JAYA Algorithm Based on Lévy Flight for Global MPPT Under Partial Shading in Photovoltaic System

Motamarri, Rambabu; Bhookya, Nagu

doi:10.1109/jestpe.2020.3036405

Cited by 41 publications

(14 citation statements)

References 40 publications

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“…The proposed algorithm (LBNS) was evaluated by comparing it with the recently proposed JayaLF [16], and AJaya [17] algorithms. Moreover, JayaLF was initialized like our proposed algorithm because its initialization, as it is proposed in [16], led to inferior performance. Also, the JayaLF was sticking to local peaks due to weak exploration.…”

Section: Resultsmentioning

confidence: 99%

“…Then, the duty ratios are redistributed uniformly within the above bounds (lines 9-15). Finally, the two duty ratios corresponding to the most significant power values initialize our metaheuristic solution update equation (lines [16][17][18].…”

Section: ) Initializationmentioning

confidence: 99%

“…By doing so, we reduce the number of update equations to one that avoid searching useless regions and reduce computational burden. Finally, we evaluated the LBNS against two recently proposed algorithms we mentioned before ( [16] and [17]), and the results showed that it significantly outperforms them. In addition, the proposed solution reduction technique can be applied to other metaheuristic techniques to further improve their performance in MPP tracking.…”

Section: Introductionmentioning

confidence: 99%

“…In [15], a Modified Butterfly Optimization Algorithm (MBOA) limits the search area and avoids exploring useless regions. In [16], an enhancement of the Jaya algorithm, named Levy flight (JayaLF), is proposed. The algorithm is divided into two phases: the exploration and exploitation phases.…”

Section: Introductionmentioning

confidence: 99%

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A Reduced Search Space Exploration Metaheuristic Algorithm for MPPT

2022

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The necessity for clean and sustainable energy has shifted the energy sector's interest in renewable energy sources. Photovoltaics (PV) is the most popular renewable energy source because the sun is ubiquitous. However, PV's power transfer efficiency varies with different load's electrical characteristics, temperatures on PV panels, and insolation conditions. Based on these factors, Maximum Power Point Tracking (MPPT) is a mechanism formulated as an optimization problem adjusting the PV to deliver the maximum power to the load. Under full insolation conditions, varying solar panel temperatures, and different loads MPPT problem is a convex optimization problem. However, when the PV's surface is partially shaded, multiple power peaks are created in the power versus voltage (P-V) curve making MPPT non-convex. Unfortunately, all optimization strategies for MPPT under partial shading applied in previous works, from traditional techniques to Machine Learning and the recently proposed Natureinspired algorithms, were either computationally expensive or/and led to extensive power losses. To this end, this work presents an algorithm that builds upon metaheuristic optimization algorithms to reduce their complexity further and mitigate the power losses during power tracking. Our experimental results demonstrated that the proposed algorithm converges faster to maximum power point with lower power losses during tracking compared to two very recently proposed MPPT algorithms under partial shading conditions. INDEX TERMSMetaheuristic Algorithms, improved Limited Search Strategy (iLSS), Photovoltaic (PV), Partial Shading (PS), Maximum Power Point Tracking (MPPT).

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Section: Resultsmentioning

confidence: 99%

Section: ) Initializationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Reduced Search Space Exploration Metaheuristic Algorithm for MPPT

2022

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“…One of the most important aspects for researchers is determining the global maximum under partial shading conditions. Some recent studies include bidirectional DPP fly back converters [6], Levy flight (JAYA-LF) based JAYA algorithm [7], flying squirrel search optimization (FSSO) [8], a spline-MPPT Technique [9], PSO based method [10].These methodologies have trade-offs between fast tracking and accuracy of GMPP. One of the most advanced methodologies called Extreme Seeking Algorithm (ESA) [11] is employed in this paper for its better performance while tracking the GMPP.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Partial Shading Performance Evaluation With a DSTATCOM Controller

et al. 2022

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A solar photovoltaic distribution static compensator (SPV-DSTATCOM) under partial shading condition (PSC) is studied using a single-stage, 3-phase grid-connected mode. Under PSC, the performance of the grid-connected SPV-DSTATCOM is studied and addressed the issues such as active current sharing, reactive power control, and harmonic elimination. Rejection of DC offset with such PSC condition is an issue when using a conventional Proportional Resonant (PR) controller. As a result of this research, a new and improved PR (IPR) based second-order generalized integrator (SOGI) has been developed that has unity gain at the fundamental frequency and greater DC offset rejection capability to address the PR controller shortcomings. The proposed controller's performance is evaluated in both steadystate and dynamic conditions with varying loads and different PSC conditions. An experimental evaluation of the proposed controller's design assumptions is also presented in the form of a comparison with both the PR controller and the adaptive PR controller.

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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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JAYA Algorithm Based on Lévy Flight for Global MPPT Under Partial Shading in Photovoltaic System

Cited by 41 publications

References 40 publications

A Reduced Search Space Exploration Metaheuristic Algorithm for MPPT

A Reduced Search Space Exploration Metaheuristic Algorithm for MPPT

Photovoltaic Partial Shading Performance Evaluation With a DSTATCOM Controller

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

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