Photovoltaic Cells Parameter Estimation Using an Enhanced Teaching–Learning-Based Optimization Algorithm

Ramadan, Abdelhady; Kamel, Salah; Korashy, Ahmed; Yu, Jie

doi:10.1007/s40998-019-00257-9

Cited by 56 publications

(48 citation statements)

References 40 publications

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“…As shown in Figure 2, the equivalent double diode circuit consists of two diodes to make voltage-current characteristics more accurate [4]. The output current ( ) of the DD model is formulated as [6]:…”

Section: The Double Diode Modelmentioning

confidence: 99%

“…Much research has been done because of the prevalence of solar energy applications. One of the main topics of research is obtaining an accurate model for solar cells [4]. In general, the power system operation and its performance are affected by increasing the penetration of photovoltaic systems.…”

Section: Introductionmentioning

confidence: 99%

“…The PV models based on accurate estimated parameters could be used in several studies in power systems such as power flow analysis, power system security, reliability, etc. [4]. Mathematical models of solar cells are divided into three types based on the number of diodes included in the circuit: single diode (SD), double diode (DD), and triple diode (TD).…”

Section: Introductionmentioning

confidence: 99%

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Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation

et al. 2021

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Recently, photovoltaic (PV) energy has been considered one of the most exciting new technologies in the energy sector. PV power plants receive considerable attention because of their wide applications. Consequently, it is important to study the parameters of the solar cell model to control and determine the characteristics of the PV systems. In this study, an improved bonobo optimizer (IBO) was proposed to improve the performance of the conventional bonobo optimizer (BO). Both the IBO and the BO were utilized to obtain the accurate values of the unknown parameters of different mathematical models of solar cells. The proposed IBO improved the performance of the conventional BO by enhancing the exploitation (local search) and exploration (global search) phases to find the best optimal solution, where the search space was reduced using Levy flights and the sine–cosine function. Levy flights enhance the explorative phase, whereas the sine–cosine function improves the exploitation phase. Both the proposed IBO and the conventional BO were applied on single, double, and triple diode models of solar cells. To check the effectiveness of the proposed algorithm, statistical analysis based on the results of 20 runs of the optimization program was performed. The results obtained by the proposed IBO were compared with other algorithms, and all results of the proposed algorithm showed their durability and exceeded other algorithms.

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Section: The Double Diode Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation

et al. 2021

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“…Conversely, the numerical (metaheuristic) evolutionary and hybrid algorithms are capable of escaping from local optima and reaching the global optimum solution easily. As per the literature, there are many metaheuristic optimization algorithms used in the estimation of PV parameters estimation, such as: Particle Swarm Optimization (PSO) [12], Artificial Bee Colony (ABC) [13], Real Coded Genetic Algorithm (RCGA) [14], Cuckoo Search (CS) [15] , Biogeography-based Heterogeneous Cuckoo Search (BHCS) [16], Firefly Algorithm (FA) [17], Moth-Flame Optimization Algorithm (MFOA) [18], Bee Pollinator Flower Pollination Algorithm (BPFPA) [19], Pattern Search (PS) [20], Harmony Search (HS) [21], Fish Swarm Algorithm (FSA) [22], Ant Lion Optimizer (ALO) [23], Water Cycle Algorithm (WCA) [24], Jaya algorithm [25], Hybridized Interior Search Algorithm (HISA) [26], Artificial Immune System (AIS) [27], Salp Swarm Algorithm (SSA) [28], Artificial Biogeography based Optimization Algorithm with Mutation (BOA-M) [29], Elephant Herd Algorithm (EHA) [30], an Artificial Bee Colony-Differential Evolution (ABC-DE) [31], improved adaptive Nelder-Mead Simplex(NMS) hybridized with ABC algorithm, hybrid EHA-NMS [32], Improved Adaptive DE (IADE) [33], Chaotic Asexual Reproduction Optimization (CARO) [34], Improved Shuffled Complex Evolution (ISCE) [35], Heterogeneous Comprehensive Learning Particle Swarm Optimizer (HCLPSO) [36], Mutative-scale Parallel Chaos Optimization Algorithm (MPCOA) [37], Artificial Ecosystem optimization [38], Marine Predators Algorithm (MPA) [39], Enhanced Teaching-Learning-Based Optimization (ETLBO) algorithm [40], Coyote Optimization Algorithm (COA) [41], Harris Hawk Optimization (HHO) [42], Sunflower Optimization (SFO)…”

Section: Introductionmentioning

confidence: 99%

A Novel Solution Methodology Based on a Modified Gradient-Based Optimizer for Parameter Estimation of Photovoltaic Models

et al. 2021

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In this paper, a modified version of a recent optimization algorithm called gradient-based optimizer (GBO) is proposed with the aim of improving its performance. Both the original gradient-based optimizer and the modified version, MGBO, are utilized for estimating the parameters of Photovoltaic models. The MGBO has the advantages of accelerated convergence rate as well as avoiding the local optima. These features make it compatible for investigating its performance in one of the nonlinear optimization problems like Photovoltaic model parameters estimation. The MGBO is used for the identification of parameters of different Photovoltaic models; single-diode, double-diode, and PV module. To obtain a generic Photovoltaic model, it is required to fit the experimentally obtained data. During the optimization process, the unknown parameters of the PV model are used as a decision variable whereas the root means squared error between the measured and estimated data is used as a cost function. The results verified the fast conversion rate and precision of the MGBO over other recently reported algorithms in solving the studied optimization problem.

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“…For example, Ref. [48] introduced an elite replacement strategy to TLBO and showed good performance in the process of solving complex nonlinear optimization functions. Ref.…”

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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Photovoltaic Cells Parameter Estimation Using an Enhanced Teaching–Learning-Based Optimization Algorithm

Cited by 56 publications

References 40 publications

Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation

Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation

A Novel Solution Methodology Based on a Modified Gradient-Based Optimizer for Parameter Estimation of Photovoltaic Models

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

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