Damping Undammed Low Frequency Oscillations in Power Systems: An ANN-Based Approach Using Pre-Disturbance Data

Abstract

The paper examines undammed low frequency oscillations (LFOs) that can lead to system collapse, citing the Jordan power network incident on May 21, 2021. Traditional model-based methods for studying LFOs' small-signal stability have limitations. To address this, an online damping controller based on an artificial neural network (ANN) is proposed. Unlike existing ANN-based methods relying on offline controllers, this novel approach utilizes pre-disturbance data from phasor measurement units (PMUs) to dampen oscillations effectively. The paper addresses challenges of partially observable systems in online eigenvalue prediction using ANN. MATLAB is used to implement a feedforward ANN system trained on PMU data. The study involves a three-area test system with various operational scenarios, training the ANN across 406 scenarios to predict eigenvalues and damp LFOs.