Optimal Model Approximation of Linear Time-Invariant Systems Using the Enhanced DE Algorithm and Improved MPPA Method
Abstract In this paper, the authors propose a novel model order reduction method integrating evolutionary and conventional approaches for higher-order linear time-invariant single-input–single-output (SISO) and multi-input–multi-output (MIMO) dynamic systems. The proposed method makes use of a diffe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Vasu, Ganji [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
Enhanced differential evolution algorithm |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2019 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Circuits, systems and signal processing - Springer US, 1982, 39(2019), 5 vom: 25. Sept., Seite 2376-2411 |
|---|---|
| Übergeordnetes Werk: |
volume:39 ; year:2019 ; number:5 ; day:25 ; month:09 ; pages:2376-2411 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00034-019-01259-y |
|---|
| Katalog-ID: |
OLC2034857909 |
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| 520 | |a Abstract In this paper, the authors propose a novel model order reduction method integrating evolutionary and conventional approaches for higher-order linear time-invariant single-input–single-output (SISO) and multi-input–multi-output (MIMO) dynamic systems. The proposed method makes use of a differential evolution algorithm with enhanced mutation operation for the determination of reduced order model (ROM) denominator polynomial coefficients. In addition, an improved multi-point Padé approximation method is used to determine the optimal ROM numerator polynomial coefficients. The optimum property of the ROM is measured by minimising the integral square of the step response error between the original high-order dynamic system and the ROM. In the case of the MIMO system reduction approach, an optimal ROM transfer function matrix is determined by minimising a single objective function. This objective function is defined by a linear scalarising of the multi-step error function matrix components $$ \left( {E_{ij} } \right) $$. The proposed method guarantees the preservation of the stability, passivity and accuracy of the original higher-order system in the ROM. The proposed method is validated by applying it to a ninth-order SISO system, as well as to the tenth- and sixth-order linearised single-machine infinite-bus power system model with and without an automatic excitation control system. The simulation results and the comparison of the integral square error and impulse response energy values of the ROM demonstrate the dominance of the proposed method over the latest reduction methods available in the literature. | ||
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| 700 | 1 | |a Ramalingaraju, Manyala |4 aut | |
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10.1007/s00034-019-01259-y doi (DE-627)OLC2034857909 (DE-He213)s00034-019-01259-y-p DE-627 ger DE-627 rakwb eng 600 VZ Vasu, Ganji verfasserin aut Optimal Model Approximation of Linear Time-Invariant Systems Using the Enhanced DE Algorithm and Improved MPPA Method 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract In this paper, the authors propose a novel model order reduction method integrating evolutionary and conventional approaches for higher-order linear time-invariant single-input–single-output (SISO) and multi-input–multi-output (MIMO) dynamic systems. The proposed method makes use of a differential evolution algorithm with enhanced mutation operation for the determination of reduced order model (ROM) denominator polynomial coefficients. In addition, an improved multi-point Padé approximation method is used to determine the optimal ROM numerator polynomial coefficients. The optimum property of the ROM is measured by minimising the integral square of the step response error between the original high-order dynamic system and the ROM. In the case of the MIMO system reduction approach, an optimal ROM transfer function matrix is determined by minimising a single objective function. This objective function is defined by a linear scalarising of the multi-step error function matrix components $$ \left( {E_{ij} } \right) $$. The proposed method guarantees the preservation of the stability, passivity and accuracy of the original higher-order system in the ROM. The proposed method is validated by applying it to a ninth-order SISO system, as well as to the tenth- and sixth-order linearised single-machine infinite-bus power system model with and without an automatic excitation control system. The simulation results and the comparison of the integral square error and impulse response energy values of the ROM demonstrate the dominance of the proposed method over the latest reduction methods available in the literature. Optimal model approximation Enhanced differential evolution algorithm Improved multi-point Padé approximation SISO and MIMO systems Sivakumar, Mangipudi (orcid)0000-0002-0168-936X aut Ramalingaraju, Manyala aut Enthalten in Circuits, systems and signal processing Springer US, 1982 39(2019), 5 vom: 25. Sept., Seite 2376-2411 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:39 year:2019 number:5 day:25 month:09 pages:2376-2411 https://doi.org/10.1007/s00034-019-01259-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2244 AR 39 2019 5 25 09 2376-2411 |
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10.1007/s00034-019-01259-y doi (DE-627)OLC2034857909 (DE-He213)s00034-019-01259-y-p DE-627 ger DE-627 rakwb eng 600 VZ Vasu, Ganji verfasserin aut Optimal Model Approximation of Linear Time-Invariant Systems Using the Enhanced DE Algorithm and Improved MPPA Method 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract In this paper, the authors propose a novel model order reduction method integrating evolutionary and conventional approaches for higher-order linear time-invariant single-input–single-output (SISO) and multi-input–multi-output (MIMO) dynamic systems. The proposed method makes use of a differential evolution algorithm with enhanced mutation operation for the determination of reduced order model (ROM) denominator polynomial coefficients. In addition, an improved multi-point Padé approximation method is used to determine the optimal ROM numerator polynomial coefficients. The optimum property of the ROM is measured by minimising the integral square of the step response error between the original high-order dynamic system and the ROM. In the case of the MIMO system reduction approach, an optimal ROM transfer function matrix is determined by minimising a single objective function. This objective function is defined by a linear scalarising of the multi-step error function matrix components $$ \left( {E_{ij} } \right) $$. The proposed method guarantees the preservation of the stability, passivity and accuracy of the original higher-order system in the ROM. The proposed method is validated by applying it to a ninth-order SISO system, as well as to the tenth- and sixth-order linearised single-machine infinite-bus power system model with and without an automatic excitation control system. The simulation results and the comparison of the integral square error and impulse response energy values of the ROM demonstrate the dominance of the proposed method over the latest reduction methods available in the literature. Optimal model approximation Enhanced differential evolution algorithm Improved multi-point Padé approximation SISO and MIMO systems Sivakumar, Mangipudi (orcid)0000-0002-0168-936X aut Ramalingaraju, Manyala aut Enthalten in Circuits, systems and signal processing Springer US, 1982 39(2019), 5 vom: 25. Sept., Seite 2376-2411 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:39 year:2019 number:5 day:25 month:09 pages:2376-2411 https://doi.org/10.1007/s00034-019-01259-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2244 AR 39 2019 5 25 09 2376-2411 |
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10.1007/s00034-019-01259-y doi (DE-627)OLC2034857909 (DE-He213)s00034-019-01259-y-p DE-627 ger DE-627 rakwb eng 600 VZ Vasu, Ganji verfasserin aut Optimal Model Approximation of Linear Time-Invariant Systems Using the Enhanced DE Algorithm and Improved MPPA Method 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract In this paper, the authors propose a novel model order reduction method integrating evolutionary and conventional approaches for higher-order linear time-invariant single-input–single-output (SISO) and multi-input–multi-output (MIMO) dynamic systems. The proposed method makes use of a differential evolution algorithm with enhanced mutation operation for the determination of reduced order model (ROM) denominator polynomial coefficients. In addition, an improved multi-point Padé approximation method is used to determine the optimal ROM numerator polynomial coefficients. The optimum property of the ROM is measured by minimising the integral square of the step response error between the original high-order dynamic system and the ROM. In the case of the MIMO system reduction approach, an optimal ROM transfer function matrix is determined by minimising a single objective function. This objective function is defined by a linear scalarising of the multi-step error function matrix components $$ \left( {E_{ij} } \right) $$. The proposed method guarantees the preservation of the stability, passivity and accuracy of the original higher-order system in the ROM. The proposed method is validated by applying it to a ninth-order SISO system, as well as to the tenth- and sixth-order linearised single-machine infinite-bus power system model with and without an automatic excitation control system. The simulation results and the comparison of the integral square error and impulse response energy values of the ROM demonstrate the dominance of the proposed method over the latest reduction methods available in the literature. Optimal model approximation Enhanced differential evolution algorithm Improved multi-point Padé approximation SISO and MIMO systems Sivakumar, Mangipudi (orcid)0000-0002-0168-936X aut Ramalingaraju, Manyala aut Enthalten in Circuits, systems and signal processing Springer US, 1982 39(2019), 5 vom: 25. Sept., Seite 2376-2411 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:39 year:2019 number:5 day:25 month:09 pages:2376-2411 https://doi.org/10.1007/s00034-019-01259-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2244 AR 39 2019 5 25 09 2376-2411 |
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10.1007/s00034-019-01259-y doi (DE-627)OLC2034857909 (DE-He213)s00034-019-01259-y-p DE-627 ger DE-627 rakwb eng 600 VZ Vasu, Ganji verfasserin aut Optimal Model Approximation of Linear Time-Invariant Systems Using the Enhanced DE Algorithm and Improved MPPA Method 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract In this paper, the authors propose a novel model order reduction method integrating evolutionary and conventional approaches for higher-order linear time-invariant single-input–single-output (SISO) and multi-input–multi-output (MIMO) dynamic systems. The proposed method makes use of a differential evolution algorithm with enhanced mutation operation for the determination of reduced order model (ROM) denominator polynomial coefficients. In addition, an improved multi-point Padé approximation method is used to determine the optimal ROM numerator polynomial coefficients. The optimum property of the ROM is measured by minimising the integral square of the step response error between the original high-order dynamic system and the ROM. In the case of the MIMO system reduction approach, an optimal ROM transfer function matrix is determined by minimising a single objective function. This objective function is defined by a linear scalarising of the multi-step error function matrix components $$ \left( {E_{ij} } \right) $$. The proposed method guarantees the preservation of the stability, passivity and accuracy of the original higher-order system in the ROM. The proposed method is validated by applying it to a ninth-order SISO system, as well as to the tenth- and sixth-order linearised single-machine infinite-bus power system model with and without an automatic excitation control system. The simulation results and the comparison of the integral square error and impulse response energy values of the ROM demonstrate the dominance of the proposed method over the latest reduction methods available in the literature. Optimal model approximation Enhanced differential evolution algorithm Improved multi-point Padé approximation SISO and MIMO systems Sivakumar, Mangipudi (orcid)0000-0002-0168-936X aut Ramalingaraju, Manyala aut Enthalten in Circuits, systems and signal processing Springer US, 1982 39(2019), 5 vom: 25. Sept., Seite 2376-2411 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:39 year:2019 number:5 day:25 month:09 pages:2376-2411 https://doi.org/10.1007/s00034-019-01259-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2244 AR 39 2019 5 25 09 2376-2411 |
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10.1007/s00034-019-01259-y doi (DE-627)OLC2034857909 (DE-He213)s00034-019-01259-y-p DE-627 ger DE-627 rakwb eng 600 VZ Vasu, Ganji verfasserin aut Optimal Model Approximation of Linear Time-Invariant Systems Using the Enhanced DE Algorithm and Improved MPPA Method 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract In this paper, the authors propose a novel model order reduction method integrating evolutionary and conventional approaches for higher-order linear time-invariant single-input–single-output (SISO) and multi-input–multi-output (MIMO) dynamic systems. The proposed method makes use of a differential evolution algorithm with enhanced mutation operation for the determination of reduced order model (ROM) denominator polynomial coefficients. In addition, an improved multi-point Padé approximation method is used to determine the optimal ROM numerator polynomial coefficients. The optimum property of the ROM is measured by minimising the integral square of the step response error between the original high-order dynamic system and the ROM. In the case of the MIMO system reduction approach, an optimal ROM transfer function matrix is determined by minimising a single objective function. This objective function is defined by a linear scalarising of the multi-step error function matrix components $$ \left( {E_{ij} } \right) $$. The proposed method guarantees the preservation of the stability, passivity and accuracy of the original higher-order system in the ROM. The proposed method is validated by applying it to a ninth-order SISO system, as well as to the tenth- and sixth-order linearised single-machine infinite-bus power system model with and without an automatic excitation control system. The simulation results and the comparison of the integral square error and impulse response energy values of the ROM demonstrate the dominance of the proposed method over the latest reduction methods available in the literature. Optimal model approximation Enhanced differential evolution algorithm Improved multi-point Padé approximation SISO and MIMO systems Sivakumar, Mangipudi (orcid)0000-0002-0168-936X aut Ramalingaraju, Manyala aut Enthalten in Circuits, systems and signal processing Springer US, 1982 39(2019), 5 vom: 25. Sept., Seite 2376-2411 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:39 year:2019 number:5 day:25 month:09 pages:2376-2411 https://doi.org/10.1007/s00034-019-01259-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2244 AR 39 2019 5 25 09 2376-2411 |
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Circuits, systems and signal processing |
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Circuits, systems and signal processing |
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eng |
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600 - Technology |
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2019 |
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2376 |
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Vasu, Ganji Sivakumar, Mangipudi Ramalingaraju, Manyala |
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39 |
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Vasu, Ganji |
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10.1007/s00034-019-01259-y |
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(ORCID)0000-0002-0168-936X |
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(orcid)0000-0002-0168-936X |
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600 |
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optimal model approximation of linear time-invariant systems using the enhanced de algorithm and improved mppa method |
| title_auth |
Optimal Model Approximation of Linear Time-Invariant Systems Using the Enhanced DE Algorithm and Improved MPPA Method |
| abstract |
Abstract In this paper, the authors propose a novel model order reduction method integrating evolutionary and conventional approaches for higher-order linear time-invariant single-input–single-output (SISO) and multi-input–multi-output (MIMO) dynamic systems. The proposed method makes use of a differential evolution algorithm with enhanced mutation operation for the determination of reduced order model (ROM) denominator polynomial coefficients. In addition, an improved multi-point Padé approximation method is used to determine the optimal ROM numerator polynomial coefficients. The optimum property of the ROM is measured by minimising the integral square of the step response error between the original high-order dynamic system and the ROM. In the case of the MIMO system reduction approach, an optimal ROM transfer function matrix is determined by minimising a single objective function. This objective function is defined by a linear scalarising of the multi-step error function matrix components $$ \left( {E_{ij} } \right) $$. The proposed method guarantees the preservation of the stability, passivity and accuracy of the original higher-order system in the ROM. The proposed method is validated by applying it to a ninth-order SISO system, as well as to the tenth- and sixth-order linearised single-machine infinite-bus power system model with and without an automatic excitation control system. The simulation results and the comparison of the integral square error and impulse response energy values of the ROM demonstrate the dominance of the proposed method over the latest reduction methods available in the literature. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstractGer |
Abstract In this paper, the authors propose a novel model order reduction method integrating evolutionary and conventional approaches for higher-order linear time-invariant single-input–single-output (SISO) and multi-input–multi-output (MIMO) dynamic systems. The proposed method makes use of a differential evolution algorithm with enhanced mutation operation for the determination of reduced order model (ROM) denominator polynomial coefficients. In addition, an improved multi-point Padé approximation method is used to determine the optimal ROM numerator polynomial coefficients. The optimum property of the ROM is measured by minimising the integral square of the step response error between the original high-order dynamic system and the ROM. In the case of the MIMO system reduction approach, an optimal ROM transfer function matrix is determined by minimising a single objective function. This objective function is defined by a linear scalarising of the multi-step error function matrix components $$ \left( {E_{ij} } \right) $$. The proposed method guarantees the preservation of the stability, passivity and accuracy of the original higher-order system in the ROM. The proposed method is validated by applying it to a ninth-order SISO system, as well as to the tenth- and sixth-order linearised single-machine infinite-bus power system model with and without an automatic excitation control system. The simulation results and the comparison of the integral square error and impulse response energy values of the ROM demonstrate the dominance of the proposed method over the latest reduction methods available in the literature. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract In this paper, the authors propose a novel model order reduction method integrating evolutionary and conventional approaches for higher-order linear time-invariant single-input–single-output (SISO) and multi-input–multi-output (MIMO) dynamic systems. The proposed method makes use of a differential evolution algorithm with enhanced mutation operation for the determination of reduced order model (ROM) denominator polynomial coefficients. In addition, an improved multi-point Padé approximation method is used to determine the optimal ROM numerator polynomial coefficients. The optimum property of the ROM is measured by minimising the integral square of the step response error between the original high-order dynamic system and the ROM. In the case of the MIMO system reduction approach, an optimal ROM transfer function matrix is determined by minimising a single objective function. This objective function is defined by a linear scalarising of the multi-step error function matrix components $$ \left( {E_{ij} } \right) $$. The proposed method guarantees the preservation of the stability, passivity and accuracy of the original higher-order system in the ROM. The proposed method is validated by applying it to a ninth-order SISO system, as well as to the tenth- and sixth-order linearised single-machine infinite-bus power system model with and without an automatic excitation control system. The simulation results and the comparison of the integral square error and impulse response energy values of the ROM demonstrate the dominance of the proposed method over the latest reduction methods available in the literature. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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Optimal Model Approximation of Linear Time-Invariant Systems Using the Enhanced DE Algorithm and Improved MPPA Method |
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Sivakumar, Mangipudi Ramalingaraju, Manyala |
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2024-07-03T22:44:39.026Z |
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