Solar cell parameters identification using hybrid Nelder-Mead and modified particle swarm optimization

Hamid, Nurul Farhana Abdul; Rahim, Nasrudin Abd; Selvaraj, Jeyraj

doi:10.1063/1.4941791

Cited by 84 publications

(31 citation statements)

References 22 publications

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“…Figure (c) shows the measured J – V characteristics of the fabricated TMO/n‐Si back contact solar cells measured under standard test conditions (STC; AM1.5G solar spectrum, 100 mW/cm 2 , 25 °C), and the data have been fitted using the double diode equation . The detailed parameters of the cells including the series resistance R s and the shunt resistance R sh obtained from the fitting results of the double diode equation, and the power conversion efficiency are listed in Table .…”

Section: Resultsmentioning

confidence: 99%

Dopant‐free back contact silicon heterojunction solar cells employing transition metal oxide emitters

Bao

Jia

et al. 2016

Physica Rapid Research Ltrs

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The present study investigates the electrical properties of transition metal oxide (TMO) emitters in dopant‐free n‐Si back contact solar cells by comparing the properties of solar cells employing three TMOs (WOx, MoOx and V2Ox) with varying electrical properties acting as p‐type contacts. The TMOs are found to induce large band bending in n‐Si, which reduces the injection level dependent interfacial recombination speed Seff and contact resistivity ρc. Among the TMO/n‐Si contacts considered, the V2Ox/n‐Si contact achieves the lowest Seff of 138 cm/s and ρc of 0.034 Ω cm2, providing the significant advantages over heavily doped a‐Si:H(p)/n‐Si contacts. The best device performance was achieved by the V2Ox/n‐Si solar cell, demonstrating an efficiency of 16.59% and an open‐circuit voltage of 610 mV relative to solar cells based on MoOx/n‐Si (15.09%, 594 mV) and WOx/n‐Si (12.44%, 539 mV). Furthermore, the present work is the first to employ WOx, V2Ox and Cs2CO3 in back contact solar cells. The fabrication process employed offers great potential for the mass production of back contact solar cells owing to simple, metal mask patterning with high alignment quality and dopant‐free steps conducted at a lower temperature.

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Section: Resultsmentioning

confidence: 99%

Dopant‐free back contact silicon heterojunction solar cells employing transition metal oxide emitters

Bao

Jia

et al. 2016

Physica Rapid Research Ltrs

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“…To verify the performance of the proposed approach and the quality of the obtained results, statistical analyses are carried out to measure the accuracy of the calculated parameters and model suitability. The results obtained are compared with recent techniques such as the Biogeography-Based Optimization algorithm with Mutation strategies (BBO-M) [68], Levenberg-Marquardt algorithm combined with Simulated Annealing (LMSA) [47], Artificial Bee Swarm Optimization algorithm [48], Artificial Bee Colony optimization (ABC) [49], hybrid Nelder-Mead and Modified Particle Swarm Optimization (NM-MPSO) [50], Repaired Adaptive Differential Evolution (RADE) [59], Chaotic Asexual Reproduction Optimization (CARO) [69] for solar cell single and double diodes. For organic flexible hydrogenated amorphous silicon, a-Si:H solar cell will be compared with the Quasi-Newton (Q-N) method and Self-Organizing Migrating Algorithm (SOMA) [54].…”

Section: Optimization Methods Referencementioning

confidence: 99%

“…The solar cell can be modelled by using the single diode model, double diode or multi-diode models. The objective function is defined from Equations (1) and (3), several research papers use different functions, for example, [48][49][50]59,68,69] use the root mean square error (RMSE), [47] use the sum of squared error (SSE). In [55,58] the individual absolute error (IAE) is used and [79] use the mean absolute errors (MAE).…”

Section: Problem Formulationmentioning

confidence: 99%

“…The proposed algorithm is applied first to extract the electrical intrinsic parameters values for single and double diode models of a 57-mm-diameter commercial (RTC France) silicon solar cell under 1000 W/m 2 at 33 • C. The extracted parameters are compared with those found by: Biogeography-Based Optimization algorithm with Mutation strategies (BBO-M) [68], Levenberg-Marquardt algorithm combined with Simulated Annealing (LMSA) [47], Artificial Bee Swarm Optimization algorithm [48], Artificial Bee Colony optimization (ABC) [49], hybrid Nelder-Mead and Modified Particle Swarm Optimization (NM-MPSO) [50], Repaired Adaptive Differential Evolution (RADE) [59], Chaotic Asexual Reproduction Optimization (CARO) [69], and the results for each model are reported in Tables 3 and 4. To confirm the accuracy of the extracted optimal values found by the Firefly algorithm, the calculated currents for the single and double diode model by optimized parameters are summarized in Tables 5 and 6 compared with individual absolute error (IAE).…”

Section: Case 1: Single and Double Diode Model (Rtc France Company)mentioning

confidence: 99%

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Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

et al. 2018

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In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 • C and 1000 W/m 2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 • C and 1000 W/m 2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell.

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“…Eqs. (17) and (18) express the evolution saturation currents of the diodes I 01-2 versus energy band-gap E g and cell temperature [11].…”

Section: Double Diode-based Modelmentioning

confidence: 99%

Static and Dynamic Photovoltaic Cell/Module Parameters Identification

Blaifi¹,

Taghezouit²

2020

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

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The accurate parameters extraction is an important step to obtain a robust PV outputs forecasting for static or dynamic modes. For these aims, several approaches have been proposed for photovoltaic (PV) cell modeling including electrical circuitbased model, empirical models, and non-parametrical models. Moreover, numerous parameter extraction methods have been introduced in the literature depending on the proposed model and the operating mode. These methods can be classified into two main approaches including automatic numerical and analytical approaches. These approaches are commonly applied in the static mode, whereas they can be employed for dynamic parameters extraction. In this chapter, as a first stage, the static parameters extraction for both single and double diodes models is exposed wherein Genetic Algorithm and outdoor measurements are considered for fixed irradiation and temperature. In the second stage, a dynamic parameters extraction is carried out using Levenberg-Marquardt algorithm, where 1 day profile outdoor measurement is considered. After that, the robustness of the proposed approaches is evaluated and the parameters obtained by the static method and that given by the dynamic technique are compared. The test is carried out using 3 days with different weather conditions profiles. The obtained results show that the parameters extraction by dynamic techniques gives satisfactory performances in terms of agreement with the real data. literature based mainly on several approaches including electrical, empirical, and non-parametrical modeling. For the non-parametrical models, two approaches are introduced namely, Artificial Neural Network (ANN) and Neuron-Fuzzy based models. The accuracy and the robustness of these approaches rely strongly on the richness of the training dataset in terms of scenarios. Neuron-Fuzzy techniques has been used to predict cell short-circuit current and open-circuit voltage for the static representation [1]. The ANN-based technique is introduced to model the PV array power for the embedded systems implementation [2]. This technique has been tested for dynamic mode dealing [3]. The ANN technique is also used for the prediction of the PV cell/module voltage directed to amorphous silicon PV technology wherein the obtained network has been tested by real dynamic data [4].In the empirical or the analytical approaches, several models have been proposed to estimate accurately both static and dynamic modes of PV cell/modules. An analytical model is proposed by [5] based on manufacturer characteristic. This model provides acceptable results for both static and dynamic working. Another model has been introduced by Sandia National Laboratory [6], which is widely employed for PV cell/model forecasting especially for the large-scale arrays. Other empirical models have been proposed in order to estimate the PV array power under uniform shading [7,8].In the electrical approaches, two models widely prevail owing to their simplicity based on equivalent circuits namely: single (SDM) and doub...

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Solar cell parameters identification using hybrid Nelder-Mead and modified particle swarm optimization

Cited by 84 publications

References 22 publications

Dopant‐free back contact silicon heterojunction solar cells employing transition metal oxide emitters

Dopant‐free back contact silicon heterojunction solar cells employing transition metal oxide emitters

Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

Static and Dynamic Photovoltaic Cell/Module Parameters Identification

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