scholarly journals Identification study of solar cell/module using recent optimization techniques

Author(s):  
Mahmoud Abbas El-Dabah ◽  
Ragab Abdelaziz El-Sehiemy ◽  
Mohamed Ahmed Ebrahim ◽  
Zuhair Alaas ◽  
Mohamed Mostafa Ramadan

This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.

2019 ◽  
Vol 11 (23) ◽  
pp. 2795 ◽  
Author(s):  
Guojiang Xiong ◽  
Jing Zhang ◽  
Dongyuan Shi ◽  
Lin Zhu ◽  
Xufeng Yuan ◽  
...  

Extracting accurate values for involved unknown parameters of solar photovoltaic (PV) models is very important for modeling PV systems. In recent years, the use of metaheuristic algorithms for this problem tends to be more popular and vibrant due to their efficacy in solving highly nonlinear multimodal optimization problems. The whale optimization algorithm (WOA) is a relatively new and competitive metaheuristic algorithm. In this paper, an improved variant of WOA referred to as MCSWOA, is proposed to the parameter extraction of PV models. In MCSWOA, three improved components are integrated together: (i) Two modified search strategies named WOA/rand/1 and WOA/current-to-best/1 inspired by differential evolution are designed to balance the exploration and exploitation; (ii) a crossover operator based on the above modified search strategies is introduced to meet the search-oriented requirements of different dimensions; and (iii) a selection operator instead of the “generate-and-go” operator used in the original WOA is employed to prevent the population quality getting worse and thus to guarantee the consistency of evolutionary direction. The proposed MCSWOA is applied to five PV types. Both single diode and double diode models are used to model these five PV types. The good performance of MCSWOA is verified by various algorithms.


Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 627
Author(s):  
Mokhtar Said ◽  
Abdullah M. Shaheen ◽  
Ahmed R. Ginidi ◽  
Ragab A. El-Sehiemy ◽  
Karar Mahmoud ◽  
...  

Recently, the use of diverse renewable energy resources has been intensively expanding due to their technical and environmental benefits. One of the important issues in the modeling and simulation of renewable energy resources is the extraction of the unknown parameters in photovoltaic models. In this regard, the parameters of three models of photovoltaic (PV) cells are extracted in this paper with a new optimization method called turbulent flow of water-based optimization (TFWO). The applications of the proposed TFWO algorithm for extracting the optimal values of the parameters for various PV models are implemented on the real data of a 55 mm diameter commercial R.T.C. France solar cell and experimental data of a KC200GT module. Further, an assessment study is employed to show the capability of the proposed TFWO algorithm compared with several recent optimization techniques such as the marine predators algorithm (MPA), equilibrium optimization (EO), and manta ray foraging optimization (MRFO). For a fair performance evaluation, the comparative study is carried out with the same dataset and the same computation burden for the different optimization algorithms. Statistical analysis is also used to analyze the performance of the proposed TFWO against the other optimization algorithms. The findings show a high closeness between the estimated power–voltage (P–V) and current–voltage (I–V) curves achieved by the proposed TFWO compared with the experimental data as well as the competitive optimization algorithms, thanks to the effectiveness of the developed TFWO solution mechanism.


Author(s):  
Mohammad Babul Hasan ◽  
Yaindrila Barua

This chapter is mainly based on an important sector of operation research-weapon’s assignment (WTA) problem which is a well-known application of optimization techniques. While we discuss about WTA, we need some common terms to be discussed first. In this section, we first introduce WTA problem and then we present some prerequisites such as optimization model, its classification, LP, NLP, SP and their classifications, and applications of SP. We also discuss some relevant software tools we use to optimize the problems. The weapon target assignment problem (WTA) is a class of combinatorial optimization problems present in the fields of optimization and operations research. It consists of finding an optimal assignment of a set of weapons of various types to a set of targets in order to maximize the total expected damage done to the opponent. The WTA problem can be formulated as a nonlinear integer programming problem and is known to be NP-complete. There are constraints on weapons available of various types and on the minimum number of weapons by type to be assigned to various targets. The constraints are linear, and the objective function is nonlinear. The objective function is formulated in terms of probability of damage of various targets weighted by their military value.


Author(s):  
Xiaochao Qian ◽  
Wei Li ◽  
Ming Yang

Model calibration is the procedure that adjusts the unknown parameters in order to fit the model to experimental data and improve predictive capability. However, it is difficult to implement the procedure because of the aleatory uncertainty. In this paper, a new method of model calibration based on uncertainty propagation is investigated. The calibration process is described as an optimization problem. A two-stage nested uncertainty propagation method is proposed to resolve this problem. Monte Carlo Simulation method is applied for the inner loop to propagate the aleatory uncertainty. Optimization method is applied for the outer loop to propagate the epistemic uncertainty. The optimization objective function is the consistency between the result of the inner loop and the experimental data. Thus, different consistency measurement methods for unary output and multivariate outputs are proposed as the optimization objective function. Finally, the thermal challenge problem is given to validate the reasonableness and effectiveness of the proposed method.


Author(s):  
J. M. Hamel

The rise of the use of additive manufacturing processes by engineering and research enterprises has greatly increased opportunities for decision makers to quickly evaluate complex geometries and components throughout the design process. This capability makes it possible to explore design trade-off for which computational or analytical models are not readily available, or are impractical to obtain given available time and resources. However, this often means that decision makers are forced to rely primarily on physical experiments for design data, and this greatly limits opportunities for the application of design optimization techniques. To meet this challenge, a new so-called “online” surrogate based optimization approach, called Experiment Driven Local Optimization (EDLO), has been developed. This approach is focused specifically on using approximation techniques and real-time online surrogate training to solve design optimization problems where the system objective function must be evaluated using physical experiments. As a result, this approach is ideally suited to design problems focused on components that are fabricated using additive manufacturing. This approach is capable of mitigating the effects of experimental uncertainty (or noise) in a design objective function, does not require close coordination between the objective function evaluations and the optimizer, and requires as few physical experiments as possible. These capabilities have been demonstrated through the use of several numerical test problems and also through a 3D printed compliant mechanism design problem. The results produced by the EDLO approach are encouraging and show that this new technique has the potential to move decision makers working with physical experiments and additive manufacturing away from engineering intuition and towards the greater potential of design optimization techniques.


2018 ◽  
Vol 8 (11) ◽  
pp. 2155 ◽  
Author(s):  
Guojiang Xiong ◽  
Jing Zhang ◽  
Xufeng Yuan ◽  
Dongyuan Shi ◽  
Yu He

Extracting accurate values for relevant unknown parameters of solar cell models is vital and necessary for performance analysis of a photovoltaic (PV) system. This paper presents an effective application of a young, yet efficient metaheuristic, named the symbiotic organisms search (SOS) algorithm, for the parameter extraction of solar cell models. SOS, inspired by the symbiotic interaction ways employed by organisms to improve their overall competitiveness in the ecosystem, possesses some noticeable merits such as being free from tuning algorithm-specific parameters, good equilibrium between exploration and exploitation, and being easy to implement. Three test cases including the single diode model, double diode model, and PV module model are served to validate the effectiveness of SOS. On one hand, the performance of SOS is evaluated by five state-of-the-art algorithms. On the other hand, it is also compared with some well-designed parameter extraction methods. Experimental results in terms of the final solution quality, convergence rate, robustness, and statistics fully indicate that SOS is very effective and competitive.


Author(s):  
Yih-Charng Deng ◽  
Robert V. Lust

Abstract Models of mechanical systems often contain unknown system parameters which cannot be determined directly from component tests. System identification techniques are methods which determine the unknown parameters based on the overall system behavior under prescribed dynamic loading. In this work a methodology was developed for identifying the unknown parameters in a nonlinear mechanical system model which produces a high degree of correlation between the model response and the test data. This technique uses numerical optimization algorithms to minimize differences between the model response and the test data. Two algorithms are used: the BFGS quasi-Newton method and the zero-th order POLYTOPE method. Several example problems are solved which demonstrate our methodology. Both methods accurately identify the unknown parameters in simple mass-spring-damper systems. For more complex nonlinear systems, such as the occupant simulation models, the gradient-based BFGS method is unable to identify the correct parameters in two test cases. This is due to the fact that the objective function contains many local minima for these systems. The POLYTOPE method gives satisfactory results for identifying complex nonlinear systems in all three test cases investigated in this study. For complex nonlinear systems the form of the objective function can affect the final solution. Best results will be achieved when the objective function is highly sensitive to the changes of the unknown parameters.


Author(s):  
Essaki Raj R. ◽  
Sundaramoorthy Sridhar

Purpose This paper aims to apply grey wolf optimizer (GWO) algorithm for steady state analysis of self-excited induction generators (SEIGs) supplying isolated loads. Design/methodology/approach Taking the equivalent circuit of SEIG, the impedances representing the stator, rotor and the connected load are reduced to a single loop impedance in terms of the unknown frequency, magnetizing reactance and core loss resistance for the given rotor speed. This loop impedance is taken as the objective function and minimized using GWO to solve for the unknown parameters. By including the value of the desired voltage as a constraint, the formulated objective function is also extended for estimating the required excitation capacitance. Findings The experimental results obtained on a three phase 415 V, 3.5 kW SEIG and the corresponding predetermined performance characteristics agree closely, thereby validating the proposed GWO method. Moreover, a comparative study of GWO with genetic algorithm and particle swarm optimization techniques reveals that GWO exhibits much quicker convergence of the objective function. Originality/value The important contributions of this paper are as follows: for the first time, GWO has been introduced for the SEIG performance predetermination and computation of the excitation capacitance for attaining the desired terminal voltage for the given load and speed; the predicted performance accuracy is improved by considering the variable core loss of the SEIG; and GWO does not require derivations of lengthy equations for calculating the SEIG performance.


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