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In this paper, a new approach based on adaptive Differential Evolution Technique (DET) is used to extract the parameters of solar cell models accurately. The adaption is achieved through crossover and mutation factor. It is indicated that the optimization with an objective function can minimize the difference between the estimated and measured valu...
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... equivalent circuit model of a solar cell using double diode model is shown in Fig. 2. The governing equation for this equivalent circuit is formulated using Kirchoff's current law for current I t and it is given ...
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... optimal value of IAE for each measurement using DET and other parameter extraction techniques such as CPSO, ABSO, PS, SA, OIS, and DAB are listed in Table 6. The comparison between CPSO, ABSO, PS, SA, OIS, DAB, and the proposed method with the optimal value of IAE for each measurement is illustrated in Fig. 12. From Fig. 12 it is indicated that the DET method have better performances than other parameters extraction methods such as CPSO, ABSO, PS, SA, OIS, and DAB. The total IAE values for each measurement is also calculated and listed in Table 6. The total IAE value of Table 6 points out that the DET has the lowest total IAE compared to ...
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... optimal value of IAE for each measurement using DET and other parameter extraction techniques such as CPSO, ABSO, PS, SA, OIS, and DAB are listed in Table 6. The comparison between CPSO, ABSO, PS, SA, OIS, DAB, and the proposed method with the optimal value of IAE for each measurement is illustrated in Fig. 12. From Fig. 12 it is indicated that the DET method have better performances than other parameters extraction methods such as CPSO, ABSO, PS, SA, OIS, and DAB. The total IAE values for each measurement is also calculated and listed in Table 6. The total IAE value of Table 6 points out that the DET has the lowest total IAE compared to other methods for ...
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... the DET method have better performances than other parameters extraction methods such as CPSO, ABSO, PS, SA, OIS, and DAB. The total IAE values for each measurement is also calculated and listed in Table 6. The total IAE value of Table 6 points out that the DET has the lowest total IAE compared to other methods for the PV module. From Table 6 and Fig. 12 it is indicated that DET outperforms CPSO, ABSO, PS, SA, OIS, and DAB for this parameter extraction ...
Citations
... Thus, Niu et al. used the chaotic approach to build an improved version of biogeographically based optimization (BBO) [13,14]. They successfully adopted the fitness value to automatically use the mutation factor (F) and crossover rate (CR) of the differential evolution (DE) method, while other authors [15,16] applied a new approach to compute the control parameters of the DE algorithm. Recently, Jian proposed a logistic chaotic map strategy to obtain the logistic chaotic JAYA algorithm (LCJAYA) [17]. ...
... The parameters are estimated based on experimental data from the RTC France cell [37] at standard test conditions for irradiation and a temperature of 33 °C for 26 points. This comparison clearly shows that the method proposed herein performs better with an RMSE value of 7.7035 × 10 -4 , smaller than the values achieved by the literature methods of 9.3 × 10 -4 [15], 7.73006 × 10 -4 [21], 9.8602 × 10 -4 [16], 9.8602 × 10 -4 [17], and 9.8602 × 10 -4 [18]. The experimental and estimated curves are shown in Fig. 3a and b, illustrating the good agreement between the experimental and numerical computation results. ...
... We present a comparison between the proposed approach and some recent algorithms found in Refs. [15][16][17][18]21] as well as Refs. [34][35][36] when the temperature is varied. ...
With renewable energy currently making the headlines, photovoltaic technology has shown significant potential as one of the best energy sources. It thus becomes necessary to predict the performance of photovoltaic systems by modeling it accurately and optimally. We propose a new hybrid algorithm for extracting PV cell parameters to improve their performance and efficiency when subjected to temperature variations. A metaheuristic approach is combined with an analytical approach to improve the accuracy and robustness. We call this approach improved differential evolution (IDE). The performance of the proposed method is evaluated for selected cell data. For validation, several analyses and comparisons are made with other methods, and the results illustrate the accuracy and precision of IDE. The proposed technique can estimate the parameters in an optimal way at any temperature, together with high convergence speed and a short simulation time.
... This modification enhances the searching capabilities by not getting trapped into the local minima. 149 Presented adaptive differential evolution technique (DET) for parameter extraction of SDM and DDM. To measure the precision of the developed method, the outcome was validated with experimental data under various working constraints. ...
Beyond meeting power demand, switching to solar energy especially solar photovoltaic (PV) offers many advantages like modularity, minimal maintenance, pollution free, and zero noise. Yet, its cell modeling is critical in design, simulation analysis, evaluation, and control of solar PV system; most importantly to tap its maximum potential. However, precise PV cell modeling is complicated by PV nonlinearity, presence of large unknown model parameter, and absence of a unique method. Since number of model parameters involved is directly related to model accuracy, and efficiency; determination of its values assume high priority. Besides, application of meta‐heuristic algorithms via numerical extraction is popular as it suits for any PV cell/module types and operating conditions. However, existence of many algorithms have drawn attention toward assessment of each method based on its merits, demerits, suitability/ability to parameter estimation problem, and complexity involved. Hence, few authors reviewed the subject of PV model parameter estimation. But existing reviews focused on comparative analysis of analytical and meta‐heuristic approaches, analysis of models, and application of meta‐heuristic methods for model parameter extraction. Thus, lack a comprehensive analysis on methods based on different objective function, assessment on environmental conditions, and cumulative analysis on selective set of algorithm based on efficiency. Therefore, this work reviews optimization algorithms presented for parameter estimation focusing on (a) objective function used, (b) modeling type, (c) algorithm employed for parameter extraction, and (d) PV technology. Further, provides a comprehensive assessment on various modules types used for validation, comparisons made with methods, advantages and disadvantages associated with each method with respect to parameter estimation platform, critical analysis on each method at STC, and varying irradiance conditions. In addition, a critical evaluation on specific set of algorithm based on objective function values is also carried out. Thus explores and display the characteristics of various techniques related to PV cell modeling and serve to be a single reference for researchers working in the field of PV parameter estimation. Graphical abstract explains about cell modeling and its parameters can be estimated via analytical and meta‐heuristic methods; irrespective of the method, both the them intend to estimate the parameters using mathematical equations or by following iterative steps.
... A penaltybased different evolution (P-DE) [37] was proposed, and it can be applied to parameter identification for solar PV modules accurately. The adaptable form of DE [38] was also revealed for parameter extraction of solar-powered cell models, and the strict exploratory insights have confirmed the property of the proposed calculation. Gao et al. [39] also designed variant DE for parameter estimation of solar photovoltaic models, and a multi-population parallel co-evolutionary DE was also used for this case [40]. ...
The efficient identification of the unknown and changeable photovoltaic parameters is a matter of considerable interest to model photovoltaic systems. The accurate and efficient parameters are important in converting the entire photovoltaic system from solar to electricity. This paper, an ensemble multi strategy-driven shuffled frog leading algorithm (EMSFLA), is proposed to optimize photovoltaic modules' parameters and enhance solar energy conversion efficiency. In the EMSFLA, opposition-based learning can consider the opposite position in each frog memeplex to enhance the convergence velocity and keep the population diversity. The mutation and crossover operators abstracted from differential evolution with greedy strategy can better balance diversification and intensification during the optimization process. Then, the performance of the EMSFLA is preliminarily verified on representative benchmark functions compared to a slice of state-of-the-art algorithms. After that, the EMSFLA is employed to identify these parameters of single, double diode effectively, and photovoltaic modules thoroughly. Finally, the proposal's stability is further investigated on various temperatures and irradiation hierarchies on several manufacturers' datasheets. The outcome of statistical experiments has indicated that the EMSFLA performs higher accuracy and reliability in estimating photovoltaic mode's critical parameters, and it may be taken as a potential tool for parameter identification tasks in photovoltaic systems. For further info about this research, you can visit resources at https://aliasgharheidari.com.
... Wei et al. [10] 2011 CPSO SDM: RMSE of 1.3900 −03 % Askarzadeh et al. [22] 2012 HS DDM: RMSE of 1.2600 × 10 −03 % Oliva et al. [8] 2014 ABC SDM: RMSE of 9.8615 −04 % DDM: RMSE of 9.8387 −04 % TDM: RMSE of 9.8452 −04 % Chellaswamy and Ramesh [12] 2016 Adaptive DE algorithm SDM: RMSE of 9.3 × 10 −4 , DDM: RMSE of 0.000924%, PV: RMSE of 0.002131% Guo et al. [11] 2016 CSO SDM : RMSE of 9.8602 × 10 −04 % DDM: RMSE of 9.8252 × 10 −04 % Lin et al. [9] 2017 MSSO SDM: RMSE of 9.8607 −04 % DDM: RMSE of 9.8281 −04 % Hamid et all. [13] 2017 GA with convex combination crossover SDM: RMSE of 9.8602 × 10 −04 % DDM: RMSE of 9.8248 × 10 −04 % PV: RMSE of 0.002425% Oliva et al. [14] 2017 WOA with chaotic maps SDM: RMSE of 9.8602 × 10 −04 % DDM: RMSE of 9.8272 × 10 −04 % Gae et al. [15] 2018 SCE with a competitive complex evolution strategy SDM: RMSE of 9.860219 × 10 −04 DDM: RMSE of 9.824849 × 10 −04 % Abd Elaziz and Oliva [19] 2018 WOA with an opposition-based learning strategy SDM: RMSE of 9.8602 × 10 −04 % DDM: RMSE of 9.8251 × 10 −04 % TDM: RMSE of 9.8249 × 10 −04 % Kang et al. [16] 2018 ImCSA SDM: RMSE of 9.8602 −04 % DDM: RMSE of 9.8249 −04 % Chen et al. [18] 2019 ISCA SDM: RMSE of 9.8602 −04 % DDM: RMSE of 9.8237 −04 % Li et al. [17] 2019 TLO SDM: RMSE of 9.8609 −04 % DDM: RMSE of 9.8612 −04 % TDM: RMSE of 9.8613 −04 % Diab and Ahmed A Zaki [20] 2020 COA SDM: RMSE of 7.7547 −04 % DDM: RMSE of 7.6480 −04 % Kumar et al. [21] 2020 the behavior of a PV solar module due to its average complexity and precise results. ...
The parameter estimation of solar cell models is considered an important problem in the computational simulation and design of photovoltaic (PV) systems. In this paper, the PV parameters of single, double, and triple-diode models are extracted and tested under different environmental conditions. The parameter estimation of the three models is presented as an optimization process with an objective function to reduce the difference between the measured and calculated data. Moreover, a new optimization algorithm for extracting the PV parameters of the single, double, and three-diode models called the Manta Ray Foraging Optimization (MRFO) algorithm has been proposed. The results show that the obtained parameters are the optimal values and give the least difference between the measured and calculated data compared with other algorithms.
... Due to its remarkable advantages, DE and its variants are widely applied to the PV models parameter extraction problem [40][41][42][43][44][45][46][47][48][49][50][51]. In [40], a penalty based DE (P-DE) was proposed to extract different PV models parameters. ...
... An improved free search DE (IFSDE) proposed in [43], was used to identify the parameter of solar cells under two conditions. In [44], a new adaptive DE technique (DET) was proposed to extract the parameters of solar cell models accurately. In this method, the adaption consists of population, CR, and F. The feasibility of the DET has been verified by different PV models. ...
... DET [44] An adaptive DE technique by improving poplation diversity and paramters CR and F adaptation was proposed to extract the parameters of solar cell models accurately. ...
Photovoltaic (PV) cells are widely used for their clean and sustainable advantages, forcing researchers to accurately model their characteristics. The behavior of PV cells can be derived from their current–voltage characteristics, depending on their unknown circuit model parameters. Due to the simulation, evaluation, control, and optimization of PV systems, it is essential to accurately and reliably extract the parameters of PV models. However, because of the non-linear, multi-variable, and multi-modal characteristics, it is still a very challenging task. With the rapid development of intelligent computing, various meta-heuristic algorithms have been devoted to extracting the parameters of different PV models. The purpose of this paper is to comprehensively review the meta-heuristic algorithms and their related variants that have been used to extract the parameters of different PV models. Different from the existing research works, this paper presents a comprehensive review based on the reliability, robustness, computational resources, and time complexity of the algorithm. These features are essential to design an algorithm for efficient parameter extraction of PV models. Based on the conducted review, some useful recommendations are provided, which have important reference significance when designing the new parameter extraction methods of PV models and are of great significance for further improving the performance, control, and design of PV cells.
... (2) (4) and (5) merely simplify the expression of the equations and can be derived from the following equations [2,3,8,[18][19][20][21][22]: ...
... In order to solve the equation system, it is also necessary to determine the reference value of the photo current I F ref and the reference value of the saturation current of the diode I 0 ref [2,8,21,22]. These two parameters can be expressed by substituting the open circuit and short circuit cases into Eq. ...
... (1). The values of the two parameters, after further sorting, can be expressed as follows [2,8,18,21,22]: ...
Many factors determine the efficient operation of a photovoltaic cell. These factors can be the intensity and spectral composition of illumination, the surface temperature, the ambient temperature, and the amount contaminations in the air and on the surface of the cells. The aim of the present study is to describe the effect of temperature gradient on the voltage and amperage changes, as well as the power output of a commercial solar cell through experimental methods and numerical simulations performed in MATLAB. The transient temperature investigations have allowed better understanding the time-dependent behavior of a solar cell under constant intensity illumination. Measurements prove that an increase in the surface temperature of the solar cell significantly reduces its performance. Measurements performed with the solar simulator show good conformity with simulated results.
... Tekli ve çoklu diyot modellerinde parametrelerinin hesaplanması optimizasyon problemine dönüştürülmüştür. Optimizasyon probleminde kullanılan amaç fonksiyonu vasıtasıyla ölçülen ve hesaplanan akım değerleri arasındaki hata değerleri en aza indirgenmektir [24]. Amaç fonksiyonunu ve kısıtları sağlayan kontrol değişkenleri ile minimum veya maksimum değer elde edilebilir. ...
Güneş pili modellenmesinde parametrelerin optimizasyonu sistemin farklı çalışma koşullarında durumunu izlemek ve modeldeki olası hataları bulmaya imkân sağlar. Güneş pillerinin tek ve çift diyot modellerindeki optimal parametrelerinin doğru ve verimli çıkarılması amacıyla parçacık sürü optimizasyon(PSO), ateş böceği (FA), guguk kuşu (CS) ve çiçek tozlaşma (FPA) meta-heuristik algoritmaları kullanılmıştır. Tekli ve çift diyot modellerinin hesaplanan ve deneysel veriler arasındaki hatayı minimize etmek amacıyla IAE ve RMSE amaç fonksiyonları kullanılmıştır. Geliştirilen algoritmaların performanslarını incelemek amacıyla literatürde bulunan diğer meta-heuristik algoritmalarla sayısal ve grafiksel olarak karşılaştırılmıştır. Karşılaştırmalı analiz verileri FPA’nın diğer yöntemlere göre yakınsama hızının daha hızlı, daha sağlam, verimli ve doğruluk açısından en iyi performansa sahip olduğu gösterilmiştir.
... The second method is the numerical method, which requires a lot of measured I-V data compared with the analytical solution method for sampling several key points. Some research papers have discussed some statistical algorithms to extract five parameters, such as improved stochastic evolutionary algorithm [8], adaptive differential evolutionary algorithm [9], particle swarm optimization algorithm [10], following the basic idea of "establishing objective function-determining initial value-iterative calculation". These methods are sensitive to initial values and prone to be trapped in local minima. ...
Traditional modelling methods are generally forward modelling, that is, the basic parameters of photovoltaic cells provided by manufacturers need to be known before in order to calculate the power output of photovoltaic cells. However, the traditional modelling method is no longer applicable to complex situations, such as if manufacturer parameters are not available, or if the original manufacturer parameters are inaccurate due to prolonged usage. In this study, a new inverse modelling method is proposed, which uses genetic algorithm (GA) to identify the internal parameters of photovoltaic cell equation and solve the equation by Gauss-Seidel iterative method. The experimental test was conducted in Wuhan, China. The results show the root mean square error is only 1.34V and 2.66V for two-day tests, which demonstrated the effectiveness of the proposed method.
... Muhsen et al. [40] presented an improved differential evolution with adaptive mutation per iteration algorithm (DEAM). Chellaswamy and Ramesh [41] presented adaptive differential evolution algorithm. Mohamed and Abdulaziz [42] proposed a differential evolution with novel mutation and adaptive crossover strategies to solve largescale global optimization problem. ...
In the past few decades, a lot of optimization methods have been applied in estimating the parameter of photovoltaic (PV) models and obtained better results, but these methods still have some deficiencies, such as higher time complexity and poor stability. To tackle these problems, an enhanced success history adaptive DE with greedy mutation strategy (EBLSHADE) is employed to optimize parameters of PV models to propose a parameter optimization method in this paper. In the EBLSHADE, the linear population size reduction strategy is used to gradually reduce population to improve the search capabilities and balance the exploitation and exploration capabilities. The less and more greedy mutation strategy is used to enhance the exploitation capability and the exploration capability. Finally, a parameter optimization method based on EBLSHADE is proposed to optimize parameters of PV models. The different PV models are selected to prove the effectiveness of the proposed method. Comparison results demonstrate that the EBLSHADE is an effective and efficient method and the parameter optimization method is beneficial to design, control, and optimize the PV systems.
... 4,9,10 However, other parameter optimization algorithms do exist that can effectively optimize IV curve parameters including Particle swarm optimization 11 ; Harmony Search algorithm 12 ; Differential Evolution. 13 The GA algorithms used within this paper were coded in such a way to effectively make use of GPU processing to minimize on computing time. However, this paper's focus is the method of utilizing a combination of injection dependent EL imaging of the module, the Dark IV of the module and parameter optimization algorithms to accurately model the PV modules performance by analyzing the cells individually. ...
The mathematical models applied to photovoltaic (PV) modules are typically oversimplified. In general, the models applied to individual PV cells are modified to obtain the “cell” voltage by dividing the module voltage by number of cells in the module. Due to the complexity of the current–voltage (IV) relation and the presence of bypass diodes in commercial PV modules this approach is inappropriate and can lead to incorrect conclusions about the electrical response of a module. This paper provides a method of determining each cell's electrical response using a combination of the Dark IV response of the entire module and injection dependent electroluminescence (EL) imaging. This combination of characterization techniques makes use of equipment that is readily available in commercial facilities as well as regular PV characterization laboratories. This method can be used as an alternative to lock‐in thermography (LIT) based methods, as the equipment required is not always present in standard PV characterization laboratories. In this study, Evolutionary Algorithms are used to optimize the individual cells' electrical parameters. This advanced characterization method can be utilized in commercial facilities, to investigate atypical module electrical response and in research laboratories to investigate the degradation in individual cells within a module.
