Static and dynamic photovoltaic models’ parameters identification using Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants

Yousri, Dalia; Allam, Dalia; Eteiba, Magdy B.; Suganthan, Ponnuthurai Nagaratnam

doi:10.1016/j.enconman.2018.12.022

Cited by 157 publications

(66 citation statements)

References 37 publications

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“…In addition, the non-linear behavior of PV cells modeling becomes a challenging task [44]. By performing numerous experimentation's authors developed three types of diode models for enhancement of power generation; single-diode (SD) [45], the two-diode (DD) [46], [47], and the three-diode model (TD) [48]. Among these three models, SD is the most widely used one due to its numerous features [49], [50].…”

Section: Pv Module Modelingmentioning

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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Section: Pv Module Modelingmentioning

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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“…Therefore, metaheuristic algorithms have been introduced as an attractive alternative for estimating the PV cells and modules parameters [25]. Such approaches may include particle swarm optimization [26], genetic algorithms [27], differential evolution [28], cuckoo search [29], among others. From the circuit shown in Figure 1, it is possible to write an implicit equation that describes the relationship between the submodule's output voltage (V) and current (I), as shown in (1), where I sat and η are the inverse saturation current, and ideality factor of D, respectively, V t,d is the diode thermal voltage and I bd is the current through BD.…”

Section: Submodule Modelmentioning

confidence: 99%

A Solution of Implicit Model of Series-Parallel Photovoltaic Arrays by Using Deterministic and Metaheuristic Global Optimization Algorithms

et al. 2020

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The implicit model of photovoltaic (PV) arrays in series-parallel (SP) configuration does not require the LambertW function, since it uses the single-diode model, to represent each submodule, and the implicit current-voltage relationship to construct systems of nonlinear equations that describe the electrical behavior of a PV generator. However, the implicit model does not analyze different solution methods to reduce computation time. This paper formulates the solution of the implicit model of SP arrays as an optimization problem with restrictions for all the variables, i.e., submodules voltages, blocking diode voltage, and strings currents. Such an optimization problem is solved by using two deterministic (Trust-Region Dogleg and Levenberg Marquard) and two metaheuristics (Weighted Differential Evolution and Symbiotic Organism Search) optimization algorithms to reproduce the current–voltage (I–V) curves of small, medium, and large generators operating under homogeneous and non-homogeneous conditions. The performance of all optimization algorithms is evaluated with simulations and experiments. Simulation results indicate that both deterministic optimization algorithms correctly reproduce I–V curves in all the cases; nevertheless, the two metaheuristic optimization methods only reproduce the I–V curves for small generators, but not for medium and large generators. Finally, experimental results confirm the simulation results for small arrays and validate the reference model used in the simulations.

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“…Recently, researchers have introduced another method to improve the performance of the basic version of the algorithms by replacing these distributions. Chaos theory is an alternative approach for these distributions and become a new trend in the modification of the optimization algorithms [36,50]. Chaos is a deterministic, pseudo-random, non-converging, non-period and bounded method can be found in non-linear dynamical systems [51].…”

Section: Fractional Chaotic Flower Pollination Algorithm Variants (Fcmentioning

confidence: 99%

“…where, T is the number of iterations. x 0 indicates the initial value is 0.7 [36]. The listed maps are employed in two phases; in the initialization stage and in modifying the random variables in the main control equations of FPA.…”

Section: Fractional Chaotic Flower Pollination Algorithm Variants (Fcmentioning

confidence: 99%

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Fractional Chaos Maps with Flower Pollination Algorithm for Partial Shading Mitigation of Photovoltaic Systems

et al. 2019

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Solar Photovoltaic (PV) systems have become prominent and have attained the attention of energy engineers, governments and researchers. To achieve the maximum benefit from the PV system in spite of its nonlinear characteristic and environmental conditions, finding a robust maximum power point tracking method is essential. Over two decades, various researchers proposed numerous MPPT methods, but they failed to evaluate their methods on consistency, reliability, and robustness over several numbers of runs. Most of the researchers examined one configuration and they did not to consider the dynamic change in the irradiation conditions. Therefore, in this manuscript, the authors introduced a novel optimization technique Fractional chaotic Flower Pollination Algorithm (FC-FPA), by merging fractional chaos maps with flower pollination algorithm (FPA). The proposed technique, help FPA in extracting the Global Maximum Power Point (GMPP) under different partial shading patterns including with different PV array configurations. The proposed FC-FPA technique is tested and evaluated over 5 different patterns of partial shading conditions. The first three patterns are tested over 4S configuration made with Shell S36 PV module. The other two patterns are applied to the 4S2P configuration of Shell SM55 PV panels. The performance of the proposed variant is investigated by tracking the GMPP for abruptly changing shade pattern. Exclusive statistical analysis is performed over several numbers of runs. Comparison with perturb and observe MPPT technique is established. These results confirm that, the proposed method shows fast convergence, zero oscillation and rapid response for the dynamic change in irradiation with consistent behavior.

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Static and dynamic photovoltaic models’ parameters identification using Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants

Cited by 157 publications

References 37 publications

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

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

A Solution of Implicit Model of Series-Parallel Photovoltaic Arrays by Using Deterministic and Metaheuristic Global Optimization Algorithms

Fractional Chaos Maps with Flower Pollination Algorithm for Partial Shading Mitigation of Photovoltaic Systems

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