Arithmetic optimization approach for parameters identification of different PV diode models with FOPI-MPPT

Mahmoud, Mahmoud F.; Mohamed, Ahmed T.; Swief, R.A.; Said, Lobna A.; Radwan, Ahmed G.

doi:10.1016/j.asej.2021.10.007

Cited by 14 publications

(3 citation statements)

References 29 publications

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“…Besides, their output power is essentially influenced by dominant variations of climate weather, such as temperature and irradiance 4 . So, it is crucial to track the peak PV output related to solar irradiance and surrounding temperature by implementing different maximum power point tracking (MPPT) schemes [5][6][7] . The MPPT methods are categorized as conventional, artificial intelligence (AI), optimization, or hybrid MPPT 8 .…”

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confidence: 99%

Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system

Mohamed

Ahmed

Metwally

2023

Sci Rep

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This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimally selected using AOA. To accomplish this study, a 100-kW benchmark PV system connected to a medium distribution utility is constructed and analyzed employing MATLAB/SIMULINK. The optimization framework seeks to minimize four standard benchmark performance indices, then select the best of the best among them. To verify the efficacy of the recommended methodology, a comprehensive comparison is conducted between AOA-based PI-IC-MPPT, modified incremental conductance MPPT (MIC), grey wolf optimization (GWO), genetic algorithm (GA), and particle swarm optimization (PSO)-based MPPT. The proposed control approach has achieved a reduction of 61, 3, 4.5, and 26.9% in the rise time and a decrease of 94, 84.7, 86.6, and 79.3% in the settling time compared with MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively.

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Section: List Of Symbolsmentioning

confidence: 99%

Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system

Mohamed

Ahmed

Metwally

2023

Sci Rep

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“…The main strategy for finding the unknown PV parameters is adjusting the curve to predict the I-V curve, wherein the data points on the predicted I-V curve match with the experiment values. There are several heuristic methods used for the PV parameter estimation problem such as particle swarm optimization (PSO) [9,10], a genetic algorithm [11], cuckoo search [12], whippy Harris hawks optimization (WHHO) [8], grey wolf optimization (WGO) [13], musical chairs algorithm [14], arithmetic optimization algorithm [15], social spider algorithm (SSA) [16], symmetric chaotic gradient-based optimizer [17], as well as hybrid PSO and WGO [18]. It can be seen that the number of heuristic-based methods is larger than that of deterministic methods.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of photovoltaic module relied on golden jackal optimization

2024

Archives of Electrical Engineering

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Due to the nonlinear current-voltage (I-V) relationship of the photovoltaic (PV) module, building a precise mathematical model of the PV module is necessary for evaluating and optimizing the PV systems. This paper proposes a method of building PV parameter estimation models based on golden jackal optimization (GJO). GJO is a recently developed algorithm inspired by the idea of the hunting behavior of golden jackals. The explored and exploited searching strategies of GJO are built based on searching for prey as well as harassing and grabbing prey of golden jackals. The performance of GJO is considered on the commercial KC200GT module under various levels of irradiance and temperature. Its performance is compared to well-known particle swarm optimization (PSO), recent Henry gas solubility optimization (HGSO) and some previous methods. The obtained results show that GJO can estimate unknown PV parameters with high precision. Furthermore, GJO can also provide better efficiency than PSO and HGSO in terms of statistical results over several runs. Thus, GJO can be a reliable algorithm for the PV parameter estimation problem under different environmental conditions.

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“…Several optimization algorithms have also been proposed to solve the problem of parameter extraction in a solar PV system. These include the supply and demand opti-mizer [27], the Harris hawk optimization [17], the arithmetic optimization approach [28], the improved estimation of distribution algorithm [29], the heap-based optimizer [30], artificial parameterless optimization [31], the black widow optimization algorithm [32], hunter-prey-based optimization [33], the improved generalized normal distribution algorithm [5], the biogeography-based teaching learning algorithm [34], hybrid variants of artificial gorilla troops and honey badger algorithm techniques [35], a hybrid adaptive Jaya algorithm [1], and particle swarm optimization [36].…”

Section: Introductionmentioning

confidence: 99%

Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter Extraction

Moustafa,

Smaili,

Almalawi

et al. 2023

Electronics

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This article presents a modified intelligent metaheuristic form of the Dwarf Mongoose Optimizer (MDMO) for optimal modeling and parameter extraction of solar photovoltaic (SPV) systems. The foraging manner of the dwarf mongoose animals (DMAs) motivated the DMO’s primary design. It makes use of distinct DMA societal groups, including the alpha category, scouts, and babysitters. The alpha female initiates foraging and chooses the foraging path, bedding places, and distance travelled for the group. The newly presented MDMO has an extra alpha-directed knowledge-gaining strategy to increase searching expertise, and its modifying approach has been led to some extent by the amended alpha. For two diverse SPV modules, Kyocera KC200GT and R.T.C. France SPV modules, the proposed MDMO is used as opposed to the DMO to efficiently estimate SPV characteristics. By employing the MDMO technique, the simulation results improve the electrical characteristics of SPV systems. The minimization of the root mean square error value (RMSE) has been used to compare the efficiency of the proposed algorithm and other reported methods. Based on that, the proposed MDMO outperforms the standard DMO. In terms of average efficiency, the MDMO outperforms the standard DMO approach for the KC200GT module by 91.7%, 84.63%, and 75.7% for the single-, double-, and triple-diode versions, respectively. The employed MDMO technique for the R.T.C France SPV system has success rates of 100%, 96.67%, and 66.67%, while the DMO’s success rates are 6.67%, 10%, and 0% for the single-, double-, and triple-diode models, respectively.

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Arithmetic optimization approach for parameters identification of different PV diode models with FOPI-MPPT

Cited by 14 publications

References 29 publications

Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system

Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system

Parameter estimation of photovoltaic module relied on golden jackal optimization

Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter Extraction

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