Optimization of Energy Efficiency in Photovoltaic Water Pumping Systems Using Neural Networks.
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| Title: | Optimization of Energy Efficiency in Photovoltaic Water Pumping Systems Using Neural Networks. |
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| Authors: | Ghoudelbourk, Sihem1 (AUTHOR) sihem.ghoud-lbourk@univ-annaba.dz, Benbouhenni, Habib2 (AUTHOR), Elbarbary, Z. M. S.3 (AUTHOR), Benidir, Mohamed4 (AUTHOR) |
| Source: | Energy Science & Engineering. Apr2026, Vol. 14 Issue 4, p2153-2181. 29p. |
| Subject Terms: | *Maximum power point trackers, *Artificial neural networks, *Solar pumps, *Induction motors, *Energy consumption, *Torque control |
| Abstract: | In light of current global issues, including rising energy prices, environmental concerns, and the requirement for resilient water systems, photovoltaic (PV) water pumping systems have emerged as a promising solution, particularly for off‐grid communities. The goal of this work is to use an optimal control strategy to achieve independence and efficiency. The chosen PV water pumping system is based on an induction motor equipped with direct torque command. To extract the maximum energy from the PV panel, a maximum power point tracking (MPPT) based on a neural network algorithm is proposed. The results obtained will be compared with those from the same system using conventional Incremental Conductance (IC) and Perturbation and Observation (P&O) methods. On the basis of the results obtained, we can state that the neural MPPT technique is the most effective, achieving an efficiency of 98% and providing the fastest tracking, superior power stability, and minimal harmonics. With a total harmonic distortion of 3.41% and a larger water volume, it is the most suitable option for a PV water pumping system. The MPPT–IC technique is a promising option, offering an efficiency of 93% with fast responsiveness, albeit resulting in larger power fluctuations. However, the MPPT–P&O strategy is the least effective due to its slow response and high harmonic distortion rate of 10.96%, with an efficiency of 87%. These factors make it the least suitable for optimal performance. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 193258059 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimization of Energy Efficiency in Photovoltaic Water Pumping Systems Using Neural Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ghoudelbourk%2C+Sihem%22">Ghoudelbourk, Sihem</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sihem.ghoud-lbourk@univ-annaba.dz</i><br /><searchLink fieldCode="AR" term="%22Benbouhenni%2C+Habib%22">Benbouhenni, Habib</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Elbarbary%2C+Z%2E+M%2E+S%2E%22">Elbarbary, Z. M. S.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Benidir%2C+Mohamed%22">Benidir, Mohamed</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energy+Science+%26+Engineering%22">Energy Science & Engineering</searchLink>. Apr2026, Vol. 14 Issue 4, p2153-2181. 29p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Maximum+power+point+trackers%22">Maximum power point trackers</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Solar+pumps%22">Solar pumps</searchLink><br />*<searchLink fieldCode="DE" term="%22Induction+motors%22">Induction motors</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br />*<searchLink fieldCode="DE" term="%22Torque+control%22">Torque control</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In light of current global issues, including rising energy prices, environmental concerns, and the requirement for resilient water systems, photovoltaic (PV) water pumping systems have emerged as a promising solution, particularly for off‐grid communities. The goal of this work is to use an optimal control strategy to achieve independence and efficiency. The chosen PV water pumping system is based on an induction motor equipped with direct torque command. To extract the maximum energy from the PV panel, a maximum power point tracking (MPPT) based on a neural network algorithm is proposed. The results obtained will be compared with those from the same system using conventional Incremental Conductance (IC) and Perturbation and Observation (P&O) methods. On the basis of the results obtained, we can state that the neural MPPT technique is the most effective, achieving an efficiency of 98% and providing the fastest tracking, superior power stability, and minimal harmonics. With a total harmonic distortion of 3.41% and a larger water volume, it is the most suitable option for a PV water pumping system. The MPPT–IC technique is a promising option, offering an efficiency of 93% with fast responsiveness, albeit resulting in larger power fluctuations. However, the MPPT–P&O strategy is the least effective due to its slow response and high harmonic distortion rate of 10.96%, with an efficiency of 87%. These factors make it the least suitable for optimal performance. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193258059 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/ese3.70477 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 2153 Subjects: – SubjectFull: Maximum power point trackers Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Solar pumps Type: general – SubjectFull: Induction motors Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Torque control Type: general Titles: – TitleFull: Optimization of Energy Efficiency in Photovoltaic Water Pumping Systems Using Neural Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ghoudelbourk, Sihem – PersonEntity: Name: NameFull: Benbouhenni, Habib – PersonEntity: Name: NameFull: Elbarbary, Z. M. S. – PersonEntity: Name: NameFull: Benidir, Mohamed IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20500505 Numbering: – Type: volume Value: 14 – Type: issue Value: 4 Titles: – TitleFull: Energy Science & Engineering Type: main |
| ResultId | 1 |
