An Adaptive TLS-ESPRIT Algorithm Based on an S-G Filter for Analysis of Low Frequency Oscillation in Wide Area Measurement Systems

In this paper, a two-stage mode identification algorithm including preprocessing and identification steps is introduced to solve the problems of measuring noise and inaccurate mode identification in the analysis of low-frequency oscillation (LFO) in a wide area measurement systems (WAMSs). S–G filter de-noising is utilized for the preprocessing. An adaptive total least squares-estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) algorithm with the order setting of the singular value accumulation percentage adjacency increment ratio (SVAPAIR) is utilized for the identifying stage. The S–G filter is used to mitigate the measuring of LFO noise of the system. Furthermore, the SVAPAIR is adopted to achieve an adaptive and precise determination of the LFO order, and then LFO parameters are extracted by the adaptive TLS-ESPRIT algorithm. The proposed algorithm has been tested and verified using the IEEE four-generator two-area system and the actual measured data of the LFO accident in the North American power grid. The simulation and experimental result showed that the proposed algorithm has better adaptability, anti-noise characteristic, and robustness compared to those of the conventional Prony, matrix pencil (MP), and TLS-ESPRIT algorithms.