Contents lists available at ScienceDirect Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes Second order blind identification algorithm with exact model order estimation for harmonic and interharmonic decomposition with reduced complexity Daniel Ramalho de Oliveira a , Marcelo Antonio Alves Lima a , Leandro Rodrigues Manso Silva a , Danton Diego Ferreira b , Carlos Augusto Duque a, ⁎ a PSCOPE-SignalProcessingandComputationalIntelligenceforPowerSystems,ElectricalEngineeringProgram,FederalUniversityofJuizdeFora,JuizdeFora,MG36. 036-900 Brazil b AIA - Artificial Intelligence and Automation Research Group, Department of Automation, Federal University of Lavras, Lavras, MG 37.200-900 Brazil ARTICLEINFO Keywords: Power quality Harmonics Interharmonics Blind source separation Second-order statistics ABSTRACT The power distribution network is susceptible to several Power Quality (PQ) disturbances. Among those, the harmonic and interharmonic distortions should be highlighted due to their high proliferation. This work pro- posestheutilizationofsignalprocessingtechniquestodecomposetheelectricalvoltageand/orcurrentsignals into its harmonic and interhamonic component waveforms through a Blind Source Separation (BSS) algorithm namedSecondOrderBlindIdentification(SOBI).Thisalgorithmisnormallyappliedtoamultivariatedataset, what implies in a necessity of multiple measurements in different points of the system that will be analyzed. However,Single-ChannelBlindSourceSeparation(SCBSS)methodwillbeproposedinthisworktoestimatethe componentsviaSOBIusingonlyonemeasuredsignalpoint.Themethodworksasasetofadaptivefilterswhose coefficients are blindly obtained via SOBI and is responsible for the components separation. An Exact Model Order(EMO)algorithmwillbeusedtoimprovetheperformanceoftheSOBIalgorithminordertoestimatethe correct number of components to be separated. Also, the EMO will be helpful to reduce the computational complexityoftheSOBI.TheperformanceoftheproposedSCBSSmethodwillbecomparedtothatoftheSCICA (Single-ChannelIndependentComponentAnalysis)basedonthewell-knownFastICAalgorithm,whichemploys HigherOrderStatistics(HOS).ItwillbeshownthattheproposedSCBSSovertakestheSCICAforharmonicand interharmonic decomposition in performance and in reduced computational complexity. Also, the proposed SCBSSmethodwillbecomparedtoEMO-ESPRITalgorithm,wherewillbeshownthattheSCBSSachievedbetter results in noisy and time-varying scenarios. Finally, the proposed SCBSS will be applied for the analysis of a voltage signal acquired from the simulation of a power system containing wind generation. 1. Introduction Duetothegeneralizeduseofnonlinearloads,suchasindustrialarc furnacesandpowerelectronicdevices,somePowerQuality(PQ)issues become evident. Among the PQ disturbances, the harmonic and inter- harmonic distortions are highlighted as they can cause an impact on powersystemreliability,operationandprotection [1].Inthisway,the harmonic and interharmonic distortion problem needs accurate esti- mations and reliable mitigation actions. Thus, standards like the IEEE 519-2014andtheIEC61,000havebeendevelopedforthisissue [2–5]. For the harmonic and interharmonic estimation, the Fast Fourier Transform (FFT) is the most widely used technique and is re- commended by IEC-61000-4-7 standard [5]. The FFT gives accurate estimationwhenthefundamentalfrequencyofthesystemisfixedand equal to the nominal value (50 Hz or 60 Hz, depending on the power system) and/or the analysis window length covers exactly an integer number of cycles of the fundamental component. However, due to the power unbalance between generation and load, the frequency of the system may vary. In addition, the presence of interharmonic compo- nents in frequencies which are not integer multiple of the FFT fre- quency resolution also deteriorates its results. In this situation, im- provements in FFT-based methods should be used. For example, to reduce the spectrum leakage problem, a special window and inter- polationtechniquewereproposedin [6].However,FFT-basedmethods relyontheconceptofparameterestimationratherthanontheconcept of waveform decomposition or source separation which are addressed https://doi.org/10.1016/j.ijepes.2020.106415 Received17February2020;Receivedinrevisedform27July2020;Accepted30July2020 ⁎ Corresponding author. E-mail address: carlos.duque@ufjf.edu.br (C.A. Duque). Electrical Power and Energy Systems 125 (2021) 106415 0142-0615/ © 2020 Elsevier Ltd. All rights reserved. T