Grey Wolf Optimization Inspired Maximum Power Extraction from SPV System for Water Pumping Application

Kumar, Astitva; Bilal, Mohd; Rizwan, Mohammad; Nangia, Uma

doi:10.1109/iconat53423.2022.9726028

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…A. Kumar et al, (2022) present an SPV-based water pumping system with a GWO MPPT achieving precise power tracking and minimal computational burden. In GWO, the grey wolves hunt in a pack.…”

Section: Grey Wolf Optimization Algorithmmentioning

confidence: 99%

Grey wolf optimization and incremental conductance based hybrid MPPT technique for solar powered induction motor driven water pump

Shetty,

Sabhahit

2023

Int. J. Renew. Energy Dev.

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The use of Solar Powered Water Pumps (SPWP) has emerged as a significant advancement in irrigation systems, offering a viable alternative to electricity and diesel-based pumping methods. The appeal of SPWPs to farmers lies in their low maintenance costs and the incentives provided by government agencies to support sustainable and cost-effective agricultural practices. However, a critical challenge faced by solar photovoltaic (PV) systems is their susceptibility to power loss under partial shading conditions, which can persist for extended periods, ultimately reducing system efficiency. To address this issue, this paper proposes the integration of Maximum Power Point Tracking (MPPT) controllers with efficient algorithms designed to identify the peak power during shading events. In this study, a hybrid approach combining Grey Wolf Optimization (GWO) and Incremental Conductance (INC) is employed to maximize the power output of SPWPs driven by an induction motor under partial shading conditions. In order to achieve faster convergence to the global peak, GWO handles the first stages of MPPT and then INC algorithm is employed at the end of the MPPT process. This method reduces the computations of GWO and streamlines the search space. The paper evaluates the performance of the induction motor in terms of speed settling time and torque ripple. To validate the effectiveness of the GWO-INC hybrid approach, simulations are conducted using the MATLAB Simulink platform. The outcomes are then compared with results obtained from various well-known approaches, including Particle Swarm Optimization – Perturb and Observe (PSO-PO), PSO-INC, and GWO-PO, illustrating the superiority of the GWO-INC hybrid approach in enhancing the efficiency and performance of solar water pumps during shading. The GWO-INC excels with 99.6% accuracy in uniform shading and 99.8% in partial shading. It achieves convergence in a mere 0.55 seconds under uniform shading conditions and only 0.42 seconds when partial shading is present. Moreover, it significantly reduces torque oscillations, with a torque ripple of 8.26% in cases of uniform shading and 10.56% in partial shading.

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“…A. Kumar et al, (2022) present an SPV-based water pumping system with a GWO MPPT achieving precise power tracking and minimal computational burden. In GWO, the grey wolves hunt in a pack.…”

Section: Grey Wolf Optimization Algorithmmentioning

confidence: 99%

Grey wolf optimization and incremental conductance based hybrid MPPT technique for solar powered induction motor driven water pump

Shetty,

Sabhahit

2023

Int. J. Renew. Energy Dev.

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“…In these studies, one or more of the current, voltage and speed parameters of the motor side are measured. At the same time, one or more of the current, voltage and radiation values are measured on the PV side [36][37][38]. Different motor types and MPPT algorithms have been used in PV pump systems operating under PSC.…”

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confidence: 99%

A Sensorless MPPT Approach For PV Pumb System Used BLDC Motor

Atagün,

Çelikel

2024

Balkan Journal of Electrical and Computer Engineering

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PV irrigation systems have begun to be used intensively today, as energy needs increase. In Partially Shaded Conditions (PSC), the efficiency of the PV system decreases significantly, and traditional Maximum Power Point Tracking (MPPT) algorithms become insufficient. On the other hand, traditional MPPT algorithms require sensors to measure the current and voltage of the PV system. In this study, a sensorless hybrid MPPT algorithm is proposed to reduce system costs and enable operation without the need for PV system data. A simulation study was conducted in the MATLAB/Simulink environment to examine the PV system. The proposed algorithm has been tested under four different PSC scenarios. PV system power, motor speed, and currents were examined under each condition. The high maximum power tracking performance of the proposed algorithm is demonstrated through simulation results. In the steady state, the lowest MPPT efficiency was 95.66%, whereas the highest MPPT efficiency was 99.9%. The MPPT algorithm completed in less than 2 seconds, with the first stage taking 1.3 seconds to reach most of the maximum PV system power. The second stage of the MPPT algorithm was used to achieve maximum power in a narrower area.

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An Advanced and Robust Approach to Maximize Solar Photovoltaic Power Production

et al. 2022

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The stochastic and erratic behavior of solar photovoltaic (SPV) is a challenge, especially due to changing meteorological conditions. During a partially irradiated SPV system, the performance of traditional maximum power point tracking (MPPT) controllers is unsatisfactory because of multiple peaks in the Power-Voltage curve. This work is an attempt to understand the performance uncertainties of the SPV system under different shading conditions and its mitigation. Here, a novel hybrid metaheuristic algorithm is proposed for the effective and efficient tracking of power. The algorithm is inspired by the movement of grey wolves and the swarming action of birds, and is thus known as the hybrid grey wolf optimizer (HGWO). The study focuses on the transient and steady-state performance of the proposed controller during different conditions. A comparative analysis of the proposed technique with incremental conductance and a particle swarm optimizer for different configurations is presented. Thus, the results are presented based on power extracted, shading loss, convergence factor and efficiency. The proposed HGWO–MPPT is found to be better as it has a maximum efficiency of 94.30% and a minimum convergence factor of 0.20 when compared with other techniques under varying conditions for different topologies. Furthermore, a practical assessment of the proposed controller on a 6.3 kWp rooftop SPV system is also presented in the paper. Energy production is increased by 8.55% using the proposed approach to the practical system.

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Grey Wolf Optimization Inspired Maximum Power Extraction from SPV System for Water Pumping Application

Cited by 3 publications

References 25 publications

Grey wolf optimization and incremental conductance based hybrid MPPT technique for solar powered induction motor driven water pump

Grey wolf optimization and incremental conductance based hybrid MPPT technique for solar powered induction motor driven water pump

A Sensorless MPPT Approach For PV Pumb System Used BLDC Motor

An Advanced and Robust Approach to Maximize Solar Photovoltaic Power Production

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