基于RA-CNN和同步相量的风电场次/超同步振荡参数智能辨识方法

doi:10.11930/j.issn.1004-9649.202208068

中国电力 ›› 2023, Vol. 56 ›› Issue (4): 46-55,67.DOI: 10.11930/j.issn.1004-9649.202208068

• 新能源基地经直流送出系统稳定性分析与控制技术 • 上一篇下一篇

基于RA-CNN和同步相量的风电场次/超同步振荡参数智能辨识方法

陆友文¹, 崔昊², 陈佳宁², 彭祥佳¹, 冯双², 刘栋³

1. 东南大学软件学院, 江苏南京 210096;
2. 东南大学电气工程学院, 江苏南京 210096;
3. 国电南瑞科技股份有限公司, 江苏南京 211106

收稿日期:2022-08-18 修回日期:2023-01-13 出版日期:2023-04-28 发布日期:2023-04-26
作者简介:陆友文(1997-),男,硕士研究生,从事人工智能在电力系统中的应用研究,E-mail:220205655@seu.edu.cn;崔昊(1997-),女,硕士研究生,从事电力系统稳定分析研究,E-mail:cui_hao@163.com;陈佳宁(1995-),男,硕士研究生,从事电力系统稳定分析研究,E-mail:plutoteris@126.com;彭祥佳(1998-),女,硕士研究生,从事人工智能在电力系统中的应用研究,E-mail:220205721@seu.edu.cn;冯双(1990-),女,通信作者,博士,副教授,硕士生导师,主要从事电力电子化电力系统分析与控制、人工智能在电力系统中的应用研究,E-mail:sfeng@seu.edu.cn;刘栋(1983-),男,从事电网调度自动化分析与控制技术研究,E-mail:liudong2@sgepri.sgcc.com.cn
基金资助:
国家自然科学基金资助项目（含高渗透并网变流器电力系统宽频强迫振荡机理及监测方法研究，51807025）

Intelligent Identification Method of Wind Farm Sub-synchronous/Super-synchronous Oscillation Parameters Based on RA-CNN and Synchrophasor

LU Youwen¹, CUI Hao², CHEN Jianing², PENG Xiangjia¹, FENG Shuang², LIU Dong³

1. College of Software Engineering, Southeast University, Nanjing 210096, China;
2. School of Electrical Engineering, Southeast University, Nanjing 210096, China;
3. NARI Technology Co., Ltd., Nanjing 211106, China

Received:2022-08-18 Revised:2023-01-13 Online:2023-04-28 Published:2023-04-26
Supported by:
This work is supported by National Natural Science Foundation of China (Research on Broadband Forced Oscillation Mechanism and Monitoring Method of Power System with High Penetration Grid-Connected Converter, No.51807025).

摘要/Abstract

摘要： 近年来风电并网比例大幅提高，由此引发的次/超同步振荡的发生概率也大大提高，严重威胁系统的安全稳定性。准确辨识次/超同步振荡参数是抑制振荡的基础，提出基于注意力机制的残差卷积神经网络的辨识方法。卷积神经网络的局部相关性和权值共享决定了其具有更强的特征学习和表达能力，通过结合注意力机制可以更准确地辨识振荡参数。同时，引入残差连接，用以解决深层卷积神经网络存在的梯度消失和网络退化问题。仿真结果表明：相较于传统方法，该方法不仅能在较短时间窗数据上完整地辨识次/超同步振荡的参数，且能规避传统方法因主观因素带来的辨识误差，降低振荡参数辨识的复杂度。

关键词: 次/超同步振荡, 同步相量, 参数辨识, 卷积神经网络, 注意力机制, 残差网络

Abstract: In recent years, the proportion of wind power connected to the grid has increased significantly, and the probability of occurrence of sub-synchronous/super-synchronous oscillations caused by this has also been greatly raised, which seriously threatens the safety and steadiness of the system. Accurate identification of sub-synchronous/super-synchronous oscillation parameters is the basis for oscillation suppression. Therefore, this paper proposes an identification method based on the attention mechanism of the residual convolutional neural network (CNN). The local correlation and weight sharing of the convolutional neural network determine its stronger feature learning and expression ability, and thus, the oscillation parameters can be more accurately identified when it is combined with the attention mechanism. Meanwhile, this method introduces residual connections to solve the problems of gradient vanishing and network degradation in the deep convolutional neural network. Simulations indicate that compared with the traditional method, this method can completely identify the parameters of sub-synchronous/super-synchronous oscillations on the data of a short time window. In addition, it can avoid the identification error caused by the subjective factors of the traditional method and reduce the complexity of oscillation parameter identification.

Key words: sub-synchronous/super-synchronous oscillation, synchrophasor, parameter identification, convolutional neural network, attention mechanism, residual network

陆友文, 崔昊, 陈佳宁, 彭祥佳, 冯双, 刘栋. 基于RA-CNN和同步相量的风电场次/超同步振荡参数智能辨识方法[J]. 中国电力, 2023, 56(4): 46-55,67.

LU Youwen, CUI Hao, CHEN Jianing, PENG Xiangjia, FENG Shuang, LIU Dong. Intelligent Identification Method of Wind Farm Sub-synchronous/Super-synchronous Oscillation Parameters Based on RA-CNN and Synchrophasor[J]. Electric Power, 2023, 56(4): 46-55,67.

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基于RA-CNN和同步相量的风电场次/超同步振荡参数智能辨识方法

Intelligent Identification Method of Wind Farm Sub-synchronous/Super-synchronous Oscillation Parameters Based on RA-CNN and Synchrophasor

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基于RA-CNN和同步相量的风电场次/超同步振荡参数智能辨识方法

Intelligent Identification Method of Wind Farm Sub-synchronous/Super-synchronous Oscillation Parameters Based on RA-CNN and Synchrophasor

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