A Particle-Swarm-Optimization-Based Parameter Extraction Routine for Three-Diode Lumped Parameter Model of Organic Solar Cells

Wei, Tiankuo; Yu, Fangyong; Huang, Gongyi; Xu, Chuanlong

doi:10.1109/led.2019.2926315

Cited by 34 publications

(14 citation statements)

References 17 publications

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“…However, the accuracy of SDM and DDM are less than the TDM circuit model, and therefore, the modeling of TDM is discussed in this section. Further, the fitness function to estimate the parameters of the TDM is formulated along with the concept of the Newton-Raphson method [33,94,95].…”

Section: Mathematical Modeling and Problem Formulationmentioning

confidence: 99%

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

Kumar¹,

Mary²

2021

J Comput Electron

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Parameter identification and accurate photovoltaic (PV) modeling from basic I-V information are necessary for simulation, optimization, and control of the PV systems. Therefore, this paper proposes an Improved-African Vultures Optimization (I-AVO) algorithm, which combines the general Opposition-Based Learning (OBL) and Orthogonal Learning to extract the unknown parameters of the solar Photovoltaic (PV) modules accurately and effectually. The proposed I-AVO algorithm is developed from the basic version of the recently proposed African Vultures Optimization (AVO) algorithm. The solar PV parameters estimation problem is considered to be a complex optimization problem with the characteristics such as multidimensional, nonlinear, Transcendental, and multi-modal. Therefore, the basic variant of AVO struggles to produce the optimal and is stuck at local optima when it handles this complex optimization problem. Therefore, the I-AVO is formulated by combining the features of OL and OBL, along with the AVO, to generate the optimal solution. Out of various PV models, Three-Diode Model has been considered to determine the parameters. Furthermore, Newton-Raphson (NR) technique is discussed to solve the chaotic behavior of the I-V curve relation. The obtained results proved that the proposed I-AVO along with NR, called I-AVO-NR, can accurately obtain the optimal solution. The superiority of the proposed algorithm is proved to be better than other advanced algorithms based on the obtained results and their comparison. Based on the statistical test value obtained from Friedman's test, the proposed algorithm stood first among eight algorithms with the ranking value of 1.542 for two case studies.

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Section: Mathematical Modeling and Problem Formulationmentioning

confidence: 99%

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

Kumar¹,

Mary²

2021

J Comput Electron

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“…These structures are influenced by natural events, such as swarming activities, mechanisms focused on nature, and physics. Genetic algorithm (GA) [18], [19], particle swarm optimization [20]- [22], enhanced leader particle swarm optimization algorithm (PSO) [23], niche particle swarm optimization in parallel computing algorithm [24], several versions of differential evolution (DE) [25]- [28], penalty-based DE algorithm [29], sunflower optimizer [30], grey wolf optimizer (GWO) [31], whale optimizer algorithm (WOA) [32], harris-hawk optimizer (HHO) [33], improved salp swarm algorithm (ISSA) [34], several version of JAYA algorithm [35], multiple learning backtracking search algorithm [36], coyote optimization algorithm [37], teaching-learning-based optimization and its various versions [38]- [44], political optimizer (PO) [4], evolutionary shuffled frog leaping algorithm [45], slime-mould optimizer (SMO) [46], [47], marine predator algorithm (MPA) [48], equilibrium optimizer (EO) [49], ions motion optimization (IMO) [50], improved PSO (IPSO) [51], Forensic-based investigation algorithm [52], and improved learning-search algorithm [53] are among good heuristic-based structures. Some studies have endeavored to hybridize a few of these strategies to boost their performance, such as hybrid grey wolf optimizer with cuckoo search algorithm [54], hybrid firefly with pattern search algorithms [55], hybrid grey wolf optimizer with particle swarm algorithm [56], hybrid WO with DE algorithm [26], hybrid GA with simulated annealing algorithm [18], etc.…”

Section: Introductionmentioning

confidence: 99%

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Premkumar¹,

Jangir²,

Ramakrishnan³

et al. 2021

IEEE Access

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When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I)voltage (V) and power (P) voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer's datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1. 8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite.

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“…At present, many meta-heuristic methods have been used to extract PV model parameters. For example, genetic algorithm (GA) [24], [26], particle swarm optimization (PSO) [27]- [30], adaptive differential evolution (ADE) [31]- [33], ant lion optimizer (ALO) [34], artificial bee colony (ABC) [35]- [37], cuckoo search (CS) [38], hybrid flower pollination (HFP) [39], harmony search (HS) [40], water cycle (WC) [41], and improved whale optimization (IWO) [42], etc. Although metaheuristic algorithms can obtain relatively satisfactory results in the parameter extraction of PV models, most of them are difficult to find accurate global optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Parameter Extraction of Photovoltaic Models Using a Dynamic Self-Adaptive and Mutual- Comparison Teaching-Learning-Based Optimization

Xiong

Yuan

et al. 2021

IEEE Access

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A Particle-Swarm-Optimization-Based Parameter Extraction Routine for Three-Diode Lumped Parameter Model of Organic Solar Cells

Cited by 34 publications

References 17 publications

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Parameter Extraction of Photovoltaic Models Using a Dynamic Self-Adaptive and Mutual- Comparison Teaching-Learning-Based Optimization

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