基于滑窗FFT的次同步振荡时变幅频在线监测方法

doi:10.11930/j.issn.1004-9649.201901118

中国电力 ›› 2020, Vol. 53 ›› Issue (11): 139-146.DOI: 10.11930/j.issn.1004-9649.201901118

基于滑窗FFT的次同步振荡时变幅频在线监测方法

杨京¹, 王彤¹, 唐俊刺²

1. 华北电力大学新能源电力系统国家重点实验室，北京 102206;
2. 国网辽宁省电力有限公司，辽宁沈阳 110006

收稿日期:2019-01-30 修回日期:2019-07-19 出版日期:2020-11-05 发布日期:2020-11-05
通讯作者: 国家自然科学基金资助项目(基于智能电网多维信息融合的系统安全保护研究，51637005)；国家电网有限公司总部科技项目(系统保护动作行为全过程智能分析、控制决策与评价关键技术研究)
作者简介:杨京(1994—)，男，硕士研究生，从事新能源电力系统保护与控制研究，E-mail：763034978@qq.com;王彤(1985—)，女，通信作者，博士，副教授，从事新能源电力系统保护与控制研究，E-mail：hdwangtong@126.com
基金资助:
This work is supported by National Natural Science Foundation of China (Research on System Security Protection Based on Multidimensional Information Fusion of Smart Grid, No. 51637005) and Science and Technology Project of SGCC (Research on Key Technologies of Intelligent Analysis, Control Decision and Evaluation in the Whole Process of System Protection Action Behavior)

Subsynchronous Oscillation Time-varying Amplitude Frequency On-Line Monitoring Method Based on Sliding Window FFT

YANG Jing¹, WANG Tong¹, TANG Junci²

1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;
2. State Grid Liaoning Electric Power Supply Co. Ltd., Shenyang 110006, China

Received:2019-01-30 Revised:2019-07-19 Online:2020-11-05 Published:2020-11-05

摘要/Abstract

摘要： 提出基于滑窗FFT（快速傅立叶变换）的次同步振荡时变幅频监测方法，对振荡信号的时变模态参数进行在线辨识。首先采用加窗插值方法减少频谱泄露和栅栏效应，降低FFT辨识误差。然后，通过时间窗的滑动对每个时间窗截取的信号进行FFT，得到振荡频率和振荡幅值的动态序列，即频率和幅值随时间变化关系，通过对时变振荡幅值的分析计算得到衰减因子的动态序列。最后，以理想非平稳信号、仿真信号及电网实测信号作为测试算例，通过与Prony和HHT算法的对比分析，表明该方法不仅不受模态混叠现象影响，而且具有一定抗噪能力，能够有效辨识随机时变振荡模态，实现次同步振荡在线监测分析。

关键词: 次同步振荡, 时变幅频, 模态辨识, 滑窗, FFT

Abstract: This paper proposes a time-varying amplitude frequency monitoring method based on sliding window FFT of sub-synchronous oscillation to identify the time-varying modal parameters of the oscillating signals on-line. Firstly, windowed interpolation methods are used to reduce the spectral leakage and fence effect, and decrease the FFT identification errors. Then, through the sliding of the time windows, the signals intercepted by each time window is transformed through FFT to obtain a dynamic sequence of oscillation frequency and oscillation amplitude, that is, varying frequency and amplitude with time. The dynamic sequence of the damping factors is obtained by analyzing and calculating the time-varying oscillation amplitude. Finally, using the ideal non-stationary signals, simulation signals and the measured signals of the power grid as test cases, the comparison results with the Prony and HHT algorithms show that this method not only can eliminate the influence of modal mixing, but also has anti-noise ability. The method can effectively identify the random time-varying oscillation modes and achieve on-line monitoring and analysis of sub-synchronous oscillations.

Key words: sub-synchronous oscillation, time-varying amplitude frequency, modal identification, sliding window, FFT

杨京, 王彤, 唐俊刺. 基于滑窗FFT的次同步振荡时变幅频在线监测方法[J]. 中国电力, 2020, 53(11): 139-146.

YANG Jing, WANG Tong, TANG Junci. Subsynchronous Oscillation Time-varying Amplitude Frequency On-Line Monitoring Method Based on Sliding Window FFT[J]. Electric Power, 2020, 53(11): 139-146.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201901118

https://www.electricpower.com.cn/CN/Y2020/V53/I11/139

参考文献

[1] 潘尔生, 李晖, 肖晋宇, 等. 考虑大范围多种类能源互补的中国西部清洁能源开发外送研究[J]. 中国电力, 2018, 51(9): 158-164
PAN Ersheng, LI Hui, XIAO Jinyu, et al. Research on the development and transmission of clean energy in Western China considering wide range coordination of multi-energy[J]. Electric Power, 2018, 51(9): 158-164
[2] 裴哲义, 丁杰, 李晨, 等. 分布式光伏并网问题分析与建议[J]. 中国电力, 2018, 51(10): 80-87
PEI Zheyi, DING Jie, LI Chen, et al. Analysis and suggestion for distributed photovoltaic generation[J]. Electric Power, 2018, 51(10): 80-87
[3] 董晓亮, 谢小荣, 杨煜, 等. 双馈风机串补输电系统次同步谐振影响因素及稳定区域分析[J]. 电网技术, 2015, 39(1): 189-193
DONG Xiaoliang, XIE Xiaorong, YANG Yu, et al. Impacting factors and stable area analysis of subsynchronous resonance in DFIG based wind farms connected to series-compensated power system[J]. Power System Technology, 2015, 39(1): 189-193
[4] 李明节, 于钊, 许涛, 等. 新能源并网系统引发的复杂振荡问题及其对策研究[J]. 电网技术, 2017, 41(4): 1035-1042
LI Mingjie, YU Zhao, XU Tao, et al. Study of complex oscillation caused by renewable energy integration and its solution[J]. Power System Technology, 2017, 41(4): 1035-1042
[5] 肖湘宁, 罗超, 廖坤玉. 新能源电力系统次同步振荡问题研究综述[J]. 电工技术学报, 2017, 32(6): 85-97
XIAO Xiangning, LUO Chao, LIAO Kunyu. Review of the research on subsynchronous oscillation issues in electric power system with renewable energy sources[J]. Transactions of China Electrotechnical Society, 2017, 32(6): 85-97
[6] 王伟胜, 张冲, 何国庆, 等. 大规模风电场并网系统次同步振荡研究综述[J]. 电网技术, 2017, 41(4): 1050-1060
WANG Weisheng, ZHANG Chong, HE Guoqing, et al. Overview of research on subsynchronous oscillations in large-scale wind farm integrated system[J]. Power System Technology, 2017, 41(4): 1050-1060
[7] 伍凌云, 李兴源, 孙衢, 等. 基于Prony辨识的复杂交直流系统次同步振荡特性分析[J]. 四川大学学报(工程科学版), 2008, 40(3): 155-160
WU Lingyun, LI Xingyuan, SUN Qu, et al. Subsynchronous oscillation analysis based on prony identification method[J]. Journal of Sichuan University (Engineering Science Edition), 2008, 40(3): 155-160
[8] 李安娜, 吴熙, 蒋平, 等. 基于形态滤波和Prony算法的低频振荡模式辨识的研究[J]. 电力系统保护与控制, 2015, 43(3): 137-142
LI Anna, WU Xi, JIANG Ping, et al. Research on identifying low frequency oscillation modes based on morphological filtering theory and Prony algorithm[J]. Power System Protection and Control, 2015, 43(3): 137-142
[9] 李宽, 李兴源, 赵睿, 等. 基于希尔伯特—黄变换的次同步振荡检测研究[J]. 华东电力, 2012, 40(1): 62-66
LI Kuan, LI Xingyuan, ZHAO Rui, et al. Subsynchronous oscillation detection base on the Hilbert-Huang transform method[J]. East China Electric Power, 2012, 40(1): 62-66
[10] 蔡国伟, 杨德友, 张俊丰, 等. 基于实测信号的电力系统低频振荡模态辨识[J]. 电网技术, 2011, 35(1): 59-65
CAI Guowei, YANG Deyou, ZHANG Junfeng, et al. Mode identification of power system low-frequency oscillation based on measured signal[J]. Power System Technology, 2011, 35(1): 59-65
[11] 郭成, 李群湛. Prony算法辨识传递函数的模型阶数选取研究[J]. 系统仿真学报, 2009, 21(22): 7042-7044, 7047
GUO Cheng, LI Qunzhan. Research on model order selection of transfer function identification using prony method[J]. Journal of System Simulation, 2009, 21(22): 7042-7044, 7047
[12] 杨玉坤, 杨明玉. Prony算法在谐波、间谐波参数辨识中的应用[J]. 电力系统及其自动化学报, 2012, 24(3): 121-126
YANG Yukun, YANG Mingyu. Application of prony algorithm in parameter identification of harmonics and inter-harmonics[J]. Proceedings of the CSU-EPSA, 2012, 24(3): 121-126
[13] 徐艳春, 陈国训, 李振兴, 等. 基于HHT的电能质量多扰动信号检测方法[J]. 电力科学与技术学报, 2016, 31(1): 55-61
XU Yanchun, CHEN Guoxun, LI Zhenxing, et al. Power quality disturbance signal detection method based on HHT[J]. Journal of Electric Power Science and Technology, 2016, 31(1): 55-61
[14] 阮有兴(Nguyen Huu Hung). 基于希尔伯特—黄变换的移动荷载作用下桥梁健康监测方法研究 [D]. 长春: 吉林大学, 2013.
Nguyen Huu Hung. Research on the health monitoring methods of bridge subjected to moving load based on Hilbert-Huang transform [D]. Changchun: Jilin University, 2013.
[15] 赵明鉥, 刘建政, 王一, 等. 基于改进希尔伯特-黄变换的泄漏电流特性分析[J]. 中国电机工程学报, 2016, 36(20): 5657-5667, 5743
ZHAO Mingshu, LIU Jianzheng, WANG Yi, et al. Research on characteristics of leakage current based on improved HHT algorithm[J]. Proceedings of the CSEE, 2016, 36(20): 5657-5667, 5743
[16] 程军圣, 于德介, 杨宇. 基于支持矢量回归机的Hilbert-Huang变换端点效应问题的处理方法[J]. 机械工程学报, 2006, 42(4): 23-31
CHENG Junsheng, YU Dejie, YANG Yu. Process method for end effects of Hilbert-Huang transform based on support vector regression machines[J]. Chinese Journal of Mechanical Engineering, 2006, 42(4): 23-31
[17] 徐力彬, 宋余庆, 刘毅. 基于镜像延拓和窗函数的端点效应抑制方法[J]. 计算机工程, 2015, 41(4): 112-116
XU Libin, SONG Yuqing, LIU Yi. Restraining method for end effect based on mirror extension and window function[J]. Computer Engineering, 2015, 41(4): 112-116
[18] 周亚琪, 应黎明, 胡克飞, 等. 基于全相FFT的电力变压器噪声衰减模型[J]. 中国电力, 2018, 51(9): 93-100
ZHOU Yaqi, YING Liming, HU Kefei, et al. Attenuation model of transformer noise based on all-phase FFT[J]. Electric Power, 2018, 51(9): 93-100
[19] 胡广书. 数字信号处理: 理论、算法与实现 [M]. 北京: 清华大学出版社, 1997.
[20] 张超. 高压直流输电谐波及间谐波检测技术的研究 [D]. 哈尔滨: 哈尔滨工业大学, 2016.
ZHANG Chao. Research on the harmonic and inter-harmonic detection technology in HVDC [D]. Harbin: Harbin Institute of Technology, 2016.
[21] 郑志萍, 吴军, 杨武盖, 等. 一种新的阻尼正弦原子分解算法辨识SSO模态参数[J]. 中国电力, 2016, 49(1): 75-79
ZHENG Zhiping, WU Jun, YANG Wugai, et al. Modal parameter identification of SSO based on damping sine atomic decomposition optimized by improved IWO[J]. Electric Power, 2016, 49(1): 75-79

基于滑窗FFT的次同步振荡时变幅频在线监测方法

Subsynchronous Oscillation Time-varying Amplitude Frequency On-Line Monitoring Method Based on Sliding Window FFT

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 12

编辑推荐

Metrics

[1]	赵牧驰, 汪海蛟, 刘纯, 何国庆, 孙艳霞, 王盼盼. 汽轮机发电机组宽频带阻抗建模及次/超同步振荡分析[J]. 中国电力, 2024, 57(7): 238-246.
[2]	周于清, 李大虎, 姚伟, 宗启航, 周泓宇, 文劲宇. 受端近区光伏电站对LCC-HVDC系统稳定性影响分析[J]. 中国电力, 2024, 57(3): 170-182.
[3]	李博浩, 郭昆丽, 吕家君, 蔡维正, 刘璐豪, 刘凤仪, 郝翊帆. 弱电网下改进LADRC抑制直驱风机次同步振荡研究[J]. 中国电力, 2023, 56(4): 56-67.
[4]	余高旺, 方陈, 樊占峰, 蔺立, 龚赟, 朱云峰. 基于汉宁窗的配电网同步相量测量装置算法及应用[J]. 中国电力, 2022, 55(6): 18-24.
[5]	蔡维正, 郭昆丽, 刘璐雨, 吴朝俊. 基于一阶LADRC控制的直驱风机次同步振荡抑制策略[J]. 中国电力, 2022, 55(4): 175-184.
[6]	张帆, 高本锋, 李铁成. 基于SVG的光伏并网SSO附加阻尼抑制策略[J]. 中国电力, 2021, 54(12): 11-19,44.
[7]	王胜利, 许刚. 特高压近区风电汇集地区次同步振荡特征及防控措施[J]. 中国电力, 2020, 53(3): 28-34,65.
[8]	梅益铭, 顾伟飞, 祝相云, 朱宝. 汽轮机阀门关闭试验中的噪声分析和信号处理[J]. 中国电力, 2019, 52(9): 154-160.
[9]	周亚琪, 应黎明, 胡克飞, 刘宪敏. 基于全相FFT的电力变压器噪声衰减模型[J]. 中国电力, 2018, 51(9): 93-100.
[10]	郑志萍，吴军，杨武盖，岑炳成，柯丽娜. 一种新的阻尼正弦原子分解算法辨识SSO模态参数[J]. 中国电力, 2016, 49(1): 75-79.
[11]	齐阳. 多HVDC输电系统SSO抑制措施的协调控制研究[J]. 中国电力, 2014, 47(4): 108-112.
[12]	王华伟, 韩民晓, 张健, 郭小江, 吴娅妮, 杨万开, 刘耀, 宋瑞华. 送端弱系统对特高压直流工程调试和运行的影响[J]. 中国电力, 2014, 47(10): 47-51.

模态框（Modal）标题

基于滑窗FFT的次同步振荡时变幅频在线监测方法

Subsynchronous Oscillation Time-varying Amplitude Frequency On-Line Monitoring Method Based on Sliding Window FFT

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 12

编辑推荐

Metrics