A Low-Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S-Transform with Bat Algorithm

Abstract

The complexity of the interconnected grid and the continuous increase of new energy sources have led to an acute problem with low-frequency oscillation (LFO) in power system. Identification and monitoring of LFO in power grid are prerequisites for effective control of low-frequency oscillation phenomena. To address matter that the traditional S-transform time-frequency window function has a fixed scale and cannot be applied to the specific local characteristics of different signals, an adaptive generalized S-transform algorithm based on a bat algorithm is proposed in this paper. It uses adjustment parameters to control the generalized Gaussian window function. The parameters are automatically adjusted by a bat algorithm adaptive optimization to find the best time-frequency characterization. Secondly, the PMU data waveform with implicit low-frequency oscillation information is converted into a two-dimensional time-frequency figure including the onset moment, frequency, and amplitude. The system enables identification and visual monitoring of low-frequency oscillations. After that, simulation experiments of New England system are conducted. The superiority of the proposed method is verified, which can greatly improve the time-frequency resolution of PMU active power data signal and has effective noise immunity.