Predictive frequency-based sequence estimator for control of grid-tied converters under highly distorted conditions
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IEEE
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This paper proposes a novel frequency-based predictive sequence estimator that allows for the isolation of voltages and currents harmonic components needed for the control of grid-tied converters. The proposed method relays on an enhanced Sliding Goertzel Transformation (SGT) by adding a predictive estimator with a prediction horizon equal to the SGT processing window. The performance of the proposed method is compared with the well-established DSOGI alternative, proving a higher estimation bandwidth as well as improved immunity to changes in the magnitude, frequency and phase of the tracked signals. Additionally, the close-loop performance in a current-controlled grid-tied inverter using the proposed sequence extractor is analyzed. The presented results allow to quantitatively measure the estimator impact over the power converter performance in a real application
This paper proposes a novel frequency-based predictive sequence estimator that allows for the isolation of voltages and currents harmonic components needed for the control of grid-tied converters. The proposed method relays on an enhanced Sliding Goertzel Transformation (SGT) by adding a predictive estimator with a prediction horizon equal to the SGT processing window. The performance of the proposed method is compared with the well-established DSOGI alternative, proving a higher estimation bandwidth as well as improved immunity to changes in the magnitude, frequency and phase of the tracked signals. Additionally, the close-loop performance in a current-controlled grid-tied inverter using the proposed sequence extractor is analyzed. The presented results allow to quantitatively measure the estimator impact over the power converter performance in a real application
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El presente trabajo ha sido parcialmente financiado por las becas predoctorales programa Severo Ochoa para la formación en investigación y docencia universitaria de Principado de Asturias, PCTI-FICYT, ID BP14-135. También apoyado por el Gobierno de España, Ministerio de Economía y Competitividad, ENE2016-77919-R, y por la Unión Europea a través de los Fondos FEDER