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Synchrophasor를 이용한 한전계통의 저주파 진동 스펙트럴 해석
심관식(Kwan-Shik Shim),최준호(Jun-Ho Choi),김상태(Sang-Tae Kim) 한국조명·전기설비학회 2013 조명·전기설비학회논문지 Vol.27 No.12
The parameters of electromechanical modes offer considerable insight into the dynamic stability properties of a power system. This paper presents a results of a LFO(low-frequency oscillation) based on the time-synchronized signals measured by synchrophasor in the rolling blackout. Spectral analysis was performed, and critical parameters were estimated using the data acquired from synchrophasors installed in the KEPCO system. As significant modes, a 0.68 Hz oscillation mode that occurred prior to the forced load shedding in the rolling blackout was estimated. Such an oscillation mode can cause an uncontrollable blackout. Therefore, the system should be operated so that significant oscillation modes are not activated. This results can serve as a reference in the future for reliable system operation in the event of a similar blackout.
Synchrophasor를 이용한 한전계통의 저주파 진동 해석 초기 결과
심관식(Kwan-Shik Shim),김상태(Sang-Tae Kim),남해곤(Hae-Kon Nam),최준호(Joon-Ho Choi) 대한전기학회 2014 전기학회논문지 Vol.63 No.1
The parameters of electromechanical modes offer considerable insight into the dynamic stability properties of a power system. This paper describes an initial result for extraction of dynamic parameters from synchrophasor measurements collected on the KEPCO system. Dominant modes of the system are estimated by oscillation detecting program in K-WAMS. The critical wide-area modes of KEPCO system have frequencies in the 0.4 to 0.7Hz range. And the local mode causing the low frequency oscillation of generators located on the west coast area has in the frequency 0.97 and 1.25 Hz, respectively. This results can serve as a reference in the future for reliable system operation in KEPCO system.
위상동기신호를 이용한 한전계통의 저주파진동 검출과 고유치해석
심관식(Kwan-Shik Shim),김상태(Sang-Tae Kim),김태균(Tae-Kyun Kim),안선주(Seon-Ju Ahn),최준호(Joon-Ho Choi) 대한전기학회 2017 전기학회논문지 Vol.66 No.2
This paper describes the results of a low‐frequency oscillation analysis using data measured in PMU installed in the KEPCO system, and the comparison with eigenvalues computed from the linear model. The dominant oscillation modes are estimated by applying various algorithms. The algorithms are: the extended Prony method; multiple time interval parameter estimation method; subspace system identification method; and spectral analysis. From the measurement data, modes of frequency 0.68[㎐] and 0.92[㎐] were estimated, and modes of frequency 0.63[㎐] and 0.80[㎐] were computed from the eigenvalue calculation. There was a difference between the mode estimated from measurement data and that from the linear model. This is possibly because of an error in the dynamic data of the KEPCO system used in eigenvalue calculation. Because wide area modes exist in the KEPCO system, these modes should be monitored continuously for the reliable operation of the system. In order to prevent total blackouts caused by wide area oscillation, moreover, contingency analysis should be performed in relation to this mode and appropriate measures should be established.
심관식(Kwan-Shik Shim),김상태(Sang-Tae Kim),최준호(Joon-Ho Choi),남해곤(Hae-Kon Nam),안선주(Seon-Ju Ahn) 대한전기학회 2014 전기학회논문지 Vol.63 No.7
In this paper, we propose a new parameter estimation method that can deal with the data of multiple time intervals simultaneously. If there are common modes in the multiple time intervals, it is possible to create a new polynomial by summing the coefficients of the prediction error polynomials of each time interval. By calculating the roots of the new polynomial, it is possible to estimate the common modes that exist in each time interval. The accuracy of the proposed parameter estimation method has been proven by using appropriate test signals.
沈冠埴(Kwan-Shik Shim),南海鯤(Hae-Kon Nam) 대한전기학회 2006 전기학회논문지A Vol.55 No.7
This paper describes a method of parameter estimation of time series data using discrete Fourier transform(DFT). DFT have been mainly used to precisely and rapidly obtain the frequency of a signal. In a dynamic system, a real part of a mode used to learn damping characteristics is a more important factor than the frequency of the mode. The parameter estimation method of this paper can directly estimate modes and parameters, indicating the characteristics of a dynamic system, on the basis of the Fourier transform of the time series data. Real part of a mode estimates by subtracting a frequency of the Fourier spectrum corresponding to 0.707 of a magnitude of the peak spectrum from a peak frequency, or subtracting a frequency of the power spectrum corresponding to 0.5 of the peak power spectrum from a peak frequency, or comparing the Fourier(power) spectrum ratio. Also, the residue and phase of time signal calculate by simple equation with the real part of the mode and the power spectrum that have been calculated. Accordingly, the proposed algorithm is advantageous in that it can estimate parameters of the system through a single DFT without repeatedly calculating a DFT, thus shortening the time required to estimate the parameters.