Multi-variate statistical data analysis techniques offer a powerful tool for analyzing power system response from measured data. In this thesis, a statistically-based, data-driven framework that integrates the use of complex wavelet empirical orthogonal function (EOF) analysis and the method of snapshots is proposed to identify and extract dynamically independent spatio-temporal patterns from time synchronized data. The procedure allows identification of the dominant spatial and temporal patterns in a complex data set and is particularly well suited for the study of standing and propagating features that can be associated with electromechanical oscillations in power systems. It is shown that, in addition to providing spatial and temporal information, the method improves the ability of conventional correlation analysis to capture temporal events and gives a quantitative result for both the amplitude and phase of motions, which are essential in the interpretation and characterization of transient processes in power systems. The efficiency and accuracy of the developed procedures for capturing the temporal evolution of the modal content of data from time synchronized phasor measurements of a real event in México is assessed. Results show that the proposed method can provide accurate estimation of nonstationary effects, modal frequency, time-varying mode shapes, and time instants of intermittent or irregular transient behavior associated with abrupt changes in system topology or operating conditions.
- ISBN (Buch)
- 2.5 MB
- Institution / Hochschule
- Universidad de Guadalajara – CINVESTAV - Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional Unidad Guadalajara
- Identificación de patrones
- extraction dynamic patterns wide-area measurements empirical orthogonal functions