Interharmonics analysis of power signals with fundamental frequency deviation using Kalman filtering

Interharmonics in PV systems: a review of analysis and estimation methods; considerations for selection of an apt method

“…Grouping of proposed methodologies parametric/model-based methods spectral model optimisation with high-resolution windowing [15] general noise resilient technique based on MPM [16] robust and adaptive detection in active distribution networks [17] SW-ESPRIT [18] Prony's method [19,20] linear LS method and SVD [21] AR model and Burg algorithm [22] Kalman filtering [23,24] Pisarenko's method [25] min-norm method [26] multiple signal classification [24] subspace-based methods [24] slow sampling and modified gradient search algorithm [27] non-parametric/DFT-based advanced methods DP technique [28] time-domain averaging and DF [29] DFT-based recursive group-harmonic energy distribution [30] interpolating windowed FFT algorithm [31] adaptive window width for interharmonic estimation [32] iterative weighted average phasor method [32] leakage estimation methods [33] synthetic resampling method [34] self-tuning algorithm for harmonic and interharmonic estimations [35] spectral correction method [36] statistical techniques single-channel ICA [37,38] maximum-likelihood estimators [39] SVM algorithm [40] AR model and Burg algorithm [22] machine learning methods SVM algorithm [40] artificial neural network [41,42] generalised optimisation methods PSO [43] FB-based methods filter-based methods with PLL for synchronisation • PLL-based multirate structure for interharmonic estimation [44] • Digital PLL and notch filter for interharmonic estimation…”

Considerations on signal processing for power systems in the context of Smart Grids

“…These variations of the fundamental or industrial frequencies can occur due to disturbances, its subsequent transients, the dramatic real power fluctuations of loads or the increasing integration of intermittent-renewable distributed generation [8]. These can lead to errors in current methodologies.…”

Review of signal processing techniques for time-varying harmonic decomposition

“…The fundamental or industrial frequency can vary due to various reasons such as disturbances, subsequent transients or the dramatic real power fluctuations of loads and the increasing integration of intermittent-renewable distributed generation [14] Due to non-uniformity of the filters' bandwidth, variation in the fundamental frequency leads to errors in splitting frequencies by using multi-resolution analyses. To illustrate this fact a voltage signal with fundamental amplitude of 1 pu and 7th harmonic of amplitude 0.5 pu with 1 Hz fundamental frequency variation were used.…”