CN114611987B - 一种并网型风电场的局域振荡的辨识方法 - Google Patents

一种并网型风电场的局域振荡的辨识方法 Download PDF

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CN114611987B
CN114611987B CN202210307371.9A CN202210307371A CN114611987B CN 114611987 B CN114611987 B CN 114611987B CN 202210307371 A CN202210307371 A CN 202210307371A CN 114611987 B CN114611987 B CN 114611987B
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朱悦
薛晓岑
黄杰杰
张雷
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Abstract

本发明提供一种并网型风电场的局域振荡的辨识方法,将并网型风电场系统中的节点划分为弱风险节点和高风险节点,给出了并网型风电场系统的振荡局域传播强度指标的计算方法,根据振荡局域传播强度指标判断振荡是局域振荡或广域振荡。所提出的并网型风电场局域振荡的辨识方法能够有效地辨识并网型风电场的局域振荡、广域振荡,将并网型风电场划分为振荡区域和非振荡区域,便于有针对地部署振荡抑制措施。相比仅依靠能观性指标刻画局域/广域振荡,所提方法能够提高局域/广域振荡的辨识精度。

Description

一种并网型风电场的局域振荡的辨识方法
技术领域
本发明涉及风力发电场的电气控制技术领域,尤其涉及一种并网型风电场的局域振荡的辨识方法。
背景技术
对于电力电子变换器主导的风力发电场,系统内部元件不仅通过电气端口进行连接,还依赖于电力电子变换器实现交直流侧互联。已经有研究表明,交流/直流变换器通过控制可以实现交-直流动态解耦,即体现出“振荡防火墙”特性。该控制特性预期会对振荡的传播特性产生影响,如:所产生的振荡可能被阻断或限制在某一区域内,从而产生局域振荡现象。
局域/广域振荡本质上反映的是振荡能否可观,因此直观上可采用能观性指标进行刻画。但是,实际应用中发现,仅采用能观性指标刻画局域/广域振荡存在歧义,容易出现误判,需要提出一种精确判断并网型风电场局域/广域振荡的指标及方法。
发明内容
本发明的目的是为了解决如何辨识并网型风电场局域/广域振荡现象的难题,提出振荡局域传播强度指标。
为了实现上述目的,本发明采用了如下技术方案:
一种并网型风电场的局域振荡的辨识方法,包括以下步骤:
S1:在旋转dq坐标系下,建立含N个节点的并网型风电场的节点导纳矩阵Ynode(s),令节点导纳矩阵对应的行列式为零,求出系统的闭环特征值;根据特征值实部判断风电场的稳定,若存在实部为正值的特征值,表示风电场运行失稳,风电场的振荡频率ω为特征值的虚部;
S2:将振荡频率ω代入并网型风电场的节点导纳矩阵,求出含N个节点的并网型风电场系统中各个节点的能观度Obs1、Obs2、……、ObsN;
S3:对并网型风电场系统中的节点进行划分,将各个节点的能观度Obs1、Obs2、……、ObsN与阈值0.00001进行比较,若某节点的能观度Obsn(1≤n≤N)小于等于阈值0.00001,则该节点n为弱风险节点,否则,该节点为高风险节点;
S4:构建置换矩阵Pπ,若并网型风电场系统中含有m个弱风险节点、N-m个高风险节点,这m个弱风险节点的节点号分别为π(1)、π(2)、……、π(m),N-m个高风险节点的节点号分别为π(m)、π(m+1)、……、π(N),那么含N个节点的并网型风电场系统的置换π的映射关系为
Figure GDA0003890501100000021
置换矩阵Pπ的表达式为
Figure GDA0003890501100000022
其中eπ(m)为标准基,代表长度为N的行向量,其中仅有第π(m)列元素为1,而其余元素均为0;
S5:对节点导纳矩阵进行置换运算如下:
Figure GDA0003890501100000031
其中
Figure GDA0003890501100000032
为节点导纳矩阵在振荡频率ω处的逆矩阵,
Figure GDA0003890501100000033
为置换矩阵Pπ的转置矩阵,
Figure GDA0003890501100000034
为经过重新排布后在振荡频率ω处的节点阻抗矩阵,ZR-L为经过重新排布后的弱风险区域矩阵,ZR-H为经过重新排布后的高风险区域矩阵,Zoff-P、Zoff-N为经过重新排布后弱风险、高风险区域之间的交互耦合项;
S6:定义频率为ω的振荡局域传播强度指标kP(w),其表达式为:
Figure GDA0003890501100000035
其中,|| ||2表示2范数,即欧几里得范数;
S7:根据振荡局域传播强度指标kP(w)对局域振荡进行量化评估,若kP(w)=0,系统呈现局域振荡特性;若kP(w)≠0,系统呈现广域振荡特性。
与现有技术方案相比,本申请所提出的并网型风电场局域振荡指标及辨识方法具有如下优势:
能够有效地辨识并网型风电场的局域振荡、广域振荡,将并网型风电场划分为振荡区域和非振荡区域,便于有针对地部署振荡抑制措施;相比仅依靠能观性指标刻画局域/广域振荡,所提方法能够提高局域/广域振荡的辨识精度。
附图说明
图1为本发明一个实施例—并网型风电场局域振荡辨识方法的流程图。
具体实施方式
为使本发明的目的、技术方案和优点更加清楚,以下结合具体实施例,对本发明作进一步地详细说明。
在下面的描述中阐述了很多具体细节以便于充分理解本发明,但是,本发明还可以采用不同于在此描述的其他方式来实施,因此,本发明并不限于下面公开说明书的具体实施例的限制。
请参阅图1,本发明一个实施例本发明一个实施例—并网型风电场局域振荡辨识方法的流程图,包括以下步骤:
S1:在旋转dq坐标系下,建立含15个节点的并网型风电场的节点导纳矩阵Ynode(s),令节点导纳矩阵对应的行列式为零,求出系统的闭环特征值,存在一对右半平面特征值:0.0561+50.6j,可见存在实部为正值的特征值,表示风电场运行失稳,风电场的振荡频率ω为特征值的虚部,即50.6rad/s;
S2:将振荡频率50.6rad/s代入并网型风电场的节点导纳矩阵,求出含15个节点的并网型风电场系统中各个节点的能观度Obs1、Obs2、……、Obs15如下表所示:
Figure GDA0003890501100000051
S3:对并网型风电场系统中的节点进行划分,将各个节点的能观度Obs1、Obs2、……、ObsN与阈值0.00001进行比较,若某节点的能观度Obsn(1≤n≤N)小于等于阈值0.00001,则该节点n为弱风险节点,否则,该节点为高风险节点;经过比较,可知节点#1~#3,#7~#12为弱风险节点,剩余节点#4~#6、#13~#15为高风险节点;
S4:构建置换矩阵Pπ,并网型风电场系统中含有9个弱风险节点、6个高风险节点,这9个弱风险节点的节点号分别为1、2、3、7、8、9、10、11、12,6个高风险节点的节点号分别为4、5、6、13、14、15,那么含15个节点的并网型风电场系统的置换π的映射关系为
Figure GDA0003890501100000052
置换矩阵Pπ的表达式为
Figure GDA0003890501100000061
S5:对节点导纳矩阵进行置换运算如下:
Figure GDA0003890501100000062
其中
Figure GDA0003890501100000063
为节点导纳矩阵在振荡频率ω处的逆矩阵,
Figure GDA0003890501100000064
为置换矩阵Pπ的转置矩阵,
Figure GDA0003890501100000065
为经过重新排布后在振荡频率ω处的节点阻抗矩阵,ZR-L为经过重新排布后的弱风险区域矩阵,ZR-H为经过重新排布后的高风险区域矩阵,Zoff-P、Zoff-N为经过重新排布后弱风险、高风险区域之间的交互耦合项;
S6:定义频率为ω的振荡局域传播强度指标kP(w),其表达式为:
Figure GDA0003890501100000066
其中,|| ||2表示2范数,即欧几里得范数;
将频率为50.6rad/s的振荡代入,计算出振荡局域传播强度指标kP(50.6)值为0;
S7:根据振荡局域传播强度指标kP(w)对局域振荡进行量化评估,由于振荡局域传播强度指标kP(50.6)值等于0,系统呈现局域振荡特性,说明高风险区域振荡向弱风险区域的传播通道被阻断,弱风险区域保持稳定。
综上所述,申请所提出的并网型风电场局域振荡指标及辨识方法能够有效地辨识并网型风电场的局域振荡、广域振荡,将并网型风电场划分为振荡区域和非振荡区域,便于有针对地部署振荡抑制措施;相比仅依靠能观性指标刻画局域/广域振荡,所提方法能够提高局域/广域振荡的辨识精度。
以上所述仅为本发明的实施例而已,并不用于限制本发明,对于本领域的技术人员来说,本发明可以有各种更改和变化。凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的权利要求范围。

Claims (1)

1.一种并网型风电场的局域振荡的辨识方法,其特征在于,包括以下步骤:
S1:在旋转dq坐标系下,建立含N个节点的并网型风电场的节点导纳矩阵Ynode(s),令节点导纳矩阵对应的行列式为零,求出系统的闭环特征值;根据特征值实部判断风电场的稳定,若存在实部为正值的特征值,表示风电场运行失稳,风电场的振荡频率ω为特征值的虚部;
S2:将振荡频率ω代入并网型风电场的节点导纳矩阵,求出含N个节点的并网型风电场系统中各个节点的能观度Obs1、Obs2、……、ObsN;
S3:对并网型风电场系统中的节点进行划分,将各个节点的能观度Obs1、Obs2、……、ObsN与阈值0.00001进行比较,若某节点的能观度Obsn(1≤n≤N)小于等于阈值0.00001,则该节点n为弱风险节点,否则,该节点为高风险节点;
S4:构建置换矩阵Pπ,若并网型风电场系统中含有m个弱风险节点、N-m个高风险节点,这m个弱风险节点的节点号分别为π(1)、π(2)、……、π(m),N-m个高风险节点的节点号分别为π(m)、π(m+1)、……、π(N),那么含N个节点的并网型风电场系统的置换π的映射关系为
Figure FDA0003890501090000011
置换矩阵Pπ的表达式为
Figure FDA0003890501090000021
其中eπ(m)为标准基,代表长度为N的行向量,其中仅有第π(m)列元素为1,而其余元素均为0;
S5:对节点导纳矩阵进行置换运算如下:
Figure FDA0003890501090000022
其中
Figure FDA0003890501090000023
为节点导纳矩阵在振荡频率ω处的逆矩阵,
Figure FDA0003890501090000024
为置换矩阵Pπ的转置矩阵,
Figure FDA0003890501090000025
为经过重新排布后在振荡频率ω处的节点阻抗矩阵,ZR-L为经过重新排布后的弱风险区域矩阵,ZR-H为经过重新排布后的高风险区域矩阵,Zoff-P、Zoff-N为经过重新排布后弱风险、高风险区域之间的交互耦合项;
S6:定义频率为ω的振荡局域传播强度指标kP(w),其表达式为:
Figure FDA0003890501090000026
其中,|| ||2表示2范数,即欧几里得范数;
S7:根据振荡局域传播强度指标kP(w)对局域振荡进行量化评估,若kP(w)=0,系统呈现局域振荡特性;若kP(w)≠0,系统呈现广域振荡特性。
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112381671A (zh) * 2020-11-30 2021-02-19 华北电力科学研究院有限责任公司 一种新能源场站并网电力系统的宽频振荡风险评估方法
CN113644689A (zh) * 2021-08-16 2021-11-12 清华大学 风电系统运行稳定域的构建方法、装置、电子设备及其可读存储介质

Family Cites Families (2)

* Cited by examiner, † Cited by third party
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CN110425092B (zh) * 2019-07-30 2020-06-16 华北电力大学 一种基于能量频谱的风电并网系统振荡源定位系统及方法
CN113156247B (zh) * 2021-04-23 2024-04-23 北京建筑大学 一种电力系统低频振荡的预警方法及装置

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112381671A (zh) * 2020-11-30 2021-02-19 华北电力科学研究院有限责任公司 一种新能源场站并网电力系统的宽频振荡风险评估方法
CN113644689A (zh) * 2021-08-16 2021-11-12 清华大学 风电系统运行稳定域的构建方法、装置、电子设备及其可读存储介质

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
虚拟同步发电机的输入阻抗建模及稳定性分析;高娟 等;《电网技术》;20210228;第578-586页 *
风电并网系统次同步振荡的频域模式分析;占颖等;《电力系统自动化》;20200917(第18期);第124-131页 *

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