Second order blind identification algorithm with exact model order estimation for harmonic and interharmonic decomposition with reduced complexity

• Published in: International journal of electrical power & energy systems 2021-02, Vol.125, p.106415, Article 106415
• Main Authors: de Oliveira, Daniel Ramalho, Lima, Marcelo Antonio Alves, Silva, Leandro Rodrigues Manso, Ferreira, Danton Diego, Duque, Carlos Augusto
• Format: Article
• Language: English
• Subjects: Blind source separation; Harmonics; Interharmonics; Power quality; Second-order statistics
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2020.106415

•Estimation of model order of sinusoidal components.•Harmonic and Interharmonic decomposition using Second Order Blind Identification (SOBI).•Modification in conventional SOBI algorithm to reduce computational complexity.•Comparison of the proposed method with the SCICA and EMO-ESPRIT methods.•Better performance of the proposed method compared with the conventional one in noisy and time-varying environments. 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 proposes the utilization of signal processing techniques to decompose the electrical voltage and/or current signals into its harmonic and interhamonic component waveforms through a Blind Source Separation (BSS) algorithm named Second Order Blind Identification (SOBI). This algorithm is normally applied to a multivariate data set, what implies in a necessity of multiple measurements in different points of the system that will be analyzed. However, Single-Channel Blind Source Separation (SCBSS) method will be proposed in this work to estimate the components via SOBI using only one measured signal point. The method works as a set of adaptive filters whose coefficients are blindly obtained via SOBI and is responsible for the components separation. An Exact Model Order (EMO) algorithm will be used to improve the performance of the SOBI algorithm in order to estimate the correct number of components to be separated. Also, the EMO will be helpful to reduce the computational complexity of the SOBI. The performance of the proposed SCBSS method will be compared to that of the SCICA (Single-Channel Independent Component Analysis) based on the well-known FastICA algorithm, which employs Higher Order Statistics (HOS). It will be shown that the proposed SCBSS overtakes the SCICA for harmonic and interharmonic decomposition in performance and in reduced computational complexity. Also, the proposed SCBSS method will be compared to EMO-ESPRIT algorithm, where will be shown that the SCBSS achieved better 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.