Article(id=1206314802058707253, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1206314797411418409, articleNumber=null, orderNo=null, doi=10.19457/j.1001-2095.dqcd25193, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1687190400000, receivedDateStr=2023-06-20, revisedDate=1687708800000, revisedDateStr=2023-06-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1765537461056, onlineDateStr=2025-12-12, pubDate=1710864000000, pubDateStr=2024-03-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1765537461056, onlineIssueDateStr=2025-12-12, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1765537461055, creator=13701087609, updateTime=1765537461055, updator=13701087609, issue=Issue{id=1206314797411418409, tenantId=1146029695717560320, journalId=1189987059142926344, year='2024', volume='54', issue='3', pageStart='3', pageEnd='96', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=-1, specialIssue=null, createTime=1765537459937, creator=13701087609, updateTime=1765766624077, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1207275981480477057, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1206314797411418409, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1207275981480477058, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1206314797411418409, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=68, endPage=75, ext={EN=ArticleExt(id=1206314802851430712, articleId=1206314802058707253, tenantId=1146029695717560320, journalId=1189987059142926344, language=EN, title=Identification of Voltage Saddle Node Bifurcation Point in Power System Based on Quadratic Form Approximation Method, columnId=null, journalTitle=Electric Drive, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to intervale evaluate saddle node bifurcation points(SNB) in a power system during load growth quickly and accurately, a quadratic approximation method to directly calculate the power system voltage collapse point of was proposed. Based on the approximate quadratic PV-curve of output PQ nodes in the system, load parameters were introduced into the node power balance equation, by using the complex function derivative rules to differentiate power equation twice, the first and second derivatives of node voltage to load parameters were derived theoretically, and the expression of PV curve function was further determined, then vertex coordinates were used to determine the initial position of saddle junction bifurcation point in the power system and the voltage collapse point was approximated by multiple iterations of convergence. The proposed method avoids multiple power flow calculations using the continuous power flow method,significantly reducing computational complexity. Simulation results on IEEE 14 and IEEE 118 bus systems demonstrate the effectiveness of this method. Compared with the supplemented P'Q node method and Thevenin's equivalent method, the quadratic form iterative approximation method has higher computational efficiency and robustness.

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为实现负荷增长过程中电力系统鞍结分岔点(SNB)的快速准确识别,提出一种直接计算电力系统电压崩溃点的二次型迭代逼近方法,基于系统中PQ节点输出的PV曲线为近似二次型的特点,在节点功率平衡方程中引入负荷增长参数,运用复合函数求导法则就功率方程进行两次求导,理论推导节点电压对负荷参数的一阶、二阶导数表达式,由此确定PV曲线二项式,依靠顶点坐标确定电力系统鞍结分岔点的初始位置,经多次迭代收敛逼近电压崩溃点。所提方法避免了连续潮流法的多次潮流计算,可显著降低计算量。以IEEE 14,IEEE 118节点系统进行仿真验证,证明了该方法的有效性,相较增补P'Q节点法及戴维南等值法,二次型迭代逼近法具有较高的计算效率和鲁棒性。

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张俊林(1998—),男,硕士研究生,主要研究方向为电力系统运行与控制,Email:

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张俊林(1998—),男,硕士研究生,主要研究方向为电力系统运行与控制,Email:

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张俊林(1998—),男,硕士研究生,主要研究方向为电力系统运行与控制,Email:

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Proceedings of the CSEE, 2022, 42(17):6239-6247., articleTitle=A conic optimization method for voltage stability assessment of distribution network under uncertainty, refAbstract=null), Reference(id=1207275591003362000, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1206314802058707253, doi=null, pmid=null, pmcid=null, year=2016, volume=40, issue=2, pageStart=69, pageEnd=76, url=null, language=null, rfNumber=[24], rfOrder=43, authorNames=方斯顿, 程浩忠, 徐国栋, journalName=电力系统自动化, refType=null, unstructuredReference=方斯顿, 程浩忠, 徐国栋, 等. 基于曲面二次标准型逼近的最近电压稳定临界点求取方法[J]. 电力系统自动化, 2016, 40(2):69-76., articleTitle=基于曲面二次标准型逼近的最近电压稳定临界点求取方法, refAbstract=null), Reference(id=1207275591099830994, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1206314802058707253, doi=null, pmid=null, pmcid=null, year=2016, volume=40, issue=2, pageStart=69, pageEnd=76, url=null, language=null, rfNumber=[24], rfOrder=44, authorNames=FANG Sidun, CHENG Haozhong, XU Guodong, journalName=Automation of Electric Power Systems, refType=null, unstructuredReference=FANG Sidun, CHENG Haozhong, XU Guodong, et al. Solving of closest critical point on voltage stability limit surface based on second-order standard approximation of limit surface[J]. 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IEEE 14-bus system node iteration data

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节点 迭代次数 λ Pmax(标幺值) V C(标幺植) Δ V %
5号 1 2.556 5.112 0.820 0.250
2 2.800 5.600 0.760 0.078
3 3.012 6.024 0.710 0.070
4 3.134 6.268 0.680 0.044
5 3.134 6.268 0.680 0.000
9号 1 2.330 4.660 0.850 0.176
2 2.656 5.312 0.790 0.075
3 2.934 5.868 0.740 0.067
4 3.023 6.046 0.719 0.029
5 3.023 6.046 0.719 0.000
14号 1 2.522 5.044 0.783 0.277
2 2.812 5.624 0.741 0.056
3 3.089 6.178 0.712 0.041
4 3.234 6.468 0.694 0.025
5 3.234 6.468 0.694 0.000
), ArticleFig(id=1207275583306814007, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1206314802058707253, language=CN, label=表1, caption=

IEEE 14系统各节点迭代数据

, figureFileSmall=null, figureFileBig=null, tableContent=
节点 迭代次数 λ Pmax(标幺值) V C(标幺植) Δ V %
5号 1 2.556 5.112 0.820 0.250
2 2.800 5.600 0.760 0.078
3 3.012 6.024 0.710 0.070
4 3.134 6.268 0.680 0.044
5 3.134 6.268 0.680 0.000
9号 1 2.330 4.660 0.850 0.176
2 2.656 5.312 0.790 0.075
3 2.934 5.868 0.740 0.067
4 3.023 6.046 0.719 0.029
5 3.023 6.046 0.719 0.000
14号 1 2.522 5.044 0.783 0.277
2 2.812 5.624 0.741 0.056
3 3.089 6.178 0.712 0.041
4 3.234 6.468 0.694 0.025
5 3.234 6.468 0.694 0.000
), ArticleFig(id=1207275583386505787, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1206314802058707253, language=EN, label=Tab.2, caption=

Comparison of data of three methods

, figureFileSmall=null, figureFileBig=null, tableContent=
节点系统 方法 λ C V C(标幺植) Pmax/
MW
计算
用时/
s
IEEE 14
(9号
节点)
二次型逼近法 3.023 0.719 181.38 0.049 3
连续
潮流法
3.034 0.718 182.04 4.481 0
导纳法 2.900 0.700 174.00 0.152 8
IEEE 118(66号
节点)
二次型逼近法 1.435 0.855 1 139.15 0.065 4
连续
潮流法
1.466 0.850 1 149.34 3.896 6
导纳法 1.368 0.833 1 072.51 0.267 5
), ArticleFig(id=1207275583491363393, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1206314802058707253, language=CN, label=表2, caption=

3种方法各数据对比

, figureFileSmall=null, figureFileBig=null, tableContent=
节点系统 方法 λ C V C(标幺植) Pmax/
MW
计算
用时/
s
IEEE 14
(9号
节点)
二次型逼近法 3.023 0.719 181.38 0.049 3
连续
潮流法
3.034 0.718 182.04 4.481 0
导纳法 2.900 0.700 174.00 0.152 8
IEEE 118(66号
节点)
二次型逼近法 1.435 0.855 1 139.15 0.065 4
连续
潮流法
1.466 0.850 1 149.34 3.896 6
导纳法 1.368 0.833 1 072.51 0.267 5
), ArticleFig(id=1207275583575249477, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1206314802058707253, language=EN, label=Tab.3, caption=

IEEE 118-bus system node 88 calculation results

, figureFileSmall=null, figureFileBig=null, tableContent=
λ C V C(标幺值) Pmax/MW 计算用时/s
连续潮流法 1.535 0.832 736.80 4.785 0
二次型逼近法 1.534 0.834 736.32 0.063 5
戴维南等值法[19] 1.520 0.850 729.60 0.135 4
增补P'Q点法[22] 1.531 0.842 734.88 0.235 4
), ArticleFig(id=1207275583650746953, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1206314802058707253, language=CN, label=表3, caption=

IEEE 118节点系统88号节点计算结果

, figureFileSmall=null, figureFileBig=null, tableContent=
λ C V C(标幺值) Pmax/MW 计算用时/s
连续潮流法 1.535 0.832 736.80 4.785 0
二次型逼近法 1.534 0.834 736.32 0.063 5
戴维南等值法[19] 1.520 0.850 729.60 0.135 4
增补P'Q点法[22] 1.531 0.842 734.88 0.235 4
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基于二次型迭代逼近法的电力系统电压鞍结分岔点识别
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张俊林 , 倪良华 , 孙嘉 , 吕干云 , 张金华
电气传动 | 综合能源与现代电网 2024,54(3): 68-75
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电气传动 | 综合能源与现代电网 2024, 54(3): 68-75
基于二次型迭代逼近法的电力系统电压鞍结分岔点识别
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张俊林 , 倪良华, 孙嘉, 吕干云, 张金华
作者信息
  • 南京工程学院 电力工程学院,江苏 南京 211167
  • 张俊林(1998—),男,硕士研究生,主要研究方向为电力系统运行与控制,Email:

Identification of Voltage Saddle Node Bifurcation Point in Power System Based on Quadratic Form Approximation Method
Junlin ZHANG , Lianghua NI, Jia SUN, Ganyun LÜ, Jinhua ZHANG
Affiliations
  • School of Electric Power Engineering,Nanjing Institute of Technology,Nanjing 211167,Jiangsu,China
出版时间: 2024-03-20 doi: 10.19457/j.1001-2095.dqcd25193
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为实现负荷增长过程中电力系统鞍结分岔点(SNB)的快速准确识别,提出一种直接计算电力系统电压崩溃点的二次型迭代逼近方法,基于系统中PQ节点输出的PV曲线为近似二次型的特点,在节点功率平衡方程中引入负荷增长参数,运用复合函数求导法则就功率方程进行两次求导,理论推导节点电压对负荷参数的一阶、二阶导数表达式,由此确定PV曲线二项式,依靠顶点坐标确定电力系统鞍结分岔点的初始位置,经多次迭代收敛逼近电压崩溃点。所提方法避免了连续潮流法的多次潮流计算,可显著降低计算量。以IEEE 14,IEEE 118节点系统进行仿真验证,证明了该方法的有效性,相较增补P'Q节点法及戴维南等值法,二次型迭代逼近法具有较高的计算效率和鲁棒性。

鞍结分岔点  /  负荷参数  /  PV曲线  /  迭代逼近

In order to intervale evaluate saddle node bifurcation points(SNB) in a power system during load growth quickly and accurately, a quadratic approximation method to directly calculate the power system voltage collapse point of was proposed. Based on the approximate quadratic PV-curve of output PQ nodes in the system, load parameters were introduced into the node power balance equation, by using the complex function derivative rules to differentiate power equation twice, the first and second derivatives of node voltage to load parameters were derived theoretically, and the expression of PV curve function was further determined, then vertex coordinates were used to determine the initial position of saddle junction bifurcation point in the power system and the voltage collapse point was approximated by multiple iterations of convergence. The proposed method avoids multiple power flow calculations using the continuous power flow method,significantly reducing computational complexity. Simulation results on IEEE 14 and IEEE 118 bus systems demonstrate the effectiveness of this method. Compared with the supplemented P'Q node method and Thevenin's equivalent method, the quadratic form iterative approximation method has higher computational efficiency and robustness.

saddle node bifurcation point(SNB)  /  loading parameter  /  PV curve  /  quadratic approximation
张俊林, 倪良华, 孙嘉, 吕干云, 张金华. 基于二次型迭代逼近法的电力系统电压鞍结分岔点识别. 电气传动, 2024 , 54 (3) : 68 -75 . DOI: 10.19457/j.1001-2095.dqcd25193
Junlin ZHANG, Lianghua NI, Jia SUN, Ganyun LÜ, Jinhua ZHANG. Identification of Voltage Saddle Node Bifurcation Point in Power System Based on Quadratic Form Approximation Method[J]. Electric Drive, 2024 , 54 (3) : 68 -75 . DOI: 10.19457/j.1001-2095.dqcd25193
由负荷持续动荡造成区域电网的大面积停电事故引发了计算电力系统电压崩溃点的热潮。随着新能源的接入,电网运行复杂程度不断上升,电力系统的电压稳定极限也随之波动,因此研究系统电压稳定性成为持续的热门话题[1-4]。通过引入动力学中的分岔理论,定义电压鞍结分岔点(SNB)的概念,即系统平衡失解处,也称为电压崩溃点,研究SNB的计算方法是现代电力系统安全分析的重要内容[5-6]
传统的牛顿拉夫逊法(N-R power flow)在逐步计算靠近系统电压崩溃点的过程中,其雅可比矩阵会出现在SNB附近奇异的问题,导致算法不收敛[7-8]。现有计算SNB的方法可分为两类,一类为间接计算法,另一类为直接计算法。
间接计算法中,连续潮流法[9](continuous power flow,CPF)的提出,使得雅克比矩阵在崩溃点处奇异的问题得到解决,连续潮流法通过引入负荷增长参数 λ,使雅可比矩阵维数增加一维;通过控制步长得到大量数据点,能够绘制较为完整的PV曲线。近些年来,CPF在求取SNB的过程中逐步得到优化,计算精度和效率不断提高[10-13],但是CPF需逐点计算,程序运行时间及计算量较大。导纳法[14](admittance method)通过对常规雅可比矩阵在SNB处奇异原因进行深入分析研究,将重负荷等效替代为节点的自导纳,极大地降低了雅可比矩阵随着负荷逐步增加而奇异的速度,并通过算例验证了导纳法的可行性。导纳法的提出大幅提升了计算电力系统电压SNB的速度,但其建立模型过程中忽略较多条件,计算精度不高。
直接计算法中,文献[15]提出了一种基于广域量测的单时刻潮流求解戴维南参数的方法,并将发电机无功出力越限作为限制条件[16]。文献[17]运用当前运行点及其邻域点来自不同数据源的数据,在线快速跟踪戴维南等效参数,在动态等效阻抗法的基础上提出了一种改进方法[18-20],但可能存在系统辨识数据不良的情况。文献[21]提出一种利用PQ-PV节点转换的方法获得PV曲线,进而衍生出P'Q节点定义,求出电压崩溃点。增补P'Q节点法[22]能够精确计算得到系统的SNB,但由于修改后的潮流方程系数矩阵因子表较为复杂,因此计算速度较为缓慢,且存在不收敛的问题。
鉴于以上分析,本文提出一种直接计算电力系统电压崩溃点的二次型迭代逼近法。首先,该算法引入负荷参数 λ,理论推导具有二次型关系[23-24] λ V二项式中的各项系数表达式,通过对引入 λ的潮流功率方程进行两次求导,得到表达式系数所需的 d V i / d λ d 2 V i / d λ 2的值,并整理得到二阶导数系数矩阵表达式,再由 λ V曲线顶点坐标确定电压崩溃点的初始位置。为提高计算精度,将所得结果作为下一次计算的起点,采用迭代思想逐步逼近崩溃点精确位置。通过设置电压收敛判据保证迭代结果趋于稳定。最后通过算例验证方法的可行性和有效性,并同其他方法作对比分析。
连续潮流法引入负荷参数 λ,通过在平衡方程失解处的雅可比矩阵增加一维解决了奇异的问题,使得方程变为有解。本文引入相同的思想,在常规潮流方程中引入 λ,建立如下节点功率方程模型:
P i = d p λ P i 0 = V i 2 G i i + V i j = 1 j i j = n V j ( G i j c o s δ i j - B i j s i n δ i j )
Q i = d q λ Q i 0 = - V i 2 B i i + V i j = 1 j i j = n V j ( G i j s i n δ i j - B i j c o s δ i j )
式中: P i , Q i分别为节点输出的有功功率和无功功率; V i为节点电压幅值; G i j , B i j分别为节点的互电导和互电纳; G i i , B i i分别为节点的自电导和自电纳; δ i j为节点之间的相角差; P i 0 , Q i 0分别为节点输出有功功率和无功功率的初始潮流计算值; d p , d q分别为节点有功功率和无功功率增长梯度; λ为节点负荷增长参数。
负荷增长梯度 d p , d q体现负荷的变化特性,重负荷地区负荷增长梯度较大,轻负荷地区负荷增长梯度较小,系统不同位置节点的 d p , d q值可视具体负荷变化特点设置不同的值。
观察式(1)、式(2),将系统某个节点i V i δ i λ的关系定义为两个抽象函数:
V i = f ( λ ) δ i = g ( λ )
依据现有研究,可采用二次函数来拟合生成PV曲线,建立 λV的二次函数关系式:
λ = a V 2 + b V + c
式中:abc分别为二次函数关系式各项系数。
根据抽象函数和复合函数求导法则推导二次函数各项系数,对式(4)两边V求导得:
d λ / d V = 2 a V + b
由于PV曲线的上半支系单调函数,满足反函数求导法则,由式(5)得到V λ的一阶导数:
d V d λ = 1 2 a V + b
就式(6)两边对 λ再次求导:
d 2 V d λ 2 = d d λ ( d V d λ ) = ( d V / d λ ) V d V d λ = - 2 a 2 a V + b 2 d V d λ = - 2 a ( d V d λ ) 3
联立式(4)、式(6)、式(7)三式,在获得节点的V d V / d λ d 2 V / d λ 2值的前提下,可得到二次函数三项系数abc的表达式:
a = - d 2 V / d λ 2 2 d V / d λ 3
b = 1 d V / d λ - 2 a V
c = λ - a V 2 - b V
再根据二次函数的顶点坐标,得到电压鞍结分岔点对应的电压值 V C及极限负荷参数 λ C的初始值:
V C = - b 2 a
λ C = a V C 2 + b V C + c
为逼近电压崩溃点的精确值,将得到的结果作为下一次计算的起点,经反复计算迭代,输出每次迭代结果。二次型迭代逼近法的原理如图1所示。
就式(1)、式(2)等式两边对 λ求一阶导数:
d P i d λ = d p P i 0 = i = 1 n ( P i δ j d δ j d λ + P i V j d V j d λ )
d Q i d λ = d q Q i 0 = i = 1 n ( Q i δ j d δ j d λ + Q i V j d V j d λ )
式中: V j δ j分别为PQ节点的电压幅值和相角;n为系统PQ节点个数。
式(13)、式(14)两个方程涉及到潮流解的雅可比矩阵,可以用矩阵形式表示为
d p P i 0 d q Q i 0 = J d δ i d λ d V i d λ
式中:J为潮流方程的雅可比矩阵。
为了方便表示求解所需的一阶导数值,式(15)还可以写为
d δ i d λ d V i d λ = J - 1 d p P i 0 d q Q i 0
式中: J - 1为雅可比矩阵的逆矩阵。
求得 V δ λ的一阶导数后,再对式(15)两边求 λ的二阶导数,由于 V δ λ的抽象复合函数,因此应用抽象复合函数求二阶导数法则求导:
d d λ ( d P i d λ ) = d d λ d p P i 0 = 0 = d d λ j = 1 N ( P i δ j d δ j d λ + P i V j d V j d λ ) = j = 1 N [ d d λ ( P i δ j ) d δ j d λ + P i δ j d d λ ( d δ j d λ ) + d d λ ( P i V j ) d V j d λ + P i V j d d λ ( d V j d λ ) ] = j = 1 N [ ( 2 P i δ j 2 d δ j d λ + 2 P i δ j V j d V j d λ ) d δ j d λ + P i δ j d 2 δ j d λ 2 + ( 2 P i V j 2 d V j d λ + 2 P i V j δ j d δ j d λ ) d V j d λ + P i V j d 2 V j d λ 2 ]
d d λ ( d Q i d λ ) = d d λ ( d q Q i 0 ) = 0 = d d λ j = 1 N ( Q i δ j d δ j d λ + Q i V j d V j d λ ) = j = 1 N [ d d λ ( Q i δ j ) d δ j d λ + Q i δ j d d λ ( d δ j d λ ) + d d λ ( Q i V j ) d V j d λ + Q i V j d d λ ( d V j d λ ) ] = j = 1 N [ ( 2 Q i δ j 2 d δ j d λ + 2 Q i δ j V j d V j d λ ) d δ j d λ + Q i δ j d 2 δ j d λ 2 + ( 2 Q i V j 2 d V j d λ + 2 Q i V j δ j d δ j d λ ) d V j d λ + Q i V j d 2 V j d λ 2 ]
上述方程包含对 λ的一阶偏导数项、二阶偏导数项和混合项,这些项都有各自的系数,经整理,结果可表示为
0 = J d 2 δ i d λ 2 d 2 V i d λ 2 + A ( d δ i d λ ) 2 ( d V i d λ ) 2 + B d δ i d λ d V i d λ d V i d λ d δ i d λ
式中:AB为各偏导数项的系数矩阵。
展开AB系数矩阵,可表示为
A = A 11 A 12 A 21 A 22
B = B 11 B 12 B 21 B 22
其中
A 11 = 2 P i δ j 2 A 12 = 2 P i V j 2 A 21 = 2 Q i δ j 2 A 22 = 2 Q i V j 2
B 11 = 2 P i δ j V j B 12 = 2 P i V j δ j B 21 = 2 Q i δ j V j B 22 = 2 Q i V j δ j
式中:A11A12A21A22B11B12B21B22为雅克比矩阵元素HNKL的衍生式。
J表示为
J = H N K L
其中,各元素具体表达式如下:
1) i j时,有:
H i j = P i δ j = V i V j ( G i j s i n δ i j - B i j c o s δ i j ) K i j = Q i δ j = - V i V j ( G i j c o s δ i j + B i j s i n δ i j ) N i j = P i V j = V i ( G i j c o s δ i j + B i j s i n δ i j ) L i j = Q i V j = V i ( G i j s i n δ i j - B i j c o s δ i j )
2) i = j时,有:
H i i = P i δ i = V i j = 1 j i j = n V j ( G i j s i n δ i j - B i j c o s δ i j ) K i i = Q i δ i = V i j = 1 j i j = n V j ( G i j c o s δ i j + B i j s i n δ i j ) N i i = P i V i = j = 1 j i j = n V j ( G i j c o s δ i j + B i j s i n δ i j ) + 2 G i i V i L i i = Q i V i = j = 1 j i j = n V j ( G i j s i n δ i j - B i j c o s δ i j ) - 2 B i i V i
利用式(25)、式(26)继续推导矩阵AB矩阵各元素的表达式如下:
1) i j时,有:
A 11 = 2 P i / δ j 2 = V i V j ( B i j s i n δ i j - G i j c o s δ i j ) A 12 = 2 P i / V j 2 = 0 A 21 = 2 Q i / δ j 2 = - H i j A 22 = 2 Q i / V j 2 = 0 B 11 = 2 P i / δ j V j = H i j / V j B 12 = 2 P i / V j δ j = H i j / V i B 21 = 2 Q i / δ j V j = H i j / V j B 22 = 2 Q i / V j δ j = - N i j
2) i = j时,有:
A 11 = 2 P i / δ i 2 = - J i i A 12 = 2 P i / V i 2 = 2 G i i A 21 = 2 Q i / δ i 2 = H i i A 22 = 2 Q i / V i 2 = - 2 B i i B 11 = 2 P i / δ i V i = H i j / V i B 12 = 2 P i / V i δ i = - H i j / V i B 21 = 2 Q i / δ i V i = K i i / V i B 22 = 2 Q i / V i δ i = K i i / V i
为了判定二次型迭代逼近法计算过程是否收敛,定义系统的收敛判据为 Δ V %,计算式如下:
Δ V % = | ( V m + 1 - V m ) / V m | × 100 % ε
式中:m为迭代次数; V m为第m次迭代后的电压幅值; V m + 1为第m+1次迭代后的电压幅值; ε为收敛精度,本文取0.01。
二次型逼近法步骤如图2所示。
二次型迭代逼近法求解某系统节点的电压鞍结分岔点的步骤如下:
1)根据给定的电力系统数据,对系统进行常规潮流计算,得到系统各节点电压,并设定各节点 d p d q值;
2)通过式(16)计算各节点的一阶导数项,即 d δ / d λ d V / d λ的值;
3)利用式(19)计算各节点的二阶导数 d 2 δ / d λ 2 d 2 V / d λ 2的值;
4)将步骤2)计算得到的一阶导数值 d V / d λ和步骤3)中计算得到的二阶导数值 d 2 V / d λ 2代入式(8)~式(10)中,计算得到二项式各项系数;
5)利用式(11)、式(12)计算电压崩溃点处的 λ C V C值;
6)从迭代第2次开始检查收敛判据 Δ V %是否小于0.01,若否,则以式(12)计算所得的 λ C V C作为下一次迭代的初始条件,迭代至 Δ V %小于0.01,输出目标节点的 V C值与 P m a x值。迭代过程中若PV节点发生无功越限,则转化为PQ节点。
仿真采用Matlab以及Matpower编程,输出各类图形。仿真计算中,设定功率的基准值为100 MV·A,各节点 d p = 2.0 d q = 1.5
取IEEE 14节点系统中5,9,14号节点为目标节点,代入系统数据,进行初始潮流计算,将计算得到的数据按照第2节中计算SNB的步骤进行迭代,对3个节点每次得到的P值与V值进行记录,输出每次迭代的 P V曲线,如图3a图3b图3c所示,将各节点P值转化为对应的 λ值,并得到迭代次数与 λ之间的关系曲线,如图3d所示。
图3a~图3c给出了5次迭代的曲线结果,3个节点的逼近过程呈一致性。从图3d中可看出,对于不同节点,迭代3至5次,电压崩溃点的计算结果趋于稳定,验证了二次型迭代逼近法的可行性。
为研究负荷增长梯度对电压崩溃点的影响,通过设置不同 d p , d q的取值,计算得到各取值下IEEE 14节点系统5号节点的SNB值,结果如图4所示,在 d p = 2.0 d q = 1.5附近时,负荷增长梯度取值对电压崩溃点的影响较小,因此本文后续取 d p = 2.0 d q = 1.5
表1为上述分析的各节点每次迭代时 λPmax V C Δ V %的数据。数据表明,经多次迭代, λVC变化趋于稳定,在电压收敛判据满足小于0.01时,节点的电压幅值与最大输出功率几乎不再发生变化,验证了二次型迭代逼近法的有效性。
图5给出了系数矩阵JAB在迭代过程中最小奇异值的变化情况。从图中可以看出,在迭代至5次左右时奇异值趋于稳定,表明每次迭代计算的 d V / d λ d 2 V / d λ 2均为可求解的,克服了计算电压崩溃点附近雅克比矩阵奇异的问题。
以IEEE 14节点系统9号节点、IEEE 118节点系统66号节点为研究目标,对比二次型迭代逼近法、连续潮流法和导纳法3种方法的计算结果。
对于连续潮流法,设置其步长为0.02,采用局部参数化方法,跟踪检测PV曲线的生成;对于导纳法,设置步长为0.01,将输出功率超100 MW的节点设置为重负荷节点,并转化为节点的自导纳,进行潮流计算并输出PV曲线;对于二次型迭代逼近法,设置 d p = 2.0 d q = 1.5,获取最后一次迭代的PV曲线。各节点的计算结果如图6表2所示。
图6表2中数据表明,相较于精确度较高的连续潮流法,二次型迭代逼近法计算的电压幅值与功率极限点的误差很小。连续潮流法的缺点是计算量大、计算时间长,由3种方法的运算时间可以看出,二次型迭代逼近法的运算时间是连续潮流法运算时间的几十分之一到几百分之一。导纳法虽然也有较高的计算速度,但其计算精度偏低,因此本文提出的二次型逼近法具有计算精度高、计算速度快两方面优点。
以IEEE 118节点系统88号节点为研究目标,进一步验证二次型逼近法的优越性。将二次型逼近法与其他3种直接法进行比较,表3列出了4种方法的计算结果。
表3中数据表明,虽然增补P'Q节点法也具有较高的计算精度,但其计算用时大约是二次型迭代逼近法的3~4倍。戴维南等值法的计算用时大约为二次型迭代逼近法的2倍,但计算精度不如二次型迭代逼近法。因此无论在计算用时还是计算精度方面,二次型迭代逼近法都具有较好的应用优势。
本文提出一种直接计算电力系统鞍结分岔点的二次型迭代逼近法,算例验证了二次型迭代逼近法的可行性和高效性。综上所述,结论如下:
1)二次型迭代逼近法克服了连续潮流法计算量大的缺点,迭代5次左右即达SNB,大大减少了计算量,计算速度快且计算精度高,算法具有优越性。
2)算法在迭代过程中产生的一阶导数 d V i / d λ和二阶导数 d 2 V i / d λ 2反映了PV曲线的斜率及曲率,后续可加以研究利用。
3)本文推导的基于二次型迭代逼近法计算电力系统电压崩溃点的数学模型具有一般性,可扩展应用于电网的静态电压安全分析。
在未来的研究中,将进一步考虑新能源接入对电力系统电压稳定性的影响。
  • 国家自然科学基金(52107098)
  • 江苏省研究生科研与实践创新计划项目(SJCX22_1085)
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doi: 10.19457/j.1001-2095.dqcd25193
  • 接收时间:2023-06-20
  • 首发时间:2025-12-12
  • 出版时间:2024-03-20
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  • 收稿日期:2023-06-20
  • 修回日期:2023-06-26
基金
国家自然科学基金(52107098)
江苏省研究生科研与实践创新计划项目(SJCX22_1085)
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    南京工程学院 电力工程学院,江苏 南京 211167
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