CAJ | 학술논문

根据WAMS实测数据,对电力系统低频振荡模式进行辨识,对基于数据驱动随机子空间(SSI)辨识方法进行了研究.首先通过小波技术消去信号中的噪声分量,然后消去直流分量.利用处理后的数据构造Hankel矩阵,通过QR分解、SVD分解,利用卡尔曼滤波估计得到系统的随机状态模型,再对状态矩阵进行特征值分解,最终得到系统低频振荡模式参数.利用该方法分别对理想信号、仿真信号、电力系统实测数据进行分析.分析结果表明,基于数据驱动随机子空间方法能够准确辨识出系统主导振荡模式,可以应用于低频振荡模式的在线辨识.
근거WAMS실측수거,대전력계통저빈진탕모식진행변식,대기우수거구동수궤자공간(SSI)변식방법진행료연구.수선통과소파기술소거신호중적조성분량,연후소거직류분량.이용처리후적수거구조Hankel구진,통과QR분해、SVD분해,이용잡이만려파고계득도계통적수궤상태모형,재대상태구진진행특정치분해,최종득도계통저빈진탕모식삼수.이용해방법분별대이상신호、방진신호、전력계통실측수거진행분석.분석결과표명,기우수거구동수궤자공간방법능구준학변식출계통주도진탕모식,가이응용우저빈진탕모식적재선변식.
A stochastic subspace identification (SSI) method based on data-driven is applied to identification of power system low frequency oscillation modes. Firstly, by means of wavelet transform the noise contained in each signal is eliminated, and then the DC component is also eliminated. Hankel matrix is built based on treated data. The state space model is computed by QR method, singular value decomposition and Kalman filter estimation. Finally the system low frequency oscillation modes parameters are obtained by the state matrix eigenvalues. The ideal signal, simulated signal and the WAMS data of power system are analyzed respectively by the method. Analysis results show that SSI method based on data-driven can identify the main oscillation of the system accurately and it can be applied to on-line identification of power system oscillation modes.