Artificial Intelligence-Based Online Control Scheme for the Regulations of Interconnected Thermal Power Systems
Abstract In today’s constantly advancing economy, fast and efficient load frequency control (LFC) schemes are imperative for stable power systems operation, whether conventional or new. Despite recent advances in this domain, professional engineers, to this day, face significant obstacles in dealing...
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| Autor*in: |
Orka, Nabil Anan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© King Fahd University of Petroleum & Minerals 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 48(2023), 11 vom: 24. Juni, Seite 15153-15176 |
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| Übergeordnetes Werk: |
volume:48 ; year:2023 ; number:11 ; day:24 ; month:06 ; pages:15153-15176 |
| Links: |
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| DOI / URN: |
10.1007/s13369-023-07995-3 |
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SPR053287495 |
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| 520 | |a Abstract In today’s constantly advancing economy, fast and efficient load frequency control (LFC) schemes are imperative for stable power systems operation, whether conventional or new. Despite recent advances in this domain, professional engineers, to this day, face significant obstacles in dealing with parametric uncertainty and the rejection of external disturbances. To ensure high-quality, resilient, and consistent electrical power, the developed controllers must operate efficiently, i.e., counteract frequency and tie-line power deviations. In light of this, the current study presents a novel strategy employing several machine learning algorithms to remedy the LFC dilemma, estimating the optimal controller gain values in a real-time environment. A comprehensive case study incorporating random load perturbations exemplified the trained controllers’ accuracy, robustness, and adaptability. The proposed online controllers, integrated into emerging power systems with several adjoining regions, renewable sources, and nonlinearities, outperform benchmark procedures. A rank-based statistical analysis implementing the Friedman test reveals Random Forest to be the most effective of the studied algorithms in terms of providing the lowest integral-criteria error values while preserving the minimal peak overshoot, undershoot, and settling duration. This paper demonstrates the significant potential of the suggested framework in addressing the LFC predicament in relevant sectors. | ||
| 650 | 4 | |a Load frequency control |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Interconnected power systems |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Random load perturbations |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Nonlinearities |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Online control |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Muhaimin, Sheikh Samit |4 aut | |
| 700 | 1 | |a Shahi, Md. Nazmush Shakib |4 aut | |
| 700 | 1 | |a Ahmed, Ashik |4 aut | |
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10.1007/s13369-023-07995-3 doi (DE-627)SPR053287495 (SPR)s13369-023-07995-3-e DE-627 ger DE-627 rakwb eng Orka, Nabil Anan verfasserin (orcid)0000-0001-5251-2137 aut Artificial Intelligence-Based Online Control Scheme for the Regulations of Interconnected Thermal Power Systems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In today’s constantly advancing economy, fast and efficient load frequency control (LFC) schemes are imperative for stable power systems operation, whether conventional or new. Despite recent advances in this domain, professional engineers, to this day, face significant obstacles in dealing with parametric uncertainty and the rejection of external disturbances. To ensure high-quality, resilient, and consistent electrical power, the developed controllers must operate efficiently, i.e., counteract frequency and tie-line power deviations. In light of this, the current study presents a novel strategy employing several machine learning algorithms to remedy the LFC dilemma, estimating the optimal controller gain values in a real-time environment. A comprehensive case study incorporating random load perturbations exemplified the trained controllers’ accuracy, robustness, and adaptability. The proposed online controllers, integrated into emerging power systems with several adjoining regions, renewable sources, and nonlinearities, outperform benchmark procedures. A rank-based statistical analysis implementing the Friedman test reveals Random Forest to be the most effective of the studied algorithms in terms of providing the lowest integral-criteria error values while preserving the minimal peak overshoot, undershoot, and settling duration. This paper demonstrates the significant potential of the suggested framework in addressing the LFC predicament in relevant sectors. Load frequency control (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Interconnected power systems (dpeaa)DE-He213 Random load perturbations (dpeaa)DE-He213 Nonlinearities (dpeaa)DE-He213 Online control (dpeaa)DE-He213 Muhaimin, Sheikh Samit aut Shahi, Md. Nazmush Shakib aut Ahmed, Ashik aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2023), 11 vom: 24. Juni, Seite 15153-15176 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2023 number:11 day:24 month:06 pages:15153-15176 https://dx.doi.org/10.1007/s13369-023-07995-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2023 11 24 06 15153-15176 |
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10.1007/s13369-023-07995-3 doi (DE-627)SPR053287495 (SPR)s13369-023-07995-3-e DE-627 ger DE-627 rakwb eng Orka, Nabil Anan verfasserin (orcid)0000-0001-5251-2137 aut Artificial Intelligence-Based Online Control Scheme for the Regulations of Interconnected Thermal Power Systems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In today’s constantly advancing economy, fast and efficient load frequency control (LFC) schemes are imperative for stable power systems operation, whether conventional or new. Despite recent advances in this domain, professional engineers, to this day, face significant obstacles in dealing with parametric uncertainty and the rejection of external disturbances. To ensure high-quality, resilient, and consistent electrical power, the developed controllers must operate efficiently, i.e., counteract frequency and tie-line power deviations. In light of this, the current study presents a novel strategy employing several machine learning algorithms to remedy the LFC dilemma, estimating the optimal controller gain values in a real-time environment. A comprehensive case study incorporating random load perturbations exemplified the trained controllers’ accuracy, robustness, and adaptability. The proposed online controllers, integrated into emerging power systems with several adjoining regions, renewable sources, and nonlinearities, outperform benchmark procedures. A rank-based statistical analysis implementing the Friedman test reveals Random Forest to be the most effective of the studied algorithms in terms of providing the lowest integral-criteria error values while preserving the minimal peak overshoot, undershoot, and settling duration. This paper demonstrates the significant potential of the suggested framework in addressing the LFC predicament in relevant sectors. Load frequency control (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Interconnected power systems (dpeaa)DE-He213 Random load perturbations (dpeaa)DE-He213 Nonlinearities (dpeaa)DE-He213 Online control (dpeaa)DE-He213 Muhaimin, Sheikh Samit aut Shahi, Md. Nazmush Shakib aut Ahmed, Ashik aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2023), 11 vom: 24. Juni, Seite 15153-15176 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2023 number:11 day:24 month:06 pages:15153-15176 https://dx.doi.org/10.1007/s13369-023-07995-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2023 11 24 06 15153-15176 |
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10.1007/s13369-023-07995-3 doi (DE-627)SPR053287495 (SPR)s13369-023-07995-3-e DE-627 ger DE-627 rakwb eng Orka, Nabil Anan verfasserin (orcid)0000-0001-5251-2137 aut Artificial Intelligence-Based Online Control Scheme for the Regulations of Interconnected Thermal Power Systems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In today’s constantly advancing economy, fast and efficient load frequency control (LFC) schemes are imperative for stable power systems operation, whether conventional or new. Despite recent advances in this domain, professional engineers, to this day, face significant obstacles in dealing with parametric uncertainty and the rejection of external disturbances. To ensure high-quality, resilient, and consistent electrical power, the developed controllers must operate efficiently, i.e., counteract frequency and tie-line power deviations. In light of this, the current study presents a novel strategy employing several machine learning algorithms to remedy the LFC dilemma, estimating the optimal controller gain values in a real-time environment. A comprehensive case study incorporating random load perturbations exemplified the trained controllers’ accuracy, robustness, and adaptability. The proposed online controllers, integrated into emerging power systems with several adjoining regions, renewable sources, and nonlinearities, outperform benchmark procedures. A rank-based statistical analysis implementing the Friedman test reveals Random Forest to be the most effective of the studied algorithms in terms of providing the lowest integral-criteria error values while preserving the minimal peak overshoot, undershoot, and settling duration. This paper demonstrates the significant potential of the suggested framework in addressing the LFC predicament in relevant sectors. Load frequency control (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Interconnected power systems (dpeaa)DE-He213 Random load perturbations (dpeaa)DE-He213 Nonlinearities (dpeaa)DE-He213 Online control (dpeaa)DE-He213 Muhaimin, Sheikh Samit aut Shahi, Md. Nazmush Shakib aut Ahmed, Ashik aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2023), 11 vom: 24. Juni, Seite 15153-15176 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2023 number:11 day:24 month:06 pages:15153-15176 https://dx.doi.org/10.1007/s13369-023-07995-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2023 11 24 06 15153-15176 |
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10.1007/s13369-023-07995-3 doi (DE-627)SPR053287495 (SPR)s13369-023-07995-3-e DE-627 ger DE-627 rakwb eng Orka, Nabil Anan verfasserin (orcid)0000-0001-5251-2137 aut Artificial Intelligence-Based Online Control Scheme for the Regulations of Interconnected Thermal Power Systems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In today’s constantly advancing economy, fast and efficient load frequency control (LFC) schemes are imperative for stable power systems operation, whether conventional or new. Despite recent advances in this domain, professional engineers, to this day, face significant obstacles in dealing with parametric uncertainty and the rejection of external disturbances. To ensure high-quality, resilient, and consistent electrical power, the developed controllers must operate efficiently, i.e., counteract frequency and tie-line power deviations. In light of this, the current study presents a novel strategy employing several machine learning algorithms to remedy the LFC dilemma, estimating the optimal controller gain values in a real-time environment. A comprehensive case study incorporating random load perturbations exemplified the trained controllers’ accuracy, robustness, and adaptability. The proposed online controllers, integrated into emerging power systems with several adjoining regions, renewable sources, and nonlinearities, outperform benchmark procedures. A rank-based statistical analysis implementing the Friedman test reveals Random Forest to be the most effective of the studied algorithms in terms of providing the lowest integral-criteria error values while preserving the minimal peak overshoot, undershoot, and settling duration. This paper demonstrates the significant potential of the suggested framework in addressing the LFC predicament in relevant sectors. Load frequency control (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Interconnected power systems (dpeaa)DE-He213 Random load perturbations (dpeaa)DE-He213 Nonlinearities (dpeaa)DE-He213 Online control (dpeaa)DE-He213 Muhaimin, Sheikh Samit aut Shahi, Md. Nazmush Shakib aut Ahmed, Ashik aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2023), 11 vom: 24. Juni, Seite 15153-15176 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2023 number:11 day:24 month:06 pages:15153-15176 https://dx.doi.org/10.1007/s13369-023-07995-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2023 11 24 06 15153-15176 |
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10.1007/s13369-023-07995-3 doi (DE-627)SPR053287495 (SPR)s13369-023-07995-3-e DE-627 ger DE-627 rakwb eng Orka, Nabil Anan verfasserin (orcid)0000-0001-5251-2137 aut Artificial Intelligence-Based Online Control Scheme for the Regulations of Interconnected Thermal Power Systems 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In today’s constantly advancing economy, fast and efficient load frequency control (LFC) schemes are imperative for stable power systems operation, whether conventional or new. Despite recent advances in this domain, professional engineers, to this day, face significant obstacles in dealing with parametric uncertainty and the rejection of external disturbances. To ensure high-quality, resilient, and consistent electrical power, the developed controllers must operate efficiently, i.e., counteract frequency and tie-line power deviations. In light of this, the current study presents a novel strategy employing several machine learning algorithms to remedy the LFC dilemma, estimating the optimal controller gain values in a real-time environment. A comprehensive case study incorporating random load perturbations exemplified the trained controllers’ accuracy, robustness, and adaptability. The proposed online controllers, integrated into emerging power systems with several adjoining regions, renewable sources, and nonlinearities, outperform benchmark procedures. A rank-based statistical analysis implementing the Friedman test reveals Random Forest to be the most effective of the studied algorithms in terms of providing the lowest integral-criteria error values while preserving the minimal peak overshoot, undershoot, and settling duration. This paper demonstrates the significant potential of the suggested framework in addressing the LFC predicament in relevant sectors. Load frequency control (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Interconnected power systems (dpeaa)DE-He213 Random load perturbations (dpeaa)DE-He213 Nonlinearities (dpeaa)DE-He213 Online control (dpeaa)DE-He213 Muhaimin, Sheikh Samit aut Shahi, Md. Nazmush Shakib aut Ahmed, Ashik aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2023), 11 vom: 24. Juni, Seite 15153-15176 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2023 number:11 day:24 month:06 pages:15153-15176 https://dx.doi.org/10.1007/s13369-023-07995-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2023 11 24 06 15153-15176 |
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Orka, Nabil Anan |
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artificial intelligence-based online control scheme for the regulations of interconnected thermal power systems |
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Artificial Intelligence-Based Online Control Scheme for the Regulations of Interconnected Thermal Power Systems |
| abstract |
Abstract In today’s constantly advancing economy, fast and efficient load frequency control (LFC) schemes are imperative for stable power systems operation, whether conventional or new. Despite recent advances in this domain, professional engineers, to this day, face significant obstacles in dealing with parametric uncertainty and the rejection of external disturbances. To ensure high-quality, resilient, and consistent electrical power, the developed controllers must operate efficiently, i.e., counteract frequency and tie-line power deviations. In light of this, the current study presents a novel strategy employing several machine learning algorithms to remedy the LFC dilemma, estimating the optimal controller gain values in a real-time environment. A comprehensive case study incorporating random load perturbations exemplified the trained controllers’ accuracy, robustness, and adaptability. The proposed online controllers, integrated into emerging power systems with several adjoining regions, renewable sources, and nonlinearities, outperform benchmark procedures. A rank-based statistical analysis implementing the Friedman test reveals Random Forest to be the most effective of the studied algorithms in terms of providing the lowest integral-criteria error values while preserving the minimal peak overshoot, undershoot, and settling duration. This paper demonstrates the significant potential of the suggested framework in addressing the LFC predicament in relevant sectors. © King Fahd University of Petroleum & Minerals 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract In today’s constantly advancing economy, fast and efficient load frequency control (LFC) schemes are imperative for stable power systems operation, whether conventional or new. Despite recent advances in this domain, professional engineers, to this day, face significant obstacles in dealing with parametric uncertainty and the rejection of external disturbances. To ensure high-quality, resilient, and consistent electrical power, the developed controllers must operate efficiently, i.e., counteract frequency and tie-line power deviations. In light of this, the current study presents a novel strategy employing several machine learning algorithms to remedy the LFC dilemma, estimating the optimal controller gain values in a real-time environment. A comprehensive case study incorporating random load perturbations exemplified the trained controllers’ accuracy, robustness, and adaptability. The proposed online controllers, integrated into emerging power systems with several adjoining regions, renewable sources, and nonlinearities, outperform benchmark procedures. A rank-based statistical analysis implementing the Friedman test reveals Random Forest to be the most effective of the studied algorithms in terms of providing the lowest integral-criteria error values while preserving the minimal peak overshoot, undershoot, and settling duration. This paper demonstrates the significant potential of the suggested framework in addressing the LFC predicament in relevant sectors. © King Fahd University of Petroleum & Minerals 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract In today’s constantly advancing economy, fast and efficient load frequency control (LFC) schemes are imperative for stable power systems operation, whether conventional or new. Despite recent advances in this domain, professional engineers, to this day, face significant obstacles in dealing with parametric uncertainty and the rejection of external disturbances. To ensure high-quality, resilient, and consistent electrical power, the developed controllers must operate efficiently, i.e., counteract frequency and tie-line power deviations. In light of this, the current study presents a novel strategy employing several machine learning algorithms to remedy the LFC dilemma, estimating the optimal controller gain values in a real-time environment. A comprehensive case study incorporating random load perturbations exemplified the trained controllers’ accuracy, robustness, and adaptability. The proposed online controllers, integrated into emerging power systems with several adjoining regions, renewable sources, and nonlinearities, outperform benchmark procedures. A rank-based statistical analysis implementing the Friedman test reveals Random Forest to be the most effective of the studied algorithms in terms of providing the lowest integral-criteria error values while preserving the minimal peak overshoot, undershoot, and settling duration. This paper demonstrates the significant potential of the suggested framework in addressing the LFC predicament in relevant sectors. © King Fahd University of Petroleum & Minerals 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Artificial Intelligence-Based Online Control Scheme for the Regulations of Interconnected Thermal Power Systems |
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Muhaimin, Sheikh Samit Shahi, Md. Nazmush Shakib Ahmed, Ashik |
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10.1007/s13369-023-07995-3 |
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| score |
7.4018974 |