【摘要】：同步相量測量單元(PMU)能夠?qū)﹄娏ο到y(tǒng)動態(tài)過程中發(fā)電機功角進行直接量測。然而,壞數(shù)據(jù)有可能導(dǎo)致狀態(tài)估計準確度下降甚至失效。提出了一種基于魯棒性容積卡爾曼濾波(CKF)的機電暫態(tài)過程發(fā)電機動態(tài)狀態(tài)估計方法。在CKF中構(gòu)造時變多維觀測噪聲尺度因子,根據(jù)量測新息對PMU量測誤差進行調(diào)整,使得量測量能夠?qū)顟B(tài)量預(yù)報值進行準確修正。給出了時變多維觀測噪聲尺度因子的具體構(gòu)造方法。針對濾波增益求逆發(fā)生奇異的問題,提出解決方案,對魯棒CKF動態(tài)狀態(tài)估計過程進行說明。仿真結(jié)果表明該方法能夠有效抑制量測壞數(shù)據(jù)對動態(tài)狀態(tài)估計的影響。
[Abstract]:The synchronous phasor measuring unit (PMU) can measure the power angle of generator directly in the dynamic process of power system. However, bad data may lead to the decline or even failure of state estimation accuracy. A dynamic state estimation method for electromechanical transient process generator based on robust volume Kalman filter (CKF) is proposed. The time-varying multi-dimensional observation noise scale factor is constructed in CKF, and the PMU measurement error is adjusted according to the measurement innovation, so that the state quantity prediction value can be accurately corrected. The concrete construction method of time-varying multi-dimensional observation noise scale factor is given. In order to solve the singular problem of inverse filtering gain, a solution is proposed, and the process of robust CKF dynamic state estimation is explained. The simulation results show that the method can effectively suppress the influence of bad data on dynamic state estimation.
【作者單位】： 新能源電力系統(tǒng)國家重點實驗室(華北電力大學(xué));
【基金】：國家重點基礎(chǔ)研究發(fā)展計劃(973計劃)(2012CB215206) 國家自然科學(xué)基金(51222703) 高等學(xué)校博士學(xué)科點專項科研基金(20120036110009) “111”計劃(B08013)資助項目
【分類號】：TN713;TM31
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本文編號：2511933