【院士華電行之二十】Synchronous Phasor Data Quality Enhancement and an Online Method for Thevenin Equivalent Estimation

來(lái)源:電氣與電子工程學(xué)院、校慶工作辦公室、科學(xué)技術(shù)研究院發(fā)布時(shí)間:2018-07-16

講座題目Synchronous Phasor Data Quality Enhancement and an Online Method for Thevenin Equivalent Estimation

講座時(shí)間】2018年7月17日(星期二)15:00

講座地點(diǎn)】北京校部主樓D260

【主 講 人Joe   H. Chow  美國(guó)工程院院士

【主講人簡(jiǎn)介 Joe   H. Chow教授在伊利諾伊大學(xué)厄巴納-香檳分校獲得電氣工程碩士、博士學(xué)位。1987年加入倫斯勒理工學(xué)院前,他曾在通用電氣電力系統(tǒng)部門(mén)工作。目前,他是倫斯勒理工學(xué)院電子與計(jì)算機(jī)系統(tǒng)工程學(xué)院教授,NSF/DOE工程研究中心的校園主任,負(fù)責(zé)超廣域彈性電能傳輸網(wǎng)絡(luò)(CURENT ERC)。研究方向包括大型電力系統(tǒng)的建模和控制以及同步相量測(cè)量技術(shù)。同時(shí),他是IEEE Fellow和美國(guó)工程院院士,曾獲美國(guó)自動(dòng)控制委員會(huì)的Donald Eckman獎(jiǎng),IEEE控制系統(tǒng)學(xué)會(huì)控制系統(tǒng)技術(shù)獎(jiǎng)和IEEE電力能源學(xué)會(huì)的Charles Concordia電力系統(tǒng)工程獎(jiǎng)。

內(nèi)容簡(jiǎn)介 This talk consists of two parts. The first part is entitled “Recovery of Missing Synchrophasor Data via Adaptive Filtering.” This is an ongoing research topic at RPI to improve the data quality of PMU data, so that the synchrophasor data can be used reliably by control room applications.  In previous research, RPI has proposed the use of low-rank matrix completion to recovery missing PMU data, from information available in neighboring substations.  (Low-rank matrix completion methods have been developed for video processing and consensus analysis.) In this new approach, the low-rank property is translated into an adaptive filter based on windows lasting seconds.  A stability condition has also been established for the filter.  The second part is entitled “an On-line Thevenin Equivalent Estimation Algorithm.” This is a continuation of our research on developing equivalent circuit models from power system measured data for voltage stability analysis.  Thevenin Equivalent model estimation using measured data from real systems is fraught with problems and can yield inconsistent results.  During periods in which the load and generator pattern does not change much, the estimated parameters will be driven mostly by measurement noise.  In this new online method, a thresholding method and a forgetting factor method are used to reduce the variations of the Thevenin Equivalent model parameters. 

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