Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms.
Saved in:
| Title: | Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms. |
|---|---|
| Authors: | Bavarinos, Kostas1 (AUTHOR) Bavarinos@gmail.com, Dounis, Anastasios2 (AUTHOR) aidounis@uniwa.gr, Kofinas, Panagiotis1,2 (AUTHOR) pkofinas@uniwa.gr |
| Source: | Energies (19961073). 1/15/2021, Vol. 14 Issue 2, p335. 1p. |
| Subject Terms: | *Evolutionary algorithms, *Process optimization, *Maximum power point trackers, *Reinforcement learning, *Photovoltaic power systems, *Open-circuit voltage, *Short-circuit currents, *Genetic algorithms |
| Abstract: | In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both methods exhibit fast achievement of the MPP under varying environmental conditions and are applicable in different PV systems. The only required knowledge of the PV system are the open-circuit voltage, the short-circuit current and the maximum power, all under STC, which are always provided by the manufacturer. Both methods are compared to a Fuzzy Logic Controller and the universality of the proposed methods is highlighted. After the implementation and the validation of proper performance of both methods, two evolutionary optimization algorithms (Big Bang—Big Crunch and Genetic Algorithm) are applied. The results demonstrate that both methods achieve higher energy production and in both methods the time for tracking the MPP is reduced, after the application of both evolutionary algorithms. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: enr DbLabel: Energy & Power Source An: 148300715 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bavarinos%2C+Kostas%22">Bavarinos, Kostas</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Bavarinos@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Dounis%2C+Anastasios%22">Dounis, Anastasios</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> aidounis@uniwa.gr</i><br /><searchLink fieldCode="AR" term="%22Kofinas%2C+Panagiotis%22">Kofinas, Panagiotis</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> pkofinas@uniwa.gr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. 1/15/2021, Vol. 14 Issue 2, p335. 1p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Maximum+power+point+trackers%22">Maximum power point trackers</searchLink><br />*<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Photovoltaic+power+systems%22">Photovoltaic power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Open-circuit+voltage%22">Open-circuit voltage</searchLink><br />*<searchLink fieldCode="DE" term="%22Short-circuit+currents%22">Short-circuit currents</searchLink><br />*<searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both methods exhibit fast achievement of the MPP under varying environmental conditions and are applicable in different PV systems. The only required knowledge of the PV system are the open-circuit voltage, the short-circuit current and the maximum power, all under STC, which are always provided by the manufacturer. Both methods are compared to a Fuzzy Logic Controller and the universality of the proposed methods is highlighted. After the implementation and the validation of proper performance of both methods, two evolutionary optimization algorithms (Big Bang—Big Crunch and Genetic Algorithm) are applied. The results demonstrate that both methods achieve higher energy production and in both methods the time for tracking the MPP is reduced, after the application of both evolutionary algorithms. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=148300715 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en14020335 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: 335 Subjects: – SubjectFull: Evolutionary algorithms Type: general – SubjectFull: Process optimization Type: general – SubjectFull: Maximum power point trackers Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Photovoltaic power systems Type: general – SubjectFull: Open-circuit voltage Type: general – SubjectFull: Short-circuit currents Type: general – SubjectFull: Genetic algorithms Type: general Titles: – TitleFull: Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bavarinos, Kostas – PersonEntity: Name: NameFull: Dounis, Anastasios – PersonEntity: Name: NameFull: Kofinas, Panagiotis IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 01 Text: 1/15/2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 14 – Type: issue Value: 2 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
