A Refined Taylor-Fourier Transform with Applications to Wideband Oscillation Monitoring

The recent increase in renewable energy adoption has enhanced the penetration rate of electronic equipment, leading to an increased risk of wideband oscillations. Existing wide-area measurement systems mainly focus on fundamental phasors, which cannot effectively monitor wideband oscillations. This study presents an accurate wideband oscillation monitoring method based on radial basis function (RBF) neural networks and Taylor-Fourier transform (TFT). First, discrete Fourier transform is used to obtain a preliminary estimation of the oscillation signal, and then, TFT is adopted to obtain a precise estimation even under dynamic conditions. To reduce the computational burden of TFT, an RBF neural network is used for noise intensity estimation, which adaptively determines the window length. Finally, the proposed method is verified by synthetic data and the field data collected from Guyuan and Hami, China. The experimental results show that the RBF neural network has an excellent denoising effect. When the signal-to-noise ratio is 45 dB, the maximum overall phasor error and the maximum frequency error are 1% and 0.01 Hz, respectively. Hence, it is expected to be useful for next-generation monitoring systems.