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Modified Invasive Weed Optimization for the Control of Photovoltaic Powered Induction Motor Drives in Water Pumping Systems

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Iranian Journal of Science and Technology, Transactions of Electrical Engineering Aims and scope Submit manuscript

Abstract

This paper proposes an effective energy management system for a battery-less photovoltaic fed induction motor drive for a water pumping system that aids under rapid changes in solar irradiance during partial shading conditions. A sensorless speed controller with a sliding mode controller is implemented in this research. A modified invasive weed optimization (MIWO) algorithm is integrated into perturb and observe (P&O) for the efficient working of the systems under various partial partialities shading conditions. The proposed controller uses the inverter as a maximum power point tracking (MPPT) converter for the photovoltaic system, eliminating the need for a separate converter in tracking the MPP. The obtained simulation and experimental results from the proposed hybrid P&O-MIWO are compared with the performance of P&O integration with genetic algorithm, particle swarm optimization, and grey wolf optimization for assessing the suitability of the proposed algorithm under different PSCs. Moreover, the extensive results obtained for the proposed system are analyzed under various case studies and validated by implementing them on the OPAL-RT platform. Quantitatively, the response of the proposed algorithm was recorded in terms of the tracking time, and the obtained maximum power is compared with other implemented algorithms. For the three partial shading conditions investigated in this study, the MOPO algorithm has achieved an enhanced tracking efficiency of 99.7%, 99.98%, and 99.98% with a least tracking time of 0.306 s, 0.15 s, and 0.256 s, respectively. The results obtained have clearly demonstrated the superior tracking performance of the proposed MIWO-based P&O approach compared to GA, PSO, and GWO optimized P&O for its tracking efficiency and convergence time. Additionally, it is observed that the tracking time of the proposed algorithm is about one-third of the other techniques, while the maximum power points of all implemented algorithms are comparable.

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Abbreviations

GA:

Genetic algorithm

GM:

Global maximum

GWO:

Grey wolf optimization

H:

Total dynamic head (Ft)

HIL:

Hardware-in-loop

IM:

Induction motor

LM:

Local maximum

MIWO:

Modified invasive weed optimization

MPPT:

Maximum power point tracking

P&O:

Perturb and observe

PSC:

Partial shading condition

PSO:

Particle swarm optimization

PV:

Photovoltaic

Q:

Quantity of water discharge (gal/min)

RTS:

Real time simulator

SMC:

Sliding mode controller

TL:

Load torque on motor (Nm)

TS:

Takagi-Sugeno

Vmpp:

Voltage at maximum power point

Whp:

Horsepower (hp)

WPS:

Water pumping system

ω :

Speed of the motor (rps)

ω t :

Base speed of the motor (rps)

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Authors and Affiliations

  1. CPGC Pvt. Ltd, Visakhapatnam, India

    Siva Ganesh Malla

  2. Department of Electrical and Electronics Engineering, AMET University, Tamil Nadu, India

    Priyanka Malla

  3. Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India

    M. Karthik

  4. New Horizon College of Engineering, Bangalore, Karnataka, India

    D. Satish Kumar

  5. Department of Electrical Technology, Faculty of Technology and Education, Helwan University, Cairo, Egypt

    Hilmy Awad

Authors
  1. Siva Ganesh Malla
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  2. Priyanka Malla
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  3. M. Karthik
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  4. D. Satish Kumar
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Correspondence to M. Karthik.

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Malla, S.G., Malla, P., Karthik, M. et al. Modified Invasive Weed Optimization for the Control of Photovoltaic Powered Induction Motor Drives in Water Pumping Systems. Iran J Sci Technol Trans Electr Eng 47, 925–938 (2023). https://doi.org/10.1007/s40998-023-00589-7

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