基于深度残差网络的SSO模态参数辨识  

Modal parameter identification of SSO based on deep residual network

作　　者：况且 李娟[1] 白淑华[2] Kuang Qie;Li Juan;Bai Shuhua(School of Automation,Beijing Information Science and Technology University,Beijing 100192,China;Beijing Sifang Automation Co.,Ltd.,Beijing 100084,China)

机构地区：[1]北京信息科技大学自动化学院,北京100192 [2]北京四方继保自动化股份有限公司,北京100084

出　　处：《电子测量技术》2022年第11期57-63,共7页Electronic Measurement Technology

基　　金：国家自然科学基金(51477010)项目资助。

摘　　要：针对电力系统正常运行中的微弱次同步振荡信号趋势难以辨识,辨识算法抗噪性差、辨识结果可靠性低等问题,提出一种基于深度残差网络的次同步振荡模态参数辨识方法。建立了一种由卷积层、若干残差层和全连接层等构成的深度残差网络模型;模型训练数据集依据SSO信号特点生成,全部采用仿真数据;经参数调整和优化后的模型能够实现对现场实测的低信噪比SSO信号模态参数的盲辨识。利用理想信号、含噪仿真信号和现场实测数据3种方案对模型性能验证,结果表明该算法能有效地辨识出微弱SSO的频率和阻尼等关键参数,与卷积神经网络(CNN)和随机子空间(SSI)算法相比较,辨识精度更高,受噪声干扰小,具有盲辨识的特点,可用于电力系统次同步振荡风险的预警。In view of the trend of weak subsynchronous oscillation signal in the normal operation of power system, poor noise resistance and low reliability of identification results, a identification method of subsynchronous oscillation mode parameter based on deep residual network is proposed.A deep residual network model composed of convolutional layer, several residual layer and fully connected layer is established;the model training data set is generated according to the characteristics of SSO signal, all using simulation data;the parameter adjusted and optimized model can realize the blind identification of low SSO signal mode parameters measured in the field.Using ideal signal, noise simulation signal and field measured data three schemes of the model performance verification, the results show that the algorithm can effectively identify the weak SSO frequency and damping and other key parameters, compared with convolutional neural network(CNN) and random subspace(SSI) algorithm, higher accuracy, small noise interference, has the characteristics of blind identification, can be used for power system secondary synchronous oscillation risk warning.

关 键 词：次同步振荡 模态参数辨识 残差网络 抗噪性 

分 类 号：TM712[电气工程—电力系统及自动化]