Synchronised ambient data-driven electromechanical oscillation modes extraction for interconnected power systems using the output-only observer/Kalman filter identification method

Abstract

Ambient-based oscillation modes extraction is an effective means of monitoring the small signal stability of a power system on-line. In this study, a data-driven approach based on output-only observer/Kalman filter identification (O3KID) combined with the eigensystem realisation algorithm (ERA) was proposed to extract electromechanical modes (frequency, damping ratio and mode shape) from synchronised ambient data. As a key for extracting a reduced-order state matrix using the ERA, the Markov parameters (impulse responses) are first estimated by employing O3KID on multi-channel ambient data. O3KID makes it possible to identify the modes with the ERA using only output ambient data, while ensuring the reliability of the extractions of frequencies, damping ratios and mode shapes. The performance of the proposed method was evaluated by employing the IEEE 16-generator 5-Area system and measured data from a real power system. The estimation results in all cases as well as comparison results with the RDT-ERA method and NExT-ERA method indicate that the O3KID-ERA method is a promising method for ambient data-driven electromechanical oscillation modes extraction.