Power response optimization of inverter grid parallel operation using P-ω and Q-V curves and phase feeedback based on genetic algorithm

Helder Zandonadi Maia; João Onofre Pereira Pinto; Ernane Antônio Alves Coelho

doi:10.1109/IECON.2007.4460330

Power response optimization of inverter grid parallel operation using P-ω and Q-V curves and phase feeedback based on genetic algorithm

Helder Zandonadi Maia, João Onofre Pereira Pinto, Ernane Antônio Alves Coelho

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

The aim of this work is to optimize the response of a scheme to parallel an inverter to the grid. The approach not only is based on P-ω and Q-Vcurves, but also in phase angle feedback. This scheme requires determination of 4 parameters (K_d, K_p, K_v, ω_f), which interfere in the power dynamic response (overshoot and rise time). Trial and error method was used in the past resulting in poor performance. In this work, genetic algorithm is used to find the parameters set that leads to near optimum response. A brief review of the paralleling approach and the GA based methodology is presented; simulation results are given showing its feasibility.

Original language	English
Title of host publication	Proceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society, IECON
Pages	1679-1684
Number of pages	6
DOIs	https://doi.org/10.1109/IECON.2007.4460330
State	Published - 2007
Externally published	Yes
Event	33rd Annual Conference of the IEEE Industrial Electronics Society, IECON - Taipei, Taiwan, Province of China Duration: Nov 5 2007 → Nov 8 2007

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)

Conference

Conference	33rd Annual Conference of the IEEE Industrial Electronics Society, IECON
Country/Territory	Taiwan, Province of China
City	Taipei
Period	11/5/07 → 11/8/07

Access to Document

10.1109/IECON.2007.4460330

Cite this

Maia, H. Z., Pinto, J. O. P., & Coelho, E. A. A. (2007). Power response optimization of inverter grid parallel operation using P-ω and Q-V curves and phase feeedback based on genetic algorithm. In Proceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society, IECON (pp. 1679-1684). Article 4460330 (IECON Proceedings (Industrial Electronics Conference)). https://doi.org/10.1109/IECON.2007.4460330

Maia, Helder Zandonadi ; Pinto, João Onofre Pereira ; Coelho, Ernane Antônio Alves. / Power response optimization of inverter grid parallel operation using P-ω and Q-V curves and phase feeedback based on genetic algorithm. Proceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society, IECON. 2007. pp. 1679-1684 (IECON Proceedings (Industrial Electronics Conference)).

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title = "Power response optimization of inverter grid parallel operation using P-ω and Q-V curves and phase feeedback based on genetic algorithm",

abstract = "The aim of this work is to optimize the response of a scheme to parallel an inverter to the grid. The approach not only is based on P-ω and Q-Vcurves, but also in phase angle feedback. This scheme requires determination of 4 parameters (Kd, Kp, Kv, ωf), which interfere in the power dynamic response (overshoot and rise time). Trial and error method was used in the past resulting in poor performance. In this work, genetic algorithm is used to find the parameters set that leads to near optimum response. A brief review of the paralleling approach and the GA based methodology is presented; simulation results are given showing its feasibility.",

author = "Maia, {Helder Zandonadi} and Pinto, {Jo{\~a}o Onofre Pereira} and Coelho, {Ernane Ant{\^o}nio Alves}",

year = "2007",

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language = "English",

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series = "IECON Proceedings (Industrial Electronics Conference)",

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note = "33rd Annual Conference of the IEEE Industrial Electronics Society, IECON ; Conference date: 05-11-2007 Through 08-11-2007",

}

Maia, HZ, Pinto, JOP & Coelho, EAA 2007, Power response optimization of inverter grid parallel operation using P-ω and Q-V curves and phase feeedback based on genetic algorithm. in Proceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society, IECON., 4460330, IECON Proceedings (Industrial Electronics Conference), pp. 1679-1684, 33rd Annual Conference of the IEEE Industrial Electronics Society, IECON, Taipei, Taiwan, Province of China, 11/5/07. https://doi.org/10.1109/IECON.2007.4460330

Power response optimization of inverter grid parallel operation using P-ω and Q-V curves and phase feeedback based on genetic algorithm. / Maia, Helder Zandonadi; Pinto, João Onofre Pereira; Coelho, Ernane Antônio Alves.
Proceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society, IECON. 2007. p. 1679-1684 4460330 (IECON Proceedings (Industrial Electronics Conference)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The aim of this work is to optimize the response of a scheme to parallel an inverter to the grid. The approach not only is based on P-ω and Q-Vcurves, but also in phase angle feedback. This scheme requires determination of 4 parameters (Kd, Kp, Kv, ωf), which interfere in the power dynamic response (overshoot and rise time). Trial and error method was used in the past resulting in poor performance. In this work, genetic algorithm is used to find the parameters set that leads to near optimum response. A brief review of the paralleling approach and the GA based methodology is presented; simulation results are given showing its feasibility.

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Maia HZ, Pinto JOP, Coelho EAA. Power response optimization of inverter grid parallel operation using P-ω and Q-V curves and phase feeedback based on genetic algorithm. In Proceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society, IECON. 2007. p. 1679-1684. 4460330. (IECON Proceedings (Industrial Electronics Conference)). doi: 10.1109/IECON.2007.4460330

Power response optimization of inverter grid parallel operation using P-ω and Q-V curves and phase feeedback based on genetic algorithm

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