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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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Improving photovoltaic water pumping system performance with ANN-based direct torque control using real-time

Ikram Saady1,2, Btissam Majout1, Ismail El Kafazi2

  • 1Laboratory of Engineering Modelling and Systems Analysis, Sidi Mohamed Ben Abdellah University, Fez, Morocco.

Scientific Reports
|February 2, 2025
PubMed
Summary
This summary is machine-generated.

Artificial Neural Networks enhance Photovoltaic Water Pumping Systems (PVWPS) by improving Maximum Power Point Tracking (MPPT) and Direct Torque Control (DTC). This leads to significant reductions in ripples and faster response times for renewable water pumping.

Keywords:
Artificial neural networksDirect torque controlInduction motorPhotovoltaic water pumping systemdSPACE DS1104

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Area of Science:

  • Renewable Energy Systems
  • Control Engineering
  • Artificial Intelligence

Background:

  • Photovoltaic Water Pumping Systems (PVWPS) are crucial for rural renewable energy, offering independence and cost savings.
  • Traditional PVWPS controllers, including MPPT and Direct Torque Control (DTC), face challenges like power extraction inefficiencies and motor control defects.
  • Existing DTC methods suffer from flux/torque ripples and poor low-speed performance.

Purpose of the Study:

  • To introduce Artificial Neural Networks (ANNs) to enhance PVWPS controllers.
  • To improve the efficiency and performance of PVWPS through advanced control strategies.
  • To address limitations in conventional MPPT and DTC for PV water pumping applications.

Main Methods:

  • Implementation of an ANN-based Maximum Power Point Tracking (MPPT) controller for optimal PV array power extraction.
  • Development of an ANN-based Direct Torque Control (DTC) strategy, replacing conventional components like hysteresis comparators and switching tables.
  • Modeling and simulation of the PVWPS using MATLAB/Simulink, with validation through dSPACE DS1104 Board real-time simulation.

Main Results:

  • Significant reduction in flux ripples (75.51%) and torque ripples (77.5%) in the induction motor.
  • Improvement in system response time by 44.79% and an increase in water output.
  • Real-time simulation results validated the effectiveness of the proposed ANN-based control strategies.

Conclusions:

  • ANN-based controllers offer a superior solution for PVWPS, overcoming limitations of traditional methods.
  • The proposed system demonstrates enhanced efficiency, stability, and performance in photovoltaic water pumping.
  • This advancement contributes to more reliable and efficient renewable energy solutions for water supply.