Energy Optimization of Motor-Driven Systems Using Variable Frequency Control, Soft Starters, and Machine Learning Forecasting.
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| Title: | Energy Optimization of Motor-Driven Systems Using Variable Frequency Control, Soft Starters, and Machine Learning Forecasting. |
|---|---|
| Authors: | Ahmed, Hashnayne1 (AUTHOR), Cárdenas-Lailhacar, Cristián1 (AUTHOR), Sherif, S. A.1 (AUTHOR) sasherif@ufl.edu |
| Source: | Energies (19961073). Oct2025, Vol. 18 Issue 19, p5135. 17p. |
| Subjects: | Energy management, Variable speed drives, Machine learning, Stochastic programming, Electric power consumption, Electric drives |
| Abstract: | This paper presents a unified modeling framework for quantifying power and energy consumption in motor-driven systems operating under variable frequency control and soft starter conditions. By formulating normalized expressions for voltage, current, and power factor as functions of motor speed, the model enables accurate estimation of instantaneous and cumulative energy use using only measurable electrical quantities. The effect of soft starter operation during startup is incorporated through ramp-based profiles, while variable frequency control is modeled through dynamic speed modulation. Analytical results show that variable speed control can achieve energy savings of up to 36.1% for sinusoidal speed profiles and up to 42.9% when combined with soft starter operation, with the soft starter alone contributing a consistent 8.6% reduction independent of the power factor. To support energy optimization under uncertain demand scenarios, a two-stage stochastic optimization framework is developed for motor sizing and control assignment, and four physics-guided machine learning models—MLP, LSTM, GRU, and XGBoost—are benchmarked to forecast normalized energy ratios from key electrical parameters, enabling rapid and interpretable predictions. The proposed framework provides a scalable, interpretable, and practical tool for monitoring, diagnostics, and smart energy management of industrial motor-driven systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 188680750 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Energy Optimization of Motor-Driven Systems Using Variable Frequency Control, Soft Starters, and Machine Learning Forecasting. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ahmed%2C+Hashnayne%22">Ahmed, Hashnayne</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cárdenas-Lailhacar%2C+Cristián%22">Cárdenas-Lailhacar, Cristián</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sherif%2C+S%2E+A%2E%22">Sherif, S. A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sasherif@ufl.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Oct2025, Vol. 18 Issue 19, p5135. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Energy+management%22">Energy management</searchLink><br /><searchLink fieldCode="DE" term="%22Variable+speed+drives%22">Variable speed drives</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+programming%22">Stochastic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+consumption%22">Electric power consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+drives%22">Electric drives</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents a unified modeling framework for quantifying power and energy consumption in motor-driven systems operating under variable frequency control and soft starter conditions. By formulating normalized expressions for voltage, current, and power factor as functions of motor speed, the model enables accurate estimation of instantaneous and cumulative energy use using only measurable electrical quantities. The effect of soft starter operation during startup is incorporated through ramp-based profiles, while variable frequency control is modeled through dynamic speed modulation. Analytical results show that variable speed control can achieve energy savings of up to 36.1% for sinusoidal speed profiles and up to 42.9% when combined with soft starter operation, with the soft starter alone contributing a consistent 8.6% reduction independent of the power factor. To support energy optimization under uncertain demand scenarios, a two-stage stochastic optimization framework is developed for motor sizing and control assignment, and four physics-guided machine learning models—MLP, LSTM, GRU, and XGBoost—are benchmarked to forecast normalized energy ratios from key electrical parameters, enabling rapid and interpretable predictions. The proposed framework provides a scalable, interpretable, and practical tool for monitoring, diagnostics, and smart energy management of industrial motor-driven systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=188680750 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en18195135 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 5135 Subjects: – SubjectFull: Energy management Type: general – SubjectFull: Variable speed drives Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Stochastic programming Type: general – SubjectFull: Electric power consumption Type: general – SubjectFull: Electric drives Type: general Titles: – TitleFull: Energy Optimization of Motor-Driven Systems Using Variable Frequency Control, Soft Starters, and Machine Learning Forecasting. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ahmed, Hashnayne – PersonEntity: Name: NameFull: Cárdenas-Lailhacar, Cristián – PersonEntity: Name: NameFull: Sherif, S. A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 18 – Type: issue Value: 19 Titles: – TitleFull: Energies (19961073) Type: main |
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