Power System Low Frequency Oscillation Mode Identification Based on Exact Mode Order-Exponentially Damped Sinusoids Neural Network

Renjie DING; Zhongting SHEN

doi:10.7500/AEPS20190321005

您当前的位置：

首页 >

文章列表页 >

Power System Low Frequency Oscillation Mode Identification Based on Exact Mode Order-Exponentially Damped Sinusoids Neural Network

Basic Research | 更新时间：2025-06-16

- Power System Low Frequency Oscillation Mode Identification Based on Exact Mode Order-Exponentially Damped Sinusoids Neural Network
- Automation of Electric Power Systems Vol. 44, Issue 3, Pages: 122-131(2020)
- 作者机构：
  
  清华大学电机工程与应用电子技术系,北京市 100084
- 作者简介：
- 基金信息：
- DOI：10.7500/AEPS20190321005
  CLC：
- Received：21 March 2019，
  
  Revised：2019-06-14，
  
  Published Online：29 October 2019，
  
  Published：25 March 2020
- 稿件说明：
移动端阅览
丁仁杰,沈钟婷.基于EMO-EDSNN的电力系统低频振荡模态辨识[J].电力系统自动化,2020,44(03):122-131.

DING Renjie,SHEN Zhongting.Power System Low Frequency Oscillation Mode Identification Based on Exact Mode Order-Exponentially Damped Sinusoids Neural Network[J].Automation of Electric Power Systems,2020,44(03):122-131.
丁仁杰,沈钟婷.基于EMO-EDSNN的电力系统低频振荡模态辨识[J].电力系统自动化,2020,44(03):122-131. DOI： 10.7500/AEPS20190321005.

DING Renjie,SHEN Zhongting.Power System Low Frequency Oscillation Mode Identification Based on Exact Mode Order-Exponentially Damped Sinusoids Neural Network[J].Automation of Electric Power Systems,2020,44(03):122-131. DOI： 10.7500/AEPS20190321005.

摘要

提出了基于精确模态阶数-指数型衰减正弦神经网络(EMO-EDSNN)的电力系统低频振荡模态辨识方法。首先

通过奇异值分解估计模态阶数。在关键的定阶问题上

采取EMO定阶方法

综合考虑了奇异值变化规律和奇异值本身大小2个因素

能够克服人为选取阈值的不足

提高阶数估计的准确性。然后

通过建立EDSNN将参数估计问题转化为优化问题求解。以输出信号和实测信号的平方误差最小为目标

并采用自适应的Levenberg-Marquardt算法训练神经网络收敛后

一次性计算出所有模态参数。最后

进行了数值信号仿真、EPRI-36系统仿真和实测信号仿真。仿真结果表明

所提方法能够快速准确地实现模态参数辨识。

Abstract

The paper proposes a mode identification method for low frequency oscillations in power systems based on exact mode order-exponentially damped sinusoids neural network (EMO-EDSNN). Firstly

the mode order is estimated via singular value decomposition. An EMO method is employed to solve the key problem of order determination. It comprehensively considers the variation laws of singular values change and the values themselves

thus overcoming shortages of artificial thresholds and enhancing the accuracy of order determination. Secondly

the EDSNN is constructed to translate the parameter estimation into an optimization problem. After training the neural network via the self-adaptive Levenberg-Marquardt algorithm aiming for a minimum square error between output and real signals

all the mode parameters can be obtained simultaneously. Finally

simulations of numerical signals

EPRI-36 system and actual signals are carried out. The results show that the proposed method can identify the mode parameters in an accurate and reliable manner.

关键词

Keywords

references

闵勇 , 陈磊 , 姜齐荣 . 电力系统稳定分析 [M]. 北京 : 清华大学出版社 , 2016 .

MIN Yong , CHEN Lei , JIANG Qirong . Power system stability [M]. Beijing : Tsinghua University Press , 2016 .

陈磊 , 闵勇 , 胡伟 . 基于振荡能量的低频振荡分析与振荡源定位：(一)理论基础与能量流计算 [J]. 电力系统自动化 , 2012 , 36 ( 3 ): 22 - 27 .

CHEN Lei , MIN Yong , HU Wei . Low frequency oscillation analysis and oscillation source location based on oscillation energy: Part Ⅰ　mathematical foundation and energy flow computation [J]. Automation of Electric Power Systems , 2012 , 36 ( 3 ): 22 - 27 .

陈恩泽 , 刘涤尘 , 廖清芬 , 等 . 多重扰动下的跨区电网低频振荡研究 [J]. 电工技术学报 , 2014 , 29 ( 2 ): 290 - 296 .

CHEN Enze , LIU Dichen , LIAO Qingfen , et al . Research on low frequency oscillation of interconnected power grid based on multiple disturbances [J]. Transactions of China Electrotechnical Society , 2014 , 29 ( 2 ): 290 - 296 .

王臻 . 高斯有色噪声背景下电力系统低频振荡在线模式识别的研究 [D]. 武汉 : 华中科技大学 , 2016 .

WANG Zhen . Research on online mode identification of power system low frequency oscillation considering colored Gaussian noises [D]. Wuhan : Huazhong University of Science and Technology , 2016 .

程云峰 , 张欣然 , 陆超 . 广域测量技术在电力系统中的应用研究进展 [J]. 电力系统保护与控制 , 2014 , 42 ( 4 ): 145 - 153 .

CHENG Yunfeng , ZHANG Xinran , LU Chao . Research progress of the application of wide area measurement technology in power system [J]. Power System Protection and Control , 2014 , 42 ( 4 ): 145 - 153 .

WADDUWAGE D P , ANNAKKAGE U D , NARENDRA K . Identification of dominant low-frequency modes in ring-down oscillations using multiple Prony models [J]. IET Generation ， Transmission & Distribution , 2015 , 9 ( 15 ): 2206 - 2214 .

孙英云 , 游亚雄 , 侯建兰 , 等 . 基于差分正交匹配追踪和Prony算法的低频振荡模态辨识 [J]. 电力系统自动化 , 2015 , 39 ( 10 ): 69 - 74 .

SUN Yingyun , YOU Yaxiong , HOU Jianlan , et al . Identification of low-frequency oscillation mode based on difference orthogonal matching pursuit and Prony algorithm [J]. Automation of Electric Power Systems , 2015 , 39 ( 10 ): 69 - 74 .

张程 , 金涛 . 基于ISPM和SDM-Prony算法的电力系统低频振荡模式辨识 [J]. 电网技术 , 2016 , 40 ( 4 ): 1209 - 1216 .

ZHANG Cheng , JIN Tao . Identification of low frequency oscillations in power systems using an improved smoothness priors method and second-derivative method-Prony [J]. Power System Technology , 2016 , 40 ( 4 ): 1209 - 1216 .

张俊峰 , 杨婷 , 陈珉 , 等 . 基于Prony滑动平均窗算法的电力系统低频振荡特征分析 [J]. 电力自动化设备 , 2018 , 38 ( 10 ): 178 - 183 .

ZHANG Junfeng , YANG Ting , CHEN Min , et al . Power system low-frequency oscillation characteristic analysis based on Prony moving average window algorithm [J]. Electric Power Automation Equipment , 2018 , 38 ( 10 ): 178 - 183 .

王祥超 , 张鹏 , 甄威 , 等 . 基于自然激励技术和TLS-ESPRIT方法的低频振荡模式辨识 [J]. 电力系统自动化 , 2015 , 39 ( 10 ): 75 - 80 .

WANG Xiangchao , ZHANG Peng , ZHEN Wei , et al . Identification of low frequency oscillation modes based on NExT and TLS-ESPRIT algorithm [J]. Automation of Electric Power Systems , 2015 , 39 ( 10 ): 75 - 80 .

SUBUDHI B , SAMAL S K , GHOSH S . A new low-frequency oscillatory modes estimation using TLS-ESPRIT and least mean squares sign-data (LMSSD) adaptive filtering [C]// Proceedings of the 2017 IEEE Region 10 Conference (TENCON) , November 5 - 8 2017 , Penang, Malaysia: 751 - 756 .

JIN Tao , LIU Siyi , FLESCH R C . Mode identification of low-frequency oscillations in power systems based on fourth-order mixed mean cumulant and improved TLS-ESPRIT algorithm [J]. IET Generation Transmission & Distribution , 2017 （ 1115 : 3739 - 3748 .

PHILIP J G , JAIN T . Analysis of low frequency oscillations in power system using EMO ESPRIT [J]. International Journal of Electrical Power & Energy Systems , 2018 , 95 : 499 - 506 .

RUEDA J L , JUAREZ C A , ERLICH I . Wavelet-based analysis of power system low-frequency electromechanical oscillations [J]. IEEE Transactions on Power Systems , 2011 , 26 ( 3 ): 1733 - 1743 .

HOSSEINI S A , AMJADY N , VELAYATI M H . A Fourier based wavelet approach using Heisenberg’s uncertainty principle and Shannon’s entropy criterion to monitor power system small signal oscillations [J]. IEEE Transactions on Power Systems , 2015 , 30 ( 6 ): 3314 - 3326 .

喻敏 , 王斌 , 陈绪轩 , 等 . 同步挤压小波变换在电力系统低频振荡模态参数提取中的应用 [J]. 电工技术学报 , 2017 , 32 ( 6 ): 14 - 20 .

YU Min , WANG Bin , CHEN Xuxuan , et al . Application of synchrosqueezed wavelet transform for extraction of the oscillatory parameters of low frequency oscillation in power systems [J]. Transactions of China Electrotechnical Society , 2017 , 32 ( 6 ): 14 - 20 .

杨德昌 , REHTANZ C , 李勇 , 等 . 基于改进希尔伯特-黄变换算法的电力系统低频振荡分析 [J]. 中国电机工程学报 , 2011 , 31 ( 10 ): 102 - 108 .

YANG Dechang , REHTANZ C , LI Yong , et al . Researching on low frequency oscillation in power system based on improved HHT algorithm [J]. Proceedings of the CSEE , 2011 , 31 ( 10 ): 102 - 108 .

雷强 , 刘光晔 , 朱永强 , 等 . 基于EEMD和矩阵束算法的低频振荡主导模式识别 [J]. 电力系统保护与控制 , 2016 , 44 ( 12 ): 56 - 62 .

LEI Qiang , LIU Guangye , ZHU Yongqiang , et al . Identification of the dominant mode based on EEMD and matrix pencil algorithm for low frequency oscillations [J]. Power System Protection and Control , 2016 , 44 ( 12 ): 56 - 62 .

王娜娜 , 刘涤尘 , 廖清芬 , 等 . 基于EMD-TEO及信号能量分析法的主导低频振荡模式识别 [J]. 电工技术学报 , 2012 , 27 ( 6 ): 198 - 204 .

WANG Nana , LIU Dichen , LIAO Qingfen , et al . Identification of the dominant inertial mode based on EMD-TEO and signal energy method [J]. Transactions of China Electrotechnical Society , 2012 , 27 ( 6 ): 198 - 204 .

杨德友 , 王博 , 蔡国伟 , 等 . 适用于电力系统非平稳功率振荡信号特征提取的自适应迭代滤波算法研究 [J]. 中国电机工程学报 , 2016 , 36 ( 20 ): 5431 - 5439 .

YANG Deyou , WANG Bo , CAI Guowei , et al . Extracting oscillation modes from nonstationary signals for power system using adaptive local iterative filter [J]. Proceedings of the CSEE , 2016 , 36 ( 20 ): 5431 - 5439 .

XIAO Huaishuo , WEI Jianchun , LIU Hongshun , et al . Identification method for power system low-frequency oscillations based on improved VMD and Teager-Kaiser energy operator [J]. IET Generation , Transmission & Distribution , 2017 , 11 ( 16 ): 4096 - 4103 .

李惠章 , 李承 , 王臻 . 基于混沌与Pseudo-Newton法组合优化的直接衰减正弦原子库分解方法在低频振荡分析中的应用 [J]. 中国电机工程学报 , 2018 , 38 ( 1 ): 148 - 157 .

LI Huizhang , LI Cheng , WANG Zhen . Direct damped sine atomic sparse decomposition method that based on the combinatorial optimization of chaotic and Pseudo-Newton and its application to low-frequency oscillation analysis [J]. Proceedings of the CSEE , 2018 , 38 ( 1 ): 148 - 157 .

赵妍 , 李志民 , 李天云 . 低频振荡模态参数辨识的共振稀疏分解SSI分析方法 [J]. 电工技术学报 , 2016 , 31 ( 2 ): 136 - 144 .

ZHAO Yan , LI Zhimin , LI Tianyun . Low frequency oscillation modal parameter identification using resonance-based sparse signal decomposition and SSI method [J]. Transactions of China Electrotechnical Society , 2016 , 31 ( 2 ): 136 - 144 .

谢剑 , 王晓茹 , 张鹏 . 基于PRCE方法的低频振荡模式识别 [J]. 中国电机工程学报 , 2017 , 37 ( 11 ): 3129 - 3138 .

XIE Jian , WANG Xiaoru , ZHANG Peng . Identification of low-frequency oscillations based on PRCE algorithm [J]. Proceedings of the CSEE , 2017 , 37 ( 11 ): 3129 - 3138 .

闪鑫 , 陆晓 , 翟明玉 , 等 . 人工智能应用于电网调控的关键技术分析 [J]. 电力系统自动化 , 2019 , 43 ( 1 ): 49 - 57 . DOI: 10.7500/AEPS20180629002 http://dx.doi.org/10.7500/AEPS20180629002 .

SHAN Xin , LU Xiao , ZHAI Mingyu , et al . Analysis of key technologies for artificial intelligence applied to power grid dispatch and control [J]. Automation of Electric Power Systems , 2019 , 43 ( 1 ): 49 - 57 . DOI: 10.7500/AEPS 20180629002 http://dx.doi.org/10.7500/AEPS 20180629002 .

程乐峰 , 余涛 , 张孝顺 , 等 . 机器学习在能源与电力系统领域的应用和展望 [J]. 电力系统自动化 , 2019 , 43 ( 1 ): 15 - 31 . DOI: 10.7500/AEPS20180814007 http://dx.doi.org/10.7500/AEPS20180814007 .

CHENG Lefeng , YU Tao , ZHANG Xiaoshun , et al . Machine learning for energy and electric power systems: state of the art and prospects [J]. Automation of Electric Power Systems , 2019 , 43 ( 1 ): 15 - 31 . DOI: 10.7500/AEPS20180814007 http://dx.doi.org/10.7500/AEPS20180814007 .

李洋麟 , 江全元 , 颜融 , 等 . 基于卷积神经网络的电力系统小干扰稳定评估 [J]. 电力系统自动化 , 2019 , 43 ( 2 ): 50 - 57 . DOI: 10.7500/AEPS20171203004 http://dx.doi.org/10.7500/AEPS20171203004 .

LI Yanglin , JIANG Quanyuan , YAN Rong , et al . Small-signal stability assessment of power system based on convolutional neural network [J]. Automation of Electric Power Systems , 2019 , 43 ( 2 ): 50 - 57 . DOI: 10.7500/AEPS20171203004 http://dx.doi.org/10.7500/AEPS20171203004 .

杨挺 , 赵黎媛 , 王成山 . 人工智能在电力系统及综合能源系统中的应用综述 [J]. 电力系统自动化 , 2019 , 43 ( 1 ): 2 - 14 . DOI: 10.7500/AEPS20180706005 http://dx.doi.org/10.7500/AEPS20180706005 .

YANG Ting , ZHAO Liyuan , WANG Chengshan . Review on application of artificial intelligence in power system and integrated energy system [J]. Automation of Electric Power Systems , 2019 , 43 ( 1 ): 2 - 14 . DOI: 10.7500/AEPS 20180706005 http://dx.doi.org/10.7500/AEPS 20180706005 .

马建伟 , 竺炜 , 曾喆昭 , 等 . FFT结合神经网络的低频振荡主导模式识别 [J]. 电力科学与技术学报 , 2011 , 26 ( 4 ): 88 - 93 .

MA Jianwei , ZHU Wei , ZENG Zhezhao , et al . Power system low frequency oscillation dominant pattern recognition based on FFT and neural network algorithm [J]. Journal of Electric Power Science and Technology , 2011 , 26 ( 4 ): 88 - 93 .

王臻 , 李承 , 林志芳 , 等 . 考虑高斯有色噪声的FOMC-HTLS-Adaline算法在低频振荡模式辨识中的研究 [J]. 电工技术学报 , 2017 , 32 ( 6 ): 21 - 30 .

WANG Zhen , LI Cheng , LIN Zhifang , et al . Research on low frequency oscillation mode identification based on FOMC-HLTS-Adaline algorithm considering colored Gaussian noises [J]. Transactions of China Electrotechnical Society , 2017 , 32 ( 6 ): 21 - 30 .

XIAO Xiuchun , LAI Jianhuang , WANG Changdong . Parameter estimation of the exponentially damped sinusoids signal using a specific neural network [J]. Neurocomputing , 2014 , 143 : 331 - 338 .

金涛 , 刘对 . 基于广义形态滤波与改进矩阵束的电力系统低频振荡模态辨识 [J]. 电工技术学报 , 2017 , 32 ( 6 ): 3 - 13 .

JIN Tao , LIU Dui . Power system low frequency oscillation identification based on the generalized morphological method and improved matrix pencil algorithm [J]. Transactions of China Electrotechnical Society , 2017 , 32 ( 6 ): 3 - 13 .

王宇静 , 于继来 . 电力系统振荡模态的矩阵束辨识法 [J]. 中国电机工程学报 , 2007 , 27 ( 19 ): 12 - 17 .

WANG Yujing , YU Jilai . Matrix pencil method of oscillation modes identification in power systems [J]. Proceedings of the CSEE , 2007 , 27 ( 19 ): 12 - 17 .

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 low-frequency power system oscillation based on measured signal [J]. Transactions of China Electrotechnical Society , 2011 , 35 ( 1 ): 59 - 65 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Dynamic State Estimation for Distribution Network with Photovoltaic Based on Improved Cubature Kalman Filter

基于单机模型扩展的直驱风电场通用等值模型构建方法

Optimal Construction Method for Advanced Metering Infrastructure of Power Grid Based on Multi-dimensional Compressed Sensing

Related Author

WENG Haowen

WANG Can

PAN Xuewei

QI Hang

LI Changgang

LIU Yutian

YAN Jiongcheng

LIU Hao

Related Institution

School of Robotics and Advanced Manufacturing, Harbin Institute of Technology

Key Laboratory of Power System Intelligent Dispatch and Control, Ministry of Education (Shandong University)

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University)

School of Electrical Automation and Information Engineering, Tianjin University

⁰