Parameter estimation of single diode PV module based on GWO algorithm

Nayak, Byamakesh; Mohapatra, Alivarani; Mohanty, Kanungo Barada

doi:10.1016/j.ref.2019.04.003

Cited by 82 publications

(32 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The structure of an SDM is shown in Figure 1, which contains an ideal constant current source I ph , a series resistance R s , a shunt resistance R sh , and a diode D (Ye et al, 2009;Murty and Kumar, 2020). An SDM is characterized by high simplicity and decent accuracy (Humada et al, 2016).…”

Section: Single Diode Modelmentioning

confidence: 99%

“…In recent years, due to rapid fossil fuel depletion (Peng et al, 2020), booming global energy demand (Shangguan et al, 2020a), and a series of severe eco-environmental problems (Yang et al, 2015), concepts of sustainable development and an environmentally friendly society are receiving increasingly widespread attention (Shangguan et al, 2020b). Hence, environmental protection (Sun et al, 2019) and energy structure transition (Sun and Yang, 2020) are becoming global development strategies via the application of renewable energies, e.g., solar (Zhang et al, 2019a;Murty and Kumar, 2020) and wind (Liu et al, 2020). Note that solar energy is one of the most Abbreviations: I ph , photocurrent, A; I d , I d1 , I d2 , diode's currents, A; I sd , I sd1 , I sd2 , I sd3 , diode's reverse saturation currents, A; R s , series resistor, Ω; R sh , shunt resistor, Ω; a, a 1 , a 2 , a 3 , diode's ideality factors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms

et al. 2021

View full text Add to dashboard Cite

Accurate solar cell modeling is essential for reliable performance evaluation and prediction, real-time control, and maximum power harvest of photovoltaic (PV) systems. Nevertheless, such a model cannot always achieve satisfactory performance based on conventional optimization strategies caused by its high-nonlinear characteristics. Moreover, inadequate measured output current-voltage (I-V) data make it difficult for conventional meta-heuristic algorithms to obtain a high-quality optimum for solar cell modeling without a reliable fitness function. To address these problems, a novel genetic neural network (GNN)-based parameter estimation strategy for solar cells is proposed. Based on measured I-V data, the GNN firstly accomplishes the training of the neural network via a genetic algorithm. Then it can predict more virtual I-V data, thus a reliable fitness function can be constructed using extended I-V data. Therefore, meta-heuristic algorithms can implement an efficient search based on the reliable fitness function. Finally, two different cell models, e.g., a single diode model (SDM) and double diode model (DDM) are employed to validate the feasibility of the GNN. Case studies verify that GNN-based meta-heuristic algorithms can efficiently improve modeling reliability and convergence rate compared against meta-heuristic algorithms using only original measured I-V data.

show abstract

Section: Single Diode Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Thus far, they have been widely adopted in PV cell parameter identification in recent years (Yang et al, 2020). For instance, genetic algorithm (GA) (Jervase et al, 2001), differential evolution (DE) , particle swarm optimization (PSO) (Ye et al, 2009), artificial bee colony (ABC) (Oliva et al, 2014;Yang et al, 2019a), bird mating optimizer (BMO) (Askarzadeh and Rezazadeh, 2013), whale optimization algorithm (WOA) (Elazab et al, 2018;Dasu et al, 2019), backtracking search algorithm (BSA) (Yu et al, 2018), month flame optimizer (MFO) (Allam et al, 2016), gray wolf optimization (GWO) (Yang et al, 2017;Nayak et al, 2019), biogeography-based optimization (BBO) (Niu et al, 2014), flower pollination algorithm (FPA) (Alam et al, 2015;Shang et al, 2018), harmony search (HS) (Askarzadeh and Rezazadeh, 2012), multiswarm spiral leader particle swarm optimization (MSLPSO) algorithm (Nunes et al, 2020), slime mold algorithm (SMA) (Mostafa et al, 2020) and so forth (Muangkote et al, 2019), along with their numerous hybrid/variants. Inspiringly, beetle antennae search (BAS) algorithm is a recently developed biology-based meta-heuristic algorithm (Jiang and Li, 2018), which basically replicates the searching mechanism of long-horn beetles.…”

Section: Introductionmentioning

confidence: 99%

A Powerful Bio-Inspired Optimization Algorithm Based PV Cells Diode Models Parameter Estimation

2021

View full text Add to dashboard Cite

Accurate and reliable photovoltaic (PV) cell parameter identification is critical to simulation analysis, maximum output power harvest, and optimal control of PV systems. However, inherent high-nonlinear and multi-modal characteristics usually result in thorny obstacles to hinder conventional optimization methods to obtain a fast and satisfactory performance. In this study, a novel bio-inspired grouped beetle antennae search (GBAS) algorithm is devised to effectively identify unknown parameters of three different PV models, i.e., single diode model (SDM), double diode model (DDM), and triple diode model (TDM). Compared against beetle antennae search (BAS) algorithm, optimization efficiency of GBAS algorithm is markedly enhanced based on a cooperative searching group that consists of multiple individuals rather than a single beetle. Besides, a dynamic balance mechanism between local exploitation and global exploration is designed to increase the probability for a higher quality optimum. Comprehensive case studies demonstrate that GBAS algorithm can outperform other advanced meta-heuristic algorithms in both optimization precision and stability for estimating PV cell parameters, e.g., standard deviation (SD) of root mean square error (RMSE) obtained by GBAS algorithm is 64.34% smaller than the best value obtained by other algorithms in SDM, 61.86% smaller than that in DDM.

show abstract

“…Analytical approaches require complex computational efforts and are not suitable for large or complex systems [20]. However, metaheuristic approaches are artificially intelligent population-based techniques that aim to find a global best solution for any optimization problem [1,21] by updating the population in an iterative process and are mostly utilized for estimating the PV parameters of cells and modules [22].…”

Section: Introductionmentioning

confidence: 99%

Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization

et al. 2020

View full text Add to dashboard Cite

Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 310 W polycrystalline PV module) which involve data collected under real environmental conditions for both single- and double-diode models. Results illustrate that the parameters obtained from proposed technique are better than those from the conventional PSO and various other techniques presented in the literature. Additionally, a comparison of the statistical results reveals that the proposed methodology is highly accurate, reliable, and efficient.

show abstract

Parameter estimation of single diode PV module based on GWO algorithm

Cited by 82 publications

References 37 publications

Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms

Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms

A Powerful Bio-Inspired Optimization Algorithm Based PV Cells Diode Models Parameter Estimation

Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization

Contact Info

Product

Resources

About