Evidence map›Paper›PMID 39894812›Full record

ArticleScientific reports2025

Improving photovoltaic water pumping system performance with ANN-based direct torque control using real-time simulation.

Ikram Saady, Btissam Majout, Ismail El Kafazi, Mohammed Karim, Badre Bossoufi, Najib El Ouanjli, Said Mahfoud, Ahmed Althobaiti, Thamer A H Alghamdi, Mohammed Alenezi

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ikram SaadyLaboratory of Engineering Modelling and Systems Analysis, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Btissam MajoutLaboratory of Engineering Modelling and Systems Analysis, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Ismail El KafaziSmartiLAB EMSI-Rabat, Honoris United Universities, Rabat, Morocco.
Mohammed KarimLaboratory of Engineering Modelling and Systems Analysis, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Badre BossoufiLaboratory of Engineering Modelling and Systems Analysis, Sidi Mohamed Ben Abdellah University, Fez, Morocco. badre.bossoufi@usmba.ac.ma.
Najib El OuanjliHigher School of Technology, Moulay Ismail University, Mekness, Morocco.
Said MahfoudIndustrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Ahmed AlthobaitiDepartment of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif City, 21974, Saudi Arabia.
Thamer A H AlghamdiWolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK.
Mohammed AleneziWolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK. AleneziM1@cardiff.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Photovoltaic Water Pumping Systems (PVWPS) have become increasingly important as a renewable energy solution in rural areas, providing energy independence, cost savings, and environmental friendliness. This system has two main controllers. The first controller is employed to maximize power extraction from the PV array by controlling the duty ratio of the DC-DC boost converter. The second controller is responsible for regulating the operation of the induction motor through the switching pulses of the Voltage Source Inverter (VSI). These two controllers play an essential role in the system, which increases efficiency and performance. Therefore, the innovative aspect of this work consists of introducing Artificial Neural Networks (ANNs) based on each PVWPS controller. On the one hand, ANN-based MPPT is implemented to ensure optimal performance of the PV array under varying irradiation levels. On the other hand, to overcome the defects and problems caused by Direct Torque Control (DTC), such as flux and torque ripples, high switching frequency, and challenges at low speeds, an ANN-based DTC is proposed in which each of the hysteresis comparators, switching table, and speed controller in the DTC are replaced by ANN controllers. The PVWPS based on the proposed controls is thoroughly modeled and simulated using MATLAB/Simulink software and validated using dSPACE DS1104 Board. The results demonstrate significant improvements, including a 75.51% reduction in flux ripples, a 77.5% reduction in torque ripples, a 44.79% improvement in response time, and an increase in the water quantity. Furthermore, the Real-Time simulation and visualization obtained are consistent with the simulation outcomes.

Indexed as

Artificial neural networksDirect torque controldSPACE DS1104Induction motorPhotovoltaic water pumping system

Identifiers

PMID39894812
PMCPMC11788436

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.