Single Value Decomposition to Estimate Critical Clearing Time of a Power System Using Measurements

Abstract

The transient stability analysis of large-interconnected power systems using time-domain simulations (TDS) is a significant challenge since it represents a huge computational cost. Besides, for dynamic security assessment is required have a quick response. Consequently, recent approaches are relying on using the wide-area measurement system combined with other techniques to perform transient stability assessment and counteract the drawbacks of the TDS method. However, these approaches still requiring to perform TDS to set initial parameters. This paper proposes a new algorithm to estimate the critical clearing time (CCT) based on the eigenvalue calculation and the singular value decomposition using data from wide-area measurement systems. The proposed algorithm uses the phase angles of the voltage phasors measurements at the generation buses to represent the dynamics of the internal angles of the generators. First, from a set of signals, a measurement matrix is formed using a sliding window. Then, a threshold based on the maximum singular value and the dominant eigenvalue of the measurement matrix are computed. Finally, the CCT is estimated using the dominant eigenvalue (the most energetic eigenvalue) and the threshold. The proposed algorithm is evaluated using the Kundur four-machine system and New England 39-Bus system. Its performance contrasts to the CCT calculated using the classical TDS. The simulation results demonstrated acceptable precision of the CCT against TDS. Also, it presents robustness against the effect of the noise in the measurements. Therefore, it is suitable for online applications.