A Fast GMPPT Scheme Based on Collaborative Swarm Algorithm for Partially Shaded Photovoltaic System

Deboucha, Houssam; Shams, Immad; Belaid, Sofia Lalouni; Mekhilef, Saad

doi:10.1109/jestpe.2021.3071732

Cited by 22 publications

(11 citation statements)

References 24 publications

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“…Hence, the convergence speed of the PSO degrades. Furthermore, in Deboucha et al, 27 the random coefficients from the position updating equation are removed, which aids in accelerating convergence speed; however, due to increased exploitation, local power point entrapment may occur.…”

Section: Introductionmentioning

confidence: 99%

A novel constraint‐based improved equilibrium optimization for global peak tracking of photovoltaic system under complex shading conditions

Reddy

2023

Circuit Theory & Apps

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The P-V characteristics of photovoltaic (PV) array exhibit multiple peaks during partial shading conditions (PSCs). Traditional maximum power point tracking (MPPT) approaches are unable to locate the global power point and frequently converge to the local power point. In recent years, evolutionary techniques have given a satisfactory result by tracking global maximum power point (GMPP) but at the cost of ample memory storage and computational burden. Therefore, this paper proposes an improved equilibrium optimization (IEO) technique to track maximum power from the solar photovoltaic system under PSC. The reduced search space exploration by using the skipping method is implemented to improve the convergence speed. The proposed IEO algorithm is capable of differentiating between uniform shading, different PSCs, and solar intensity variation with fast tracking speed. Similarly, the proposed algorithm has been compared with deterministic Jaya (DM-Jaya), crow search algorithm (CSA), and equilibrium optimization (EO) algorithms. The effectiveness of the proposed IEO technique-based MPPT has been validated through simulations and experiments. The results demonstrate the capability of IEO in tracking GMPP with an average efficiency of 99.72% and average tracking time of 0.708 s. Further, the proposed algorithm has been implemented for grid integration under PSC. The results reveal that the proposed method has high PV tracking efficiency, negligible steady-state error, a low MPPT period, and a quick convergence velocity.

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Section: Introductionmentioning

confidence: 99%

A novel constraint‐based improved equilibrium optimization for global peak tracking of photovoltaic system under complex shading conditions

Reddy

2023

Circuit Theory & Apps

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“…The soft‐computing strategies include metaheuristic algorithms, hybrid algorithms, fuzzy logic control, and artificial neural networks (ANNs). In recent days, various metaheuristic optimization algorithms such as honey badger algorithm, 5 collaborative swarm algorithm (CSA), 6 particle swarm optimization (PSO), 6 ant colony‐based new pheromone update (ACO‐NPU), JAYA algorithm, 7 grey wolf optimizer (GWO), 8 logarithmic PSO (LPSO), 9 and pigeon‐inspired optimization 10 have been employed to determine the GMP under PS. All these algorithms can efficiently determine GMP additionally mitigating the steady‐state oscillations.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid image processing‐based reconfiguration with RBF neural network MPPT approach for improving global maximum power and effective tracking of PV system

Rao

Raj

Naik

2023

Circuit Theory & Apps

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SummaryThe solar photovoltaic (PV) array output is reduced significantly by the frequently occurring inevitable partial shading conditions. In consequence, the array exhibits multiple peaks in its characteristics that cause the conventional maximum power point tracking (MPPT) algorithms to get stuck at the local maximum. So, to track the global maximum power (GMP) among the multiple peaks, a novel radial basis function (RBF)‐based neural network approach has been proposed for predicting the optimal GMP. Additionally, a novel and intelligent encryption‐based ruler transform (RT) reconfiguration approach is proposed to disperse the shading effect enhancing the GMP and mitigating the multiple peaks. The effectiveness of the proposed RBF‐MPPT and novel RT‐reconfiguration strategies has been tested and analyzed for a 5 × 7 PV array under distinct dynamic, uniform, and nonuniform shading conditions. The results of the proposed RBF have been compared with the conventional incremental conductance (INC) algorithm before and after reconfiguration of the PV array. Further, the ease of GMP tracking by a simple conventional INC due to the reduction of peaks after the array reconfiguration under shading conditions has been demonstrated and discussed in detail. After reconfiguration, the GMP is enhanced by 37.35%, 31.41%, 30.86%, 21.46%, 13.69%, and 8.88%, using the proposed RBF for the considered five shading conditions. The steady‐state oscillations are also considerably mitigated by employing the proposed reconfiguration and RBF strategies.

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“…Zhang [27] proposed a hybrid MPPT method based on iterative learning control (ILC) and P&O algorithm to improve the dynamic response when the irradiance changes rapidly. Deboucha et al [28] have proposed to employ a collaborative swarm algorithms including PSO, Jaya, and modified ACO for MPPT.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions

Millah

Chang

Teshome

et al. 2022

IEEE Open J. Ind. Electron. Soc.

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A partial shading condition (PSC) is one of the most common problems in the photovoltaic (PV) system. It causes the output power of a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in a power-voltage (P-V) curve with multiple peaks. Grey wolf optimization (GWO) algorithm is a new optimization algorithm based on MHA. It has been used to solve optimization problems in many applications including MPPT for a PV system. However, the accuracy and tracking time in the original GWO (OGWO) can still be further improved for various PSCs. Therefore, there have been some modified grey wolf optimization (MGWO) algorithms proposed to improve the GWO. Nevertheless, only incremental improvement has been made. Therefore, a proposed modified GWO, named enhanced grey wolf optimization (EGWO) is proposed in this paper. The proposed method adds the weighting average, the pouncing behavior and nonlinear convergence factor in the OGWO. In particular, since real wolves may engage in pouncing action when they are hunting, inclusion of pouncing completes the GWO algorithm and yields great improvements. As will be shown via simulation and experiment, the EGWO can drastically reduce the tracking time (up to 45.5% of the OGWO) and the dynamic tracking efficiency can be improved by more than 2%, compared to the OGWO. Moreover, the EGWO achieves the highest maximum power point compared to some of the existing GWO and other swarm based algorithms.

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A Fast GMPPT Scheme Based on Collaborative Swarm Algorithm for Partially Shaded Photovoltaic System

Cited by 22 publications

References 24 publications

A novel constraint‐based improved equilibrium optimization for global peak tracking of photovoltaic system under complex shading conditions

A novel constraint‐based improved equilibrium optimization for global peak tracking of photovoltaic system under complex shading conditions

A novel hybrid image processing‐based reconfiguration with RBF neural network MPPT approach for improving global maximum power and effective tracking of PV system

An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions

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