A Modified Incremental Conductance Maximum Power Point Technique for Standalone PV System

Chauhan, Urvashi; Rani, Asha; Singh, Vijander; Kumar, Bhavnesh

doi:10.1109/spin48934.2020.9071156

Cited by 14 publications

(8 citation statements)

References 20 publications

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“…Additionally, the disadvantages of the InC algorithm are also overcome by combining the InC algorithm with the GWO algorithm-based PID controller to minimize steadystate oscillations at the MPP. The obtained results show the effectiveness of the improved InC algorithm with a reduction of steady-state oscillations around the MPP as well as better tracking performance of the MPP of the PV module [37]. In addition, the fixed step size in the InC algorithm is also improved by an adaptive step size with variations.…”

Section: Siaw-pso Algorithmmentioning

confidence: 80%

“…Additionally, the MPP of a PV module on the voltagecurrent (U-I) and voltage-power (U-P) characteristics should also be determined and updated to ensure that the PV module is always operated adaptively and optimally under various operational conditions. There are currently many techniques applied to determine MPPs of the PV modules such as PO algorithms [34], [35] and InC algorithms [36], [37]. Several previous studies show that the PO algorithm exhibits disadvantages such as oscillations around the MPP in steadystate conditions, deviations from the MPP in quickly changing conditions of the irradiance and temperature and requirements of an appropriate perturbation step size [38].…”

Section: Siaw-pso Algorithmmentioning

confidence: 99%

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Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

2022

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The performance of a photovoltaic (PV) power generation system could be improved through the optimal control and operation of a PV module which is one of the fundamental components of this system. Thus, an appropriate PV module model along with precise knowledge of its parameters is necessary. This paper proposes a novel technique to estimate the source current, the saturation current of diodes, the shunt resistance, the series resistance, the ideality coefficient of diodes and the maximum power points (MPPs) of PV modules at the same time. This estimation problem can be described by the minimization of the root mean squared error (RMSE) of the powers obtained from the PV module through estimation and experiment. The improved stochastic fractal search (ISFS) algorithm is proposed to solve this minimization with two modifications. The first replaces the logarithmic function with the exponential function in the standard deviation of the diffusion technique to improve the exploration ability efficiently in the search space. The second utilizes the sine map instead of the uniform distribution in both the diffusion and update techniques for improving the performance of the ISFS algorithm. Numerical results demonstrate the remarkable ability of the ISFS algorithm in obtaining both the model parameters and MPPs of the PV module with high accuracy. The comparison shows that the ISFS algorithm outperforms other meta-heuristic algorithms such as a stochastic fractal search (SFS) algorithm, a particle swarm optimization (PSO) algorithm, and an improved particle swarm optimization (IPSO) algorithm in the proposed parameter estimation application.INDEX TERMS Estimation, maximum power point, meta-heuristic algorithms, model parameter, photovoltaic modules

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Section: Siaw-pso Algorithmmentioning

confidence: 80%

Section: Siaw-pso Algorithmmentioning

confidence: 99%

Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

2022

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“…These MPPT techniques can be divided into three types: conventional, artificial-intelligence (AI), and hybrid-based techniques. Constant voltage control (CVC) (Kumar et al, 2014;Derbeli et al, 2021), Hill-Climbing (Jordehi, 2016;Amir et al, 2017;Pavithra et al, 2021), Perturb & Observe (P&O) (Esram and Chapman, 2007;Yilmaz et al, 2019;Mousa et al, 2021), Sliding Mode Control (SMC) based MPPT technique (Ahmed and Salam, 2016), Incremental Conductance (IC) (Li and Wang, 2009;Kumar et al, 2014;Mamarelis et al, 2014;Bendib et al, 2015), incremental resistance (IR) based MPPT (Chauhan et al, 2020), and fractional voltage/current (Elbaset et al, 2015) are conventional techniques having less complex behavior and are easy to implement. Among these, P&O and IC algorithms are more prevalent.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of MPPT Control Techniques to Enhance Solar Energy Utilization and Convergence Time Under Varying Meteorological Conditions and Loads

Singh¹,

Singh²,

Verma

et al. 2022

Front. Energy Res.

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The electrical energy generated from solar energy photovoltaic (PV) technology is intermittent, varying, and irregular. With PV technology’s limited energy conversion efficiency, it is imperative to extract the maximum of converted energy. The zero slopes of the power versus voltage curve are utilized to determine the maximum power point. Conventional algorithms provide lower convergence time along with low power oscillations. This paper proposes an adaptive perturb and observe (A-P&O) maximum power point tracking (MPPT) technique for the energy conversion system. The primary objectives of the proposed technique are to obtain a more robust, better tracking capability, improved efficiency, and fast response lesser oscillations under steady-state with a simple structure to implement. Maximum power point (MPP) tracking under varying meteorological conditions and load variations is still a challenge. The proposed P&O technique has been tested under realistic meteorological variations and load variations. The comparative evaluation of the proposed adaptive-step size A-P&O MPPT technique and other conventional techniques such as perturb and observe (P&O), incremental conductance (IC), modified P&O and fuzzy logic control (FLC) have also been performed. The performance of the proposed control technique is evaluated using a MATLAB/Simulink environment. The obtained results confirm that the proposed control technique is superior in performance as compared to the other four conventional techniques.

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“…The proposed technique is simple and efficient but the drawback is that it cannot be applied in dynamically changing conditions and also its use is limited to low power application. According to Chauhan et al [14] overcomes this limitation to some extent where a modified IC algorithm has been proposed by optimizing the controller gain through Grey Wolf technique. However, results are satisfactory only for insolation variation and that too over a small scale only, as dynamic change of other parameters introduces a truncated error.…”

Section: Introductionmentioning

confidence: 99%

Enhanced model based algorithm to reinforce PV system with dynamic MPPT capability

Ehtesham

2021

IJAAS

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<p>Photovoltaic (PV) power has emerged as the most attractive resource in form of a clean and green energy. However, one major challenge associated with PV interfacing is its intermittent output characteristic which varies dramatically with the operating conditions. Thus designing an effective maximum power point tracking (MPPT) algorithm is a key aspect for optimizing PV system performance. Numerous MPPT algorithms have been proposed earlier having their own specific advantages. However, these are found to have two major limitations which have to be essentially addressed. Firstly, they become ineffective in the dynamic conditions where there is rapid change in environmental parameters like insolation and temperature. Secondly, they fail to discriminate between global and local peaks under partial shading conditions. Therefore, to achieve a reliable and efficient system operation, this paper presents an enhanced model-based (MB) algorithm that overcomes both these deficiencies. Based on new governing equations and precised estimation technique, it predetermines the MPP analytically. First simulated results are obtained where it is tested for dynamic variations of all the three parameters. Then the experimental validation is carried out on a 2 KW installed panel where real time data is recorded through CR1000 data logger and environmental parameters are sensed with elements like pyranometer and humidity sensor. A large number of experimental results are obtained for tracked MPP in the dynamic conditions, which are then summarized in tabular forms. These are finally plotted and compared with simulated results to illustrate the effectiveness of proposed MB algorithm.</p>

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A Modified Incremental Conductance Maximum Power Point Technique for Standalone PV System

Cited by 14 publications

References 20 publications

Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

Comparative Analysis of MPPT Control Techniques to Enhance Solar Energy Utilization and Convergence Time Under Varying Meteorological Conditions and Loads

Enhanced model based algorithm to reinforce PV system with dynamic MPPT capability

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