Parameter extraction of photovoltaic models using an enhanced Lévy flight bat algorithm

Deotti, Lucas Meirelles Pires; Pereira, J.L.R.; Júnior, Ivo Chaves da Silva

doi:10.1016/j.enconman.2020.113114

Cited by 84 publications

(40 citation statements)

References 56 publications

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“…The new fitness of all coot's followers is estimated and compared with the its leader fitness. If the follower fitness is better than its corresponding leader, the follower became a leader and the leader changed to a follower as in (24). The leader's positions are improved based on random function as in (25), and (26).…”

Section: Cbmo Search Algorithm Proceduresmentioning

confidence: 99%

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

et al. 2021

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In this paper, a comprehensive methodology based on coot bird metahurestic optimizer called CBMO is presented aiming to demonstrate the performance of solar units under various operating and loading scenarios. The CBMO is capable to deal with different complicated behavior in both harmonized or disorderly phases and can define its major parameters with proofed indicators. It can convey from exploration state to exploitation state to realize a fast and better convergence without trapping in local minima. At first stage of this effort, the optimal values of uncertain parameters of well-known benchmarking PV units under study are cropped with compulsory validations and comparisons. It can be mentioned here both single-and doublediode models of such units are utilized, and their unknown parameters are emphasized. Then, a Simulink detailed model is built-up employing the obtained parameters with various types of connected loads such as static resistive load and dynamic DC motor to appraise the operating performance of such units. It is worth mentioning that the performance of solar units under varied sun radiations and cell temperatures are made along the presented work. To sum up, it can be empathized that the CBMO performs well, and its results are very competitive to those available in the literature.

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Section: Cbmo Search Algorithm Proceduresmentioning

confidence: 99%

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

et al. 2021

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“…Furthermore, it is important to note that, although this set of expressions has been developed by King et al [27] based on data that were collected over the years at U.S. Sandia National Laboratories, they also have elements of physical models, such as the thermal voltage and the coefficient c 5 [10]. Interestingly, this latter is associated with a theoretical parameter of physical models, known as "diode ideality factor" and it generally has a value between 1 and 2 for PV cells [20]. Thus, the authors recommend that both this and the other coefficients presented in (7) be determined from specific procedures [27].…”

Section: Voltage-current Empirical Modelsmentioning

confidence: 99%

“…In turn, Silva et al [34] developed another expression to represent the relative efficiency within the scope of the INNDISOL Project, which is based on the empirical model that was proposed by Durisch et al [29]. As presented in reference [8], it can be given by (20).…”

Section: Relative Efficiency-based Empirical Modelsmentioning

confidence: 99%

“…Nevertheless, the number of coefficients presented by these models can be considered to be suitable for the context of PV module performance modeling. For clarity purposes, the most popular physical models have up to seven parameters to be determined [20].…”

Section: Overview Of the Described Modelsmentioning

confidence: 99%

“…In the first step, the parameter values must be determined while considering a reference operating condition [17]. Depending on the context, this task can be accomplished through methods that are based on the PV module datasheet or a I-V curve experimentally obtained at PV module terminals in a known operating condition [17][18][19][20][21]. In turn, in the second step, the parameter values must be determined in view of the required operating conditions [17].…”

Section: Introductionmentioning

confidence: 99%

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Empirical Models Applied to Distributed Energy Resources—An Analysis in the Light of Regulatory Aspects

et al. 2021

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According to the International Electrotechnical Commission (IEC) 61853 standard, the power rating of photovoltaic (PV) modules must be done on a measurement matrix that broadly covers the ranges of operating conditions encountered in the field. These results are becoming more frequent in recent module datasheets. This paper investigates the effectiveness of applying existing empirical PV performance models while using a matrix with 18 operational records, similar to the 22 that were recommended by IEC 61853, as an alternative to the thousands of records that are conventionally used to determine their coefficients. A review of fifteen empirical models is presented and the procedures for determining their coefficients are discussed. In order to validate them, they were applied to data from fourteen PV modules, which remained installed outdoors for about one year, in three locations with distinct climate types. Although the uncertainties that were obtained with the proposed approach, as compared to the conventional one, are about 1% higher for xSi and CdTe modules, and somewhat higher for mSi and CIGS modules, the total uncertainties were only around 5%, a value that is quite adequate for evaluating module performance. Moreover, these uncertainties were from two to five times smaller than those that were obtained by the method that was recommended by IEC 61853 for this purpose.

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Accurate PEM fuel cells parameters estimation using hybrid artificial bee colony differential evolution shuffled complex optimizer

Hachana

2021

Intl J of Energy Research

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Summary To get efficient current/voltage (I/V) polarization curves, the parameter identification of the proton exchange membrane (PEM) fuel cells (FCs) model based on experimental datasets and meta‐heuristic algorithms remains an active research field during the past few years. Meanwhile, estimating those parameters accurately is still a challenge. In this work, a new hybridized approach is presented to identify the PEMFC model parameters denominated the artificial bee colony differential evolution shuffled complex (ABCDESC) optimizer. In the proposed algorithm, the double execution of the probabilities evaluation and the selection strategy of ABC optimizer enables to have better exploitation phase without getting stuck into the local optimum. The sum of squared errors (SSE)‐based objective function is used to perform the optimization as it is the well used in the literature. In order to assess this new hybridized approach, a comparative study with the latest published techniques is carried out using six typical test benchmarking PEMFCs modules extensively utilized in the literature. In this context, the reached SSE values and the standard deviations among other challenging methodologies are very competitive with the best convergence speed and reduced number of function evaluations. The ABCDESC algorithm reaches a standard deviation/CPU run time (STD/CRT) of 8.4690e−16/0.298 second, 1.3275e−16/0.290 second, 1.9721e−14/0.356 second, 4.3889e−15/0.343 second, 1.4388e−12/0.49 second, and 1.6398e−17/0.326 second for 250 W, BCS 500 W, NedStack PS6, Ballard Mark V, Horizon H‐12, and Modular SR‐12 stacks; respectively. The comparison results indicate the successful use of the proposed ABCDESC optimizer to characterize the PEMFC model accurately.

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Parameter extraction of photovoltaic models using an enhanced Lévy flight bat algorithm

Cited by 84 publications

References 56 publications

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

Empirical Models Applied to Distributed Energy Resources—An Analysis in the Light of Regulatory Aspects

Accurate PEM fuel cells parameters estimation using hybrid artificial bee colony differential evolution shuffled complex optimizer

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