Academic Journal
A Systemic Approach for Identifying Parameter Errors in Simulation Models of Power Systems Using Machine Learning.
| Τίτλος: | A Systemic Approach for Identifying Parameter Errors in Simulation Models of Power Systems Using Machine Learning. |
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| Συγγραφείς: | Nascimento, Leonardo L.1 (AUTHOR) opleonash@gmail.com, Moreto, Miguel1 (AUTHOR) miguel.moreto@ufsc.br |
| Πηγή: | Journal of Control, Automation & Electrical Systems. Oct2025, Vol. 36 Issue 5, p970-980. 11p. |
| Θεματικοί όροι: | Electric power systems, Machine learning, Electric transients, Electronic data processing, Errors-in-variables models, Simulation methods & models, Model validation, Dynamic simulation |
| Περίληψη: | The quality of the models used in dynamic simulation has fundamental importance for studies on the operation and planning of electric power systems (EPS). A typical problem is the identification of incorrect parameters. This work proposes a methodology to identify errors in model parameters, using a systemic validation approach. It uses machine learning (ML) as its main tool. Although the choice of systemic validation approach is due to its comprehensive strategy due to its ability to evaluate models in a single process, the choice of using ML is appropriate because it emulates the same analyses performed by a human expert with the advantage of processing, comparing and classifying a much larger amount of data. From the generation of cases using the electromechanical transients analysis software (ETAS), two databases were created for later training of the ML: one containing errors and the other without any type of parameter errors. With appropriate data processing steps, such as signal filtering and dimensionality reduction, experiments on a fictitious 68-bus system demonstrate that the application of the new technique allows not only to check for errors in physical model parameters with great accuracy, but also to identify which of them are incorrect and the region with problems. In this way, the methodology allows to complement the techniques present in the literature, as an initial step in selecting the equipment or subsystems that require attention, especially for large-scale EPS. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Supplemental Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s40313-025-01206-0 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edo DbLabel: Supplemental Index An: 187972783 RelevancyScore: 1023 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1023.09039306641 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Systemic Approach for Identifying Parameter Errors in Simulation Models of Power Systems Using Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nascimento%2C+Leonardo+L%2E%22">Nascimento, Leonardo L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> opleonash@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Moreto%2C+Miguel%22">Moreto, Miguel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> miguel.moreto@ufsc.br</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Control%2C+Automation+%26+Electrical+Systems%22">Journal of Control, Automation & Electrical Systems</searchLink>. Oct2025, Vol. 36 Issue 5, p970-980. 11p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+transients%22">Electric transients</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Errors-in-variables+models%22">Errors-in-variables models</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+simulation%22">Dynamic simulation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The quality of the models used in dynamic simulation has fundamental importance for studies on the operation and planning of electric power systems (EPS). A typical problem is the identification of incorrect parameters. This work proposes a methodology to identify errors in model parameters, using a systemic validation approach. It uses machine learning (ML) as its main tool. Although the choice of systemic validation approach is due to its comprehensive strategy due to its ability to evaluate models in a single process, the choice of using ML is appropriate because it emulates the same analyses performed by a human expert with the advantage of processing, comparing and classifying a much larger amount of data. From the generation of cases using the electromechanical transients analysis software (ETAS), two databases were created for later training of the ML: one containing errors and the other without any type of parameter errors. With appropriate data processing steps, such as signal filtering and dimensionality reduction, experiments on a fictitious 68-bus system demonstrate that the application of the new technique allows not only to check for errors in physical model parameters with great accuracy, but also to identify which of them are incorrect and the region with problems. In this way, the methodology allows to complement the techniques present in the literature, as an initial step in selecting the equipment or subsystems that require attention, especially for large-scale EPS. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edo&AN=187972783 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s40313-025-01206-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 970 Subjects: – SubjectFull: Electric power systems Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Electric transients Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Errors-in-variables models Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Model validation Type: general – SubjectFull: Dynamic simulation Type: general Titles: – TitleFull: A Systemic Approach for Identifying Parameter Errors in Simulation Models of Power Systems Using Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nascimento, Leonardo L. – PersonEntity: Name: NameFull: Moreto, Miguel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 21953880 Numbering: – Type: volume Value: 36 – Type: issue Value: 5 Titles: – TitleFull: Journal of Control, Automation & Electrical Systems Type: main |
| ResultId | 1 |