A robust global MPPT to mitigate partial shading of triple-junction solar cell-based system using manta ray foraging optimization algorithm

Fathy, Ahmed; Rezk, Hegazy; Yousri, Dalia

doi:10.1016/j.solener.2020.06.108

Cited by 82 publications

(54 citation statements)

References 31 publications

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“…Wherein, every individual moves towards the food and manta rays in its front, the update process for each individual is achieved by the obtaining best solution during each iteration, and obtaining the solution for the one that is in front of current individual. In MRFO method, the chain foraging process is mathematically modelled as follows [47]:…”

Section: B the Manta Ray Foraging Optimization Algorithm (Mrfo)mentioning

confidence: 99%

“…Besides, individuals swim towards their front side manta ray, which is followed by cyclone foraging stage. The above-mentioned movements can be mathematically formulated as follows [47]:…”

Section: B the Manta Ray Foraging Optimization Algorithm (Mrfo)mentioning

confidence: 99%

“…During this stage, each of the manta rays tends to perform swimming backward and forward around the food hinge, and then tumbling to the new position. The mathematical expression of this process can be performed as following [47]:…”

Section: B the Manta Ray Foraging Optimization Algorithm (Mrfo)mentioning

confidence: 99%

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An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems

et al. 2020

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Multi-area power systems inhere complicated nonlinear response, which results in degraded performance due to the insufficient damping. The main causes of the damping problems are the stochastic behavior of the renewable energy sources, loading conditions, and the variations of system parameters. The load frequency control (LFC) represents an essential element for controlling multi-area power systems. Therefore, the proper design of the controllers is mandatory for preserving reliable, stable and high-quality electrical power. The controller has to suppress the deviations of the area frequency in addition to the tie-line power. Therefore, this paper proposes a new frequency regulation method based on employing the hybrid fractional order controller for the LFC side in coordination with the fractional order proportional integral derivative (FOPID) controller for the superconducting energy storage system (SMES) side. The hybrid controller is designed based on combining the FOPID and the tilt integral derivative (TID) controllers. In addition, the controller parameters are optimized through a new application of the manta ray foraging optimization algorithm (MRFO) for determining the optimum parameters of the LFC system and the SMES controllers. The optimally-designed controllers have operated cooperatively and hence the deviations of the area frequency and tie-line power are efficiently suppressed. The robustness of the proposed controllers is investigated against the variation of the power system parameters in addition to the location and/or magnitude of random/step load disturbances.

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Section: B the Manta Ray Foraging Optimization Algorithm (Mrfo)mentioning

confidence: 99%

“…Besides, individuals swim towards their front side manta ray, which is followed by cyclone foraging stage. The above-mentioned movements can be mathematically formulated as follows [47]:…”

Section: B the Manta Ray Foraging Optimization Algorithm (Mrfo)mentioning

confidence: 99%

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An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems

et al. 2020

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“…To avoid this negative phenomenon, a maximum power point tracker based on artificial neural network (ANN) [1] and ANN with an augmented state feedback precise linearization (AFL) controller for the DC/DC converter in [2] have been proposed to extract the GMP and to adaptive act with the fast and sudden changes in the irradiance levels. Moreover, the distributed MPPT techniques have been addressed in [3], multilevel inverter MPPT [4], and optimization algorithms based DC-DC converter MPPT have been proposed in [5]- [7]. Another alternative solution has been recently proposed for handling the harm impact of the partial shading phenomenon is that rearranging the shaded modules in the array, this process is defined as a reconfiguration of the PV array.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Grey Wolf Optimizer for Optimal Design of Switching Matrix for Shaded PV array Dynamic Reconfiguration

et al. 2020

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One of the worst negative phenomena faced by photovoltaic (PV) array is the operation under the shadow phenomenon, which significantly affects the generated power. Multiple local maximum power point (MPP) and unique global MPP are generated from the shaded array. Therefore, regular dispersion of the shadow falling on the PV array surface is a vital issue to extract the GMP via reconfiguration of the shaded modules in the array. This paper proposes a recent approach based on Multi-objective grey wolf optimizer (MOGWO) to reconfigure the shaded PV array optimally. The main objective of the proposed MOGWO is providing the optimal structure for the switching matrix to minimize the row current difference and maximize the output power. The benefits of the proposed strategy is performing a dynamic reconfiguration process which closes to the reality. The proposed method is validated across 9 × 9 PV array with six shade patterns. MOGWO schemes results are compared with TCT and modified SuDoKu based on several statistical metrics. The comparison reveals the superiority of MOGWO in tackling the multi-peak issue in the P-V characteristics with harvesting the highest power levels. INDEX TERMS Multi-objective grey wolf optimizer; grey wolf optimizer; Total-cross-tied; PV reconfiguration, partial shading .

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“…Calasan et al [37] identified the unknown model parameters of single-phase transfer through a chaotic Logistic map based MRFO. Fathy et al [38] used the MRFO algorithm for extracting the global maximum power point from the Triple-junction solar-based array running under shadow conditions. Selem et al [39] extracted the unidentified model parameters of PEMFC using MRFO.…”

Section: Introductionmentioning

confidence: 99%

A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler

Turgut

2020

SN Appl. Sci.

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This research study aims to introduce chaos theory into the Manta Ray Foraging Optimization (MRFO) Algorithm and optimize a real-world design problem through the chaos-enhanced versions of this method. Manta Ray Foraging Optimization algorithm is a bio-inspired swarm intelligence-based metaheuristic algorithm simulating the distinctive food search behaviors of the manta rays. However, MRFO suffers from some intrinsic algorithmic inefficiencies such as slow and premature convergence and unexpected entrapment to the local optimum points in the search domain like most of the metaheuristic algorithms in the literature. Recently, random numbers generated by chaos theory have been incorporated into the metaheuristic algorithms to solve these problems. More than twenty chaotic maps are applied to the base algorithm and ten best performing methods are considered for performance evaluation on high-dimensional optimization test problems. Forty test problems comprising unimodal and multimodal functions have been solved by chaotic variants of MRFO and extensive statistical analysis is performed. Furthermore, thermo-economic design optimization of an air-fin cooler is maintained by the chaotic MRFO variants to assess their optimization capabilities over complex engineering design problems. Ten decisive design variables of an air fin cooler are optimized in terms of total annual cost rates and optimum solutions obtained by five best chaotic MRFO algorithms are compared to the preliminary design. A significant improvement is observed in the objective function values when MRFO with chaotic operators is applied to this considered thermal design problem.

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A robust global MPPT to mitigate partial shading of triple-junction solar cell-based system using manta ray foraging optimization algorithm

Cited by 82 publications

References 31 publications

An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems

An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems

Multi-Objective Grey Wolf Optimizer for Optimal Design of Switching Matrix for Shaded PV array Dynamic Reconfiguration

A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler

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