A New Advanced Method for an Accurate Assessment of Harmonic and Supraharmonic Distortion in Power System Waveforms

Waveforms distortion is a pressing concern in Smart Grids where a massive presence of new technologies in distributed energy resources and in advanced smart metering systems is expected. In this context, the increasing diffusion of high switching frequencies static converters and the growing usage of Power Line Communication push for research dealing with the assessment of waveforms with spectral components up to 150 kHz. The analysis of such waveforms is a challenge for researchers due to the contemporaneous presence of a high number of spectral components in the range of low- (up to 2 kHz) and high- (up to 150 kHz) frequencies, with their opposite needs in term of time window length (and frequency resolution). The main idea of this paper is to improve the performances of existing methods by using a joint method of analysis based on a profitable strategy of divide and conquer; the method guarantees the best compromise between accuracy and computational efforts. A Discrete Wavelet Transform initially divides the original waveform to obtain two frequency bands: the wavelet suitability for conducting multi-resolution time-frequency analysis on waveforms in different frequency bands with different frequency resolution is effectively exploited. Then, the sliding-window modified ESPRIT method and the sliding-window Discrete Fourier Transform which uses a Nuttal window are used for the analysis of the low- and high-frequency bands, respectively; the positive characteristics of each method are exploited, minimizing the drawbacks and integrating their behavior so that the whole joint method allows an accurate estimation of each low- and high-frequency spectral component with the required acceptable computational efforts. The proposed method is tested on synthetic and measured waveforms in terms of accuracy and computational efforts. The analysis of the numerical application results clearly reveals that the proposed method improves the performances of existing methods of analysis in the examined cases.