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AI Energy Optimal Strategy on Variable Speed Drives for Multi-Parallel Aqua Pumping System

Author

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  • Manickavel Baranidharan

    (School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Rassiah Raja Singh

    (Department of Energy and Power Electronics, Vellore Institute of Technology, Vellore 632014, India)

Abstract

In the industrial world, parallel pump systems are frequently employed. Due to various reasons, the pumps are frequently operated outside their intended parameters, which reduces their efficiency and performance. To operate the pump system with optimum efficiency, the pumps and their speed selection are mandatory. This research presents an optimum switching technique for variable speed pumping stations with multi-parallel pump combinations to enhance energy savings. The proposed optimal control system is designed in such a way as to decrease overall losses in the pump system. The effectiveness of the proposed method is investigated on a real scale of a multi-parallel pump drive system in a Matlab Simulink environment, and experimental validation is performed in a laboratory prototype. The suggested approach enhances power savings and shall be adapted for various pumping applications.

Suggested Citation

  • Manickavel Baranidharan & Rassiah Raja Singh, 2022. "AI Energy Optimal Strategy on Variable Speed Drives for Multi-Parallel Aqua Pumping System," Energies, MDPI, vol. 15(12), pages 1-29, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4343-:d:838442
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    References listed on IDEAS

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    Cited by:

    1. Plamena Dinolova & Vyara Ruseva & Ognyan Dinolov, 2023. "Energy Efficiency of Induction Motor Drives: State of the Art, Analysis and Recommendations," Energies, MDPI, vol. 16(20), pages 1-26, October.
    2. Xuecong Qin & Yin Luo & Shengyuan Chen & Yunfei Chen & Yuejiang Han, 2022. "Investigation of Energy-Saving Strategy for Parallel Variable Frequency Pump System Based on Improved Differential Evolution Algorithm," Energies, MDPI, vol. 15(15), pages 1-14, July.

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