A Novel Neural Network Approach for Power System Low Frequency Oscillation Mode Identification

This paper proposes a novel neural network approach for low frequency oscillation mode identification in power systems. After employing the fast Fourier transform in order selection, we then introduce a specific neural network whose topology strictly follows the exponentially damped sinusoidal model of low frequency oscillation signals. We thus turn the parameter estimation into an optimization problem. Simulations show that the proposed approach is superior to the state-of-art neural network schemes in anti-noise ability, parameter accuracy and computation speed.