd25121
Gang QIN, Changqing ZHANG, Xuejiao JIANG, Lei ZHONG, Chaojun ZHOU, Min WU. Identification Technology of Bipolar Short Circuit Fault in High Voltage DC Power Grid Considering Instantaneous Power Characteristics[J]. Electric Drive, 2024 , 54 (6) : 83 -88 . DOI: 10.19457/j.1001-2095.dqcd25121
高压直流电网是一种利用半导体功率电子器件将交流电转换成高压直流之后,再通过高压直流输电线路将电能输送到较远距离的电力系统,它在跨越长距离的电力传输中具有重要作用[1-2]。在高压直流输电系统中,整流器是将交流电转换为直流电的设备,其输出直流电的特性会对系统的瞬时功率特性产生重要影响,尤其在设备老化及恶劣天气等因素的共同作用下,会对电网系统的安全运行产生极大负面影响,并且高压直流电网的内部运行特性更加复杂,其故障的排查和处理也较为困难,更加需要运用高效准确的故障识别技术。
王振浩等[3]建立MMC-HVDC电网的双极短路等效模型,通过同步挤压小波变换方法获取电网故障电压的高频分量,利用压缩感知算法求取电流稀疏向量的最优解,获取电网双极短路故障定位,但该方法存在故障定位精度低的问题。李博通等[4]建立直流配电网拓扑结构模型,分析直流配电网工作原理和直流电网母线虚拟电压特性。基于母线虚拟电压方法,完成高压直流电网的双极短路故障元件识别。该方法存在故障识别准确度低的问题。李璐璐等[5]在建立三相可控整流器模型后,设计了电流环和电压环,利用惯性权重改进粒子群算法优化了粒子参数,虽然提高了相控型整流器的控制能力,但该方法仍然存在计算算力较低的问题。陈涛等[6]以全控型整流器为研究对象,分析混合谐波抑制机理,设计最优抑制条件后并将混合谐波抑制方法应用其中,虽然提高了整流器电能质量,但该方法还是存在延迟问题。
为了解决上述方法中存在的问题,本文提出考虑瞬时功率特性的高压直流电网双极短路故障识别技术。
首先,建立高压直流电网拓扑结构模型,用于高压直流电网双极短路故障的分析。建立由4个模块化多电平换流器(MMC)组成的高压直流电网[7-9],其高压直流电网拓扑结构模型如图1所示。
图1中,A,B端为新能源馈入站,C,D端为火电机接入站,4个端口组成“口”字结构,在每条线路的两端安装直流断路器和平波电阻器。模块化多电平换流器结构如图2所示。
图2中,Ufv为直流侧电压,UaUbUc为交流侧的三相电压,L1为每个桥臂的总抗阻,R1为每个桥臂子模块中开关损耗等效电阻,Lx为平波电抗器。
瞬时功率特性是指在短时间内的发电机、变压器或电动机等电气设备的瞬时功率变化情况。瞬时功率特性通常与设备的电路结构、负载特性、工作环境等因素有关。在高压直流电网中,由于设备和负荷的特性,电流和功率的瞬时特性会随着时间的推移而发生变化,并在一定程度上反映电路的工作状态。当双极短路故障发生时,电流和功率的瞬时特性的变化将表现出明显的不规律性,这可以用来识别故障的位置和类型。为此,在高压直流电网线路端口处安装保护装置,根据端点的瞬时功率判断高压直流电网双极短路故障发生在内区还是外区。瞬时功率特性的双极短路故障内区、外区等效电路如图3所示。
设置安装保护装置的高压直流电网端口为cu[10-12]。安装保护装置的高压直流电网端口m的瞬时功率 Δ P m公式表达如下:
Δ P m = Δ u m ( t ) Δ i m ( t )
式中: Δ u m ( t )为端口m的瞬时电压值; Δ i m ( t )为端口m的瞬时电流值。
两个相邻端口的瞬时功率差 Δ P公式表达如下式所示:
Δ P = Δ P m - Δ P m + 1
当高压直流电网处于正常运行状态时,端口瞬时功率值为0,当高压直流电网处于故障状态时,可将瞬时功率作为双极短路故障内区、外区故障判定的重要依据[13-14]
当高压直流发生外区故障时,高压直流电网等效电路图见图3a所示。
当发生双极短路故障时,线路两端电压呈迅速下降的状态,此时会有故障叠加电流ig 流过安装保护装置的电网端口c和端口u,电网端口c和端口u的电流 i c i u变为 i c ' ( t ) - i u ' ( t ),如下式:
i c ' ( t ) = i c ( t ) + i g ( t ) - i u ' ( t ) = - i u ( t ) + i g ( t )
发生双极短路故障后,线路两端电压压降 u Z ' ( t )公式表达如下:
u Z ' ( t ) = R 1 i c ( t ) + R 2 [ - i u ( t ) ] + ( R 1 + R 2 ) i g ( t ) + L 1 d i c ' ( t ) d t + L 2 d [ - i u ( t ) ] d t
式中: R 1 R 2为直流线路等效电阻; L 1 L 2为直流线路等效电抗。
可以推导出发生故障时,线路电压增量公式表达如下:
Δ u Z ( t ) = R 1 i g ( t ) + R 2 i g ( t ) + L 1 d i c ( t ) ' d t + L 2 d [ - i u ( t ) ' d t
c u两端瞬时电压 Δ u c 0 Δ u u 0 | Δ u c | | Δ u u |,电网发生双极短路故障在外区。
发生双极短路故障后,直流线路两端的放电电流公式表达如下:
i c ( t ) ' = i c ( t ) + i g ( t ) - 1 2 i v ( t ) [ - i u ( t ) ' ] = [ - i u ( t ) ] + i g ( t ) + 1 2 i v ( t )
式中: i v ( t )为等效电容充电电流。
c u两端瞬时电流 Δ i c ( t ) 0 Δ [ - i u ( t ) ] 0 | Δ i c ( t )   | | Δ [ - i u ( t ) ] |时,电网发生双极短路故障在外区,此时的瞬时功率大于0。
当高压直流发生内区故障时,高压直流电网等效电路图见图2b。可将故障点看成故障处连接了一个电压源 u g,即故障叠加电压。根据2.1节外区故障推理可知,当 c u两端瞬时电压 Δ u c 0 Δ u u 0 Δ i c ( t ) 0 Δ [ - i u ( t ) ] 0时,电网发生双极短路故障在内区,此时,高压直流电网的瞬时功率值小于0。
在完成高压直流电网双极短路故障发生的内、外区判断后,进一步基于双端故障电流最大值定位法获取双极短路故障定位。当检测出线路 l 1发生双极短路故障后,直流断路器断开、模块化多电平换流器(MMC)迅速闭锁,此刻,记录电网中心调控云监测到的MMC1和MMC2的电流值与电压值[15]。根据电网双极短路故障的暂态特性分析可知:故障电流在电容完全放电后达到最大值,据此测量故障电流的最大值,随后,通过MMC1和MMC2端测量的非同步最大电流值和电压值完成故障定位。
为了避免同时投入多个MMC子模块造成两模块间反向充电的问题,采用一次投入一个MMC子模块的方法来完成测量,从而获得更高精度的故障定位[16-17]。完成MMC1端的第一个子模块的最大故障电流值及电压、电容值后,再将第2个子模块投入,直至投入完所有的子模块。MMC2端测量方法同MMC1端,获取所有最大故障电压、电流值后,再开始故障定位计算。发生双极短路故障线路 l 1闭锁前的等效电路如图4所示。
图4中,电容等效值 C 1公式表达如下:
C 1 = 6 C N
式中: N为MMC子模块个数; C为单位长度线路的电容。
电感等效值 L 1公式表达如下:
L 1 = ( 2 3 L d 1 + 2 L d 2 ) 2 c 0 l 0
式中: L d 1为桥臂电抗器电感; L d 2为平波电抗器电感; c 0为MMC1端距离故障点的距离; l 0为线路长度。
电阻等效值 R 1公式表达如下:
R 1 = 2 / 3 R q + 2 c 0 r 0 + R g
式中: R q为桥臂电阻; R g为故障回路过渡电阻; r 0为单位长度线路电阻值。
根据故障等效电路图可知,故障两端MMC呈TLC(triple-level cell)串联电路状态,可推导出电路关系公式表达如下:
L 1 C 1 d U c d t 2 + R 1 C 1 d U c d t 2 + U c = 0
式中: U c为MMC1端电容电压。
当高压直流电网发生双极短路故障时,电容处于放电状态,故障电路放电过程的微分方程公式表达如下:
ε = R 1 2 L 1 ξ 2 = 1 L 1 C 1 - ( R 1 2 L 1 ) 2 ξ 0 = ε 2 + ξ 2 χ = a r c t a n ξ ε
式中: ε为衰减系数; ξ为衰减振荡角频率; ξ 0为初始衰减系数; χ为衰减系数与衰减振荡角频率的反正切函数。
进一步推导出放电过程微分方程的特征值 a 1,2公式表达如下:
a 1,2 = - ε ± j ξ
MMC1端电容的瞬间电压 u c公式表达如下:
u c = U c ξ 0 ξ e - ε t s i n ( ξ t + χ )
电容瞬间电流 i c公式表达如下:
i c = U c ξ L 1 e - ε t s i n ( ξ t )
电感瞬间电压公式表达如下:
U Z = - U c ξ 0 ξ e - ε t s i n ( ξ t - χ )
根据MMC各个子模块在电容放电过程中测得的电压值和最大电流值,可以得到公式表达如下:
U 1 I m a x 1 = 2 3 R 1 + R g + 2 c 0 r 0 U 2 I m a x 2 = 2 3 R 1 + R g + 2 L 1 c 0 r 0
式中: U 1 U 2分别为MMC1和MMC2端测得的电压; I m a x 1 I m a x 2分别为MMC1和MMC2端测得的最大电流。
最后得到过渡电阻 R h公式表达如下:
R h = U 1 2 I m a x 1 + U 2 2 I m a x 2 - 2 3 R 1
故障测距 d公式表达如下:
d = U 1 4 R 0 I m a x 1 - U 2 4 R 0 I m a x 2 + l 0 2
式中: R 0为子模块中开关导通损耗等效电阻。
基于此,完成高压直流电网双极短路故障的定位,实现最终的故障识别。
为了验证考虑瞬时功率特性的高压直流电网双极短路故障识别技术的有效性,对其展开测试,实验具体过程如下:
步骤1:对高压直流电网拓扑结构进行分析。
步骤2:根据 cu两端瞬时电流和电压状况,识别瞬时功率特性的双极短路故障。
步骤3:检测线路l1是否发生双极短路故障,若发生短路故障,则测量故障电流的最大值。
步骤4:根据故障等效电路图获取故障两端MMC的TLC串联电路状态。
步骤5:通过MMC1和MMC2端测量的非同步最大电流值和电压值完成故障定位。
在Windows7 Intel core i7-4210H 8GB操作系统、编程环境为python3.7的计算机上,使用PSCAD软件搭建高压直流电网作为实验对象完成实验分析。实验平台如图5所示。
采用所提方法(考虑瞬时功率特性的高压直流电网双极短路故障识别方法)、文献[3]方法(基于迭代软阈值压缩感知理论的高压直流电网双极短路故障识别方法)和文献[4]方法(基于母线虚拟电压的高压直流电网双极短路故障识别方法)完成实验测试。
在搭建的高压直流电网的内、外区设置双极短路故障,采用所提方法、文献[3]方法和文献[4]方法识别双极短路故障,记录3种方法的双极短路故障的内、外区判断结果如图6所示。
分析图6可知,功率大于0的实验样本处于外区故障,功率小于0的实验样本处于内区故障。与文献[3]、文献[4]方法相比,所提方法的双极短路故障内、外区判断故障功率与实际故障区接近。由此可知,所提方法的双极短路故障内、外分区故障判断正确,更利于双极短路故障定位识别。由于所提方法考虑了瞬时功率特性,从而精准判断了高压直流电网故障的内、外区。
在搭建的高压直流电网设置双极短路故障,采用所提方法、文献[3]方法和文献[4]方法,识别双极短路故障,记录3种方法的双极短路故障定位识别精度如图7所示。分析图7可知,与文献[3]方法和文献[4]方法相比,所提方法的双极短路故障距离与实际故障更接近,表明所提方法的双极短路故障定位识别精度较高。所提方法基于双端故障电流最大值定位法,获取双极短路故障定位,提高了双极短路故障定位识别精度。
采用所提方法、文献[3]方法和文献[4]方法对搭建的高压直流电网实行双极短路故障识别,记录3种方法的双极短路故障识别时间如图8所示。分析图8可知,随着实验次数的增加,3种方法的双极短路故障识别时间随之增加。当实验次数达到10次时,文献[3]方法和文献[4]方法的双极短路故障识别时间分别为106.3 ms和99.6 ms,而所提方法的识别时间仅为78.5 ms。由此可知,所提方法的双极短路故障识别时间较短。
随着新能源技术的发展,具有高效输电性能和远距离传输性能的高压直流输电技术进一步得到广泛应用。其中,高压直流电网双极短路故障识别技术成为了当下电力系统研究的重点课题之一。文章通过建立高压直流电网拓扑结构模型,能够准确判断高压直流电网双极短路故障区域,完成了对高压直流电网双极短路故障的识别,该方法不仅具有较高的故障定位识别精度,还能有效缩短故障识别时间。
  • 中国南方电网有限责任公司科技项目(073000KK52180005)
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2024年第54卷第6期
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doi: 10.19457/j.1001-2095.dqcd25121
  • 接收时间:2023-05-05
  • 首发时间:2025-11-26
  • 出版时间:2024-06-20
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  • 收稿日期:2023-05-05
  • 修回日期:2023-05-31
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中国南方电网有限责任公司科技项目(073000KK52180005)
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    海南电网有限责任公司,海南 海口 570100
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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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