Power Quality Enhancement in Grid‐Connected Photovoltaic Systems Using Hybrid Harbor Seal Whiskers Optimization and Interpretable Generalized Additive Neural Networks.
Saved in:
| Title: | Power Quality Enhancement in Grid‐Connected Photovoltaic Systems Using Hybrid Harbor Seal Whiskers Optimization and Interpretable Generalized Additive Neural Networks. |
|---|---|
| Authors: | Hariprabhu, M.1 (AUTHOR), Kumar, C.2 (AUTHOR), Raj, T. Dharma3 (AUTHOR), Barua, Sourav4 (AUTHOR) barua@eee.green.edu.bd, Ponce-Silva, Mario (AUTHOR) mario.ps@cenidet.tecnm.mx |
| Source: | International Transactions on Electrical Energy Systems. 4/23/2026, Vol. 2026, p1-22. 22p. |
| Subject Terms: | *Photovoltaic power systems, *Artificial neural networks, *Voltage control, *Harmonic distortion (Physics), *Metaheuristic algorithms, *Renewable energy sources, *Power supply quality, *Feedback control systems |
| Abstract: | The integration of solar energy into modern power grids supports sustainability and energy efficiency but also introduces power quality (PQ) challenges such as harmonic distortion, voltage sag, swell, and fluctuations. In order to reduce PQ problems in renewable energy systems (RESs), this research proposes a novel hybrid control strategy that combines the interpretable generalized additive neural networks (IGANNs) with the harbor seal whiskers optimization algorithm (HSWOA). The unified PQ conditioner (UPQC), enhanced with a tilted integral fractional derivative with filter plus fractional derivative (TIFDNFD) controller, is employed for compensation. IGANN predicts the gain parameters for the TIFDNFD controller, while HSWOA efficiently tunes these parameters through an adaptive optimization process. The proposed HSWOA‐IGANN technique is implemented in the MATLAB platform, and its performance is compared to various existing methods. The proposed method achieved a minimum total harmonic distortion (THD) of 0.42% and ensured stable load voltage at ±220 V during grid disturbances with deviations limited to 0.09–0.11 V, outperforming existing methods. The proposed method ensures efficiency, faster convergence, and accurate control of PQ, enhancing grid stability and reliability. [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: 193258280 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Power Quality Enhancement in Grid‐Connected Photovoltaic Systems Using Hybrid Harbor Seal Whiskers Optimization and Interpretable Generalized Additive Neural Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hariprabhu%2C+M%2E%22">Hariprabhu, M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kumar%2C+C%2E%22">Kumar, C.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Raj%2C+T%2E+Dharma%22">Raj, T. Dharma</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Barua%2C+Sourav%22">Barua, Sourav</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> barua@eee.green.edu.bd</i><br /><searchLink fieldCode="AR" term="%22Ponce-Silva%2C+Mario%22">Ponce-Silva, Mario</searchLink> (AUTHOR)<i> mario.ps@cenidet.tecnm.mx</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Transactions+on+Electrical+Energy+Systems%22">International Transactions on Electrical Energy Systems</searchLink>. 4/23/2026, Vol. 2026, p1-22. 22p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Photovoltaic+power+systems%22">Photovoltaic power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Voltage+control%22">Voltage control</searchLink><br />*<searchLink fieldCode="DE" term="%22Harmonic+distortion+%28Physics%29%22">Harmonic distortion (Physics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br />*<searchLink fieldCode="DE" term="%22Power+supply+quality%22">Power supply quality</searchLink><br />*<searchLink fieldCode="DE" term="%22Feedback+control+systems%22">Feedback control systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The integration of solar energy into modern power grids supports sustainability and energy efficiency but also introduces power quality (PQ) challenges such as harmonic distortion, voltage sag, swell, and fluctuations. In order to reduce PQ problems in renewable energy systems (RESs), this research proposes a novel hybrid control strategy that combines the interpretable generalized additive neural networks (IGANNs) with the harbor seal whiskers optimization algorithm (HSWOA). The unified PQ conditioner (UPQC), enhanced with a tilted integral fractional derivative with filter plus fractional derivative (TIFDNFD) controller, is employed for compensation. IGANN predicts the gain parameters for the TIFDNFD controller, while HSWOA efficiently tunes these parameters through an adaptive optimization process. The proposed HSWOA‐IGANN technique is implemented in the MATLAB platform, and its performance is compared to various existing methods. The proposed method achieved a minimum total harmonic distortion (THD) of 0.42% and ensured stable load voltage at ±220 V during grid disturbances with deviations limited to 0.09–0.11 V, outperforming existing methods. The proposed method ensures efficiency, faster convergence, and accurate control of PQ, enhancing grid stability and reliability. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193258280 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/etep/4763394 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Photovoltaic power systems Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Voltage control Type: general – SubjectFull: Harmonic distortion (Physics) Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Renewable energy sources Type: general – SubjectFull: Power supply quality Type: general – SubjectFull: Feedback control systems Type: general Titles: – TitleFull: Power Quality Enhancement in Grid‐Connected Photovoltaic Systems Using Hybrid Harbor Seal Whiskers Optimization and Interpretable Generalized Additive Neural Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hariprabhu, M. – PersonEntity: Name: NameFull: Kumar, C. – PersonEntity: Name: NameFull: Raj, T. Dharma – PersonEntity: Name: NameFull: Barua, Sourav – PersonEntity: Name: NameFull: Ponce-Silva, Mario IsPartOfRelationships: – BibEntity: Dates: – D: 23 M: 04 Text: 4/23/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20507038 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: International Transactions on Electrical Energy Systems Type: main |
| ResultId | 1 |
