## 1 Introduction  
Power quality is an essential feature in modern electrical systems. The increase in the number of time-variant and non-linear loads and the proliferation of distributed generation is affecting the performance of electric networks [(1; 2)]. One of the main aspects to take care of is the harmonic content. Nonlinear loads and switched devices produce harmonics and interharmonics, increasing energy losses and producing measurement errors, equipment overheating, communication interferences, and light flicker [(3; 4)]. New technologies, such as smart metering, renewable energy sources, electric vehicles, and other end-user equipment connected to the grid through static converters are responsible for interharmonic emissions in the range above 2 kHz (known as supraharmonics); thus, expanding the interest of researchers on energy quality [(5; 6)]. With the increase of non-linear loads in power systems, interharmonics are becoming a growing concern [(7; 8)]. The presence of interharmonics complicates the analysis and measurement due to the change of the periodicity of the waveform and a greater sensitivity to desynchronization problems. All this leads to the appearance of spectral leakage when applying methods based on the Fouriertransform, giving rise to instability and unreliability in the RMS (Root Mean Squared) values obtained by interacting with the leakage produced by nearby interharmonics. In recent years, several signal processing methods have been proposed to enhance the identification of the interharmonic content [9; 10; 11; 12]. Fourier transform, wavelet transform, Hilbert-Huang transform, and Chirp z-transform are the main non-parametric methods applied to power system waveform analysis. Parametric methods have been proposed too to perform the analysis, such as Kalman filters, ESPRIT (estimation of signal parameters by rotational invariance technique), MUSIC (multiple signal classification), and the Prony method. Hybrid methods provide high-frequency resolution and better estimation accuracy. Other modern techniques, such as enhanced the phase locked loop (EPLL), artificial neural network, and adaptive linear neural network (ADALINE) have also been used in power quality analysis. Parametric methods achieve higher frequency resolution, with tracking capabilities for time-varying harmonics and interharmonics, and are more suitable for interharmonic measurement than non-parametric methods. However, according to [11], these algorithms are time-consuming and not robust to noise and outliers, and their performance depends on the prior information of the order of the model. Non-parametric methods estimate harmonics directly from the signal, being less computationally expensive and easier to apply than parametric or hybrid methods [12]. The most common non-parametric method is the discrete Fourier transform (DFT), which is sufficiently robust and time-efficient, which justifies the use of DFT-based methods in power quality analyzers. However, the signal sampling time limits the frequency resolution and it may suffer from spectral leakage caused by fundamental frequency deviation and interharmonics [11]. The International Electrotechnical Commission (IEC) standards 61000-4-7 [13] and 61000-4-30 [14], propose the use of the DFT transform and describe the spectral and time groupings required to perform power quality measurements. Harmonic and interharmonic grouping simplifies the need to know the frequency location and amplitude of a single component. With frequency grouping, the 61000-4-7 standard aims to collect most of the spectral leakage produced within each group, minimizing the error due to this leakage that spreads out of the group, (although it also picks up more spectral leakage from outside of the group). Time grouping, contemplated in standard 61000-4-30, partially solves the information loss due to leakage, increasing the accuracy, especially in the total power, and thus minimizing the error due to the lack of stationarity of the signal.