In the active distribution networks (ADN), the interharmonic distortion level is aggravated due to the growing penetration of distributed generations and wide application of power-electronic loads. The interharmonics, even at a low amplitude level, can cause power-quality problems, deteriorate the measurement accuracies of the advanced metering infrastructures, and increase the possibilities of power system oscillations. In this paper, an interharmonic detection scheme is proposed to effectively extract the interharmonic components of the power system even with the extremely noisy signals, and then to estimate their frequencies and magnitudes. In the scheme, a totally data-dependent threshold is proposed to detect interharmonics adaptively. This adaptive threshold is determined iteratively based on the statistical character of the background noise bins. And then the time-varying frequencies and magnitudes of the detected interharmonics are estimated using the time-domain method. Case studies show that the adaptive threshold is robust under the conditions with high noisy signal, and the proposed scheme can estimate precisely the frequencies and magnitudes of the interharmonics detected for both the steady-state and dynamic interharmonic conditions. Furthermore, the proposed scheme is also verified using the field 10-kV current signal of one fuel company and 0.4-kV voltage signal of one electric vehicle's charging station.
- active distribution network (ADN)
- Taylor-Fourier transform (TFT)