Model Of Photovoltaic Module Research Articles

An efficient, fast, and robust algorithm for single diode model parameters estimation of photovoltaic solar cells

AbstractParameter estimation of photovoltaic (PV) solar cells and module models pays attention to researchers owing to their importance in practical considerations. The single diode model (SDM) circuit with five unknown parameters is widely used to model PV solar cells and modules. In this paper, a novel approach called alternate optimization (AO) algorithm based on a discrete search is proposed to estimate the SDM parameters. The proposed algorithm provides efficient and robust performance, considering a limited set of discrete values and increasing the convergence speed. Two practical case studies with actual measurements are considered to assess the proposed AO algorithm: the RTC France solar cell and monocrystalline PV modules with different irradiations and temperatures. The numerical findings underscore the superior performance of the proposed AO algorithm across various metrics. Notably, it achieves an exceptional Root Mean Square Error (RMSE) of 7.7426 × 10−04 for the RTC France PV cell and approximately 1 × 10−03 RMSE for monocrystalline PV modules. Additionally, the algorithm exhibits unparalleled speed, showcasing the fastest convergence with an elapsed time of 1.66 × 10−05—markedly 4.45 times quicker than the fastest method documented in the literature for SDM parameter estimation. Furthermore, the proposed AO algorithm stands out for its efficiency, requiring a maximum of five iterations for parameter estimation, a substantial improvement compared to the more than 10 iterations typically needed by algorithms in the existing literature. Its robustness is also commendable, as evidenced by the stability of final RMSE values across a variety of experiments, distinguishing it from less robust algorithms found in the literature.

Development and improvement of a transient temperature model of PV modules: Concept of trailing data

AbstractThe development of a transient temperature model of photovoltaic (PV) modules is presented in this paper. Currently, there are a few steady‐state temperature models targeted at assessing and predicting the PV module temperature. One of the most commonly used models is the Faiman thermal model. This model is derived from the modified Hottel‐Whillier‐Bliss (HWB) model for flat‐plate solar‐thermal collector under steady‐state conditions and assumes low or no thermal mass in the modules (i.e., short time constants such that transients are neglected, and steady‐state conditions are assumed). The transient extension of the Faiman model we present in this paper introduces a thermal mass, which provides two advantages. First of all, it improves the temperature prediction under dynamic conditions. Second, our transient extension to the Faiman model allows the accurate parametrization of the Faiman model under dynamic conditions. We present our model and parametrization method. Furthermore, we applied the model and parametrization method to a 1‐year data set with 5‐min resolved outdoor module measurements. We demonstrate a significant improvement in temperature prediction for the transient model, especially under dynamic conditions.

Partially shaded photovoltaic panel

This paper deals with a model of partially shaded photovoltaic module. It is build as an electric circuit consisting of individual solar cells and bypass diodes. The parameters and operating points of individual solar cells change depending on the solar cell temperature and the level of solar irradiance. The output characteristics of the photovoltaic module model are determined numerically by finding solutions for the given electrical circuit, considering solar irradiance on the surface of each individual solar cell. The proposed model can provide realistic behaviour of partially shaded photovoltaic module required in simulation tools used in the development of a micro inverter. The model was confirmed by measurements performed on the tested photovoltaic module.

Parameter extraction of photovoltaic model based on butterfly optimization algorithm with chaos learning strategy

Accurate extraction of photovoltaic (PV) model parameter is significant for the evaluation of PV system. Most of existing methods for extracting the PV parameters suffer from low extraction accuracy and are prone to fall into local optima. To solve these shortcomings, a chaos learning butterfly optimization algorithm (CLBOA) is proposed. First, the introduced Cauchy mutation increases the perturbation to butterflies and helps the algorithm to jump out of local optima. Second, a chaos learning strategy is proposed to lead the butterflies within the population to learn from the optimal individual using the improved Tent chaos mapping, which strengthens the learning exchange of the population, enhances the ability of global exploration and local exploitation, and optimizes the convergence speed and accuracy. Third, the three worst individual positions were randomly initialized using a terminal elimination mechanism to increase population diversity. After evaluating CLBOA through the CEC2022 benchmark suite, it was used to extract parameters for a total of 12 PV cell and module models for 5 materials. Compared with 9 well-known algorithms, CLBOA performs excellent in terms of convergence performance, parameters extraction accuracy, running time and improvement index. Finally, CLBOA was applied on YL PV power station model of Guizhou Power Grid in China under different irradiations and temperatures. The I-V and P-V curves reconstructed by CLBOA are highly fit to the synthesized curves, and the individual absolute error (IAE) and relative error (RE) also confirm the satisfactory accuracy of parameters extraction. Comprehensive analysis shows that CLBOA can extract parameters better than the comparison algorithms, demonstrating its strong potential for PV parameters extraction.

In-depth analysis of photovoltaic module parameter estimation

Accurate knowledge of photovoltaic (PV) module model parameters plays an important role in PV power generation system. Therefore, in this study, the single-diode model of PV modules, physical meaning and solution methods of various parameters were introduced firstly. Then, the influence of various parameters on I–V characteristics was analyzed, and the parameter sensitivities of various parameters to the open circuit voltage and short circuit current were quantitatively discussed. Finally, different types of PV modules in a public dataset were used as objects, and different algorithms were employed to estimate the value of each parameter. Through simulation, some important points were provided: (ⅰ) the influence of photocurrent and parallel resistance on I–V characteristics is relatively independent, while the influence of reverse saturation current, series resistance, and ideality factor on I–V characteristics is highly coupled; (ii) the system degrees of freedom is totally different in case of considering the reverse saturation current as a dependent and independent variable. The improvement of the system degrees of freedom remarkably reduces the root mean square error of I–V characteristics; (iii) the estimated parameter resulted from meta-heuristic algorithms do not have the characteristic of uniqueness and may not represent the true values of the parameters.

A MIC-LSTM based parameter extraction method for single-diode PV model

In recent years, the installed capacity of renewable energy systems has seen rapid growth, particularly in photovoltaic (PV) power. Photovoltaic modules, being the fundamental elements of the PV system, play a crucial role in determining system performance. However, the challenge arises from the inconsistent decay rates of PV modules, which significantly impact the accuracy of PV system modeling. To address this issue, this paper introduces a novel MIC-LSTM based parameter extraction method for the single-diode PV model. This method focuses on accurately deriving PV module model parameters under various decay rates. By establishing a mapping relationship between the current-voltage (I-V) curve characteristics and the five unknown parameters in the photovoltaic module model, the proposed method demonstrates high precision in parameter extraction. Simulation and experimental verifications are carried out to validate the proposed method, where the extraction accuracy is 99.3%, 98.39%, 98.85%, 97.91%, and 98.36% for the five unknown model parameters.

A Contribution of a Computer Tool Using ATP-EMTP TACS to the Modeling of a Photovoltaic (PV) Module

The access to electrical distribution systems with the use of solar photovoltaic's is becoming a viable alternative for the provision of generation through alternative sources of electricity. However, many studies of power quality and transient stability need to be made to meet the technical requirements of the network. Therefore, this article deals with the modeling of photovoltaic module in ATP-EMTP tool called TACS for future studies of the insertion of the PV system in medium and low voltage. To validate this model comparisons of the simulations with the data provided by the manufacturer, thus validating the developed model were performed.

Simulink model of a bifacial PV module based on the manufacturer datasheet

The paper proposes an update of a mathematical model of a Photo-Voltaic (PV) module, considering the possibility that also the rear face of the PV module produces energy. The proposed approach allows to write a new descriptive equation, whose terms are function of the information always available in the modern datasheet of the PV module’s manufacturers. This implies that no pre-processing of the datasheet’s parameters is needed to use the proposed model, whichever are the solar irradiance and the cell/module temperature. The model considers the total solar radiation (front and back side), such that the produced energy increases. Simulink environment is used to calculate the total solar radiation.

Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm

AbstractAccurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells.

Wind Velocity and Forced Heat Transfer Model for Photovoltaic Module

This study proposes a computational model to define the wind velocity of the environment on the photovoltaic (PV) module via heat transfer concepts. The effect of the wind velocity and PV module is mostly considered a cooling effect. However, cooling and controlling the PV module temperature leads to the capability to optimize the PV module efficiency. The present study applied a nominal operating cell temperature (NOCT) condition of the PV module as a reference condition to determine the wind velocity and PV module temperature. The obtained model has been examined in contrast to the experimental heat transfer equation and outdoor PV module performance. The results display a remarkable matching of the model with experiments. The model’s novelty defines the PV module temperature in relation to the wind speed, PV module size, and various ambient temperatures that were not included in previous studies. The suggested model could be used in PV module test specification and provide analytical evaluation.

Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm

Given the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter extraction presents a challenging problem. This challenge is exacerbated by the propensity of conventional algorithms to get trapped in local optima due to the complex nature of the problem. Accurate parameter estimation, nonetheless, is crucial due to its significant impact on the PV system’s performance, influencing both current and energy production. While traditional methods have provided reasonable results for PV model variables, they often require extensive computational resources, which impacts precision and robustness and results in many fitness evaluations. To address this problem, this paper presents an improved algorithm for PV parameter extraction, leveraging the opposition-based exponential distribution optimizer (OBEDO). The OBEDO method, equipped with opposition-based learning, provides an enhanced exploration capability and efficient exploitation of the search space, helping to mitigate the risk of entrapment in local optima. The proposed OBEDO algorithm is rigorously verified against state-of-the-art algorithms across various PV models, including single-diode, double-diode, three-diode, and photovoltaic module models. Practical and statistical results reveal that the OBEDO performs better than other algorithms in estimating parameters, demonstrating superior convergence speed, reliability, and accuracy. Moreover, the performance of the proposed algorithm is assessed using several case studies, further reinforcing its effectiveness. Therefore, the OBEDO, with its advantages in terms of computational efficiency and robustness, emerges as a promising solution for photovoltaic model parameter identification, making a significant contribution to enhancing the performance of PV systems.

A comprehensive performance evaluation of bifacial photovoltaic modules: insights from a year-long experimental study conducted in the Canadian climate

Bifacial photovoltaic (PV) modules, capable of capturing solar energy from both sides of the cells, are becoming increasingly popular as their manufacturing costs approach those of traditional monofacial modules. Accurate estimation of their power generation capacity is essential for optimizing their use. This study evaluates a power production model for bifacial PV modules using local irradiance data from Razon+ in Sherbrooke, Canada, and Solcast irradiance data derived from satellite imagery and weather models. The model's performance was assessed throughout the year, with particular attention to the impact of snow coverage during winter. To address computational efficiency, the study evaluated ray tracing and a 2D view factor model, selecting the more time-efficient method. Experimental validation showed that, using local irradiance data, the model achieved Normalized Root Mean Square Errors (NRMSE) of 18.77%, 4.94%, 3.93%, and 6.22% for winter, spring, summer, and fall, respectively. With Solcast data, the NRMSEs were 22.76%, 15.32%, 14.72%, and 17.78% for the corresponding seasons. While the model performed satisfactorily in spring, summer, and fall, it was less accurate in winter. To enhance winter accuracy, the model incorporated snow coverage, using snow depth as a metric to detect snow on the front surface. This adjustment improved the accuracy by 51.1%.

EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems

The butterfly optimization algorithm (BOA) is a meta-heuristic algorithm that mimics foraging and mating behavior of butterflies. In order to alleviate the problems of slow convergence, local optimum and lack of population diversity of BOA, an enhanced adaptive butterfly optimization algorithm (EABOA) is proposed in this paper. First, a new adaptive fragrance model is designed, which provided a finer fragrance perception way and effectively enhanced the convergence speed and accuracy. Second, Lévy flight with high-frequency short-step jumping and low-frequency long-step walking is adopted to help the algorithm jump out of the local optimum. Third, the dimension learning-based hunting is employed to enhance information exchange by creating neighbors for each butterfly, thus improving the balance between local and global search and maintaining population diversity. In addition, the Fitness-Distance-Constraint (FDC) method is introduced to enhance constraint handling in EABOA (named FDC-EABOA). The proposed EABOA is compared with 8 well-known algorithms and 8 BOA variants in CEC 2022 test suite and the results were statistically analyzed using Friedman, Friedman aligned rank, Wilcoxon signed rank, Quade rank and multiple comparisons, analysis of variance (ANOVA) and range analysis. Finally, EABOA and FDC-EABOA are applied to seven engineering problems (parameter identification of photovoltaic module model, speed reducer design, tension/compression spring design, pressure vessel design, gear train design, welded beam design, SOPWM for 3-level inverters), and metrics such as Improvement Index (IF) and Mean Constraint Violation (MV) confirm that the proposed algorithms are satisfactory. Experimental results and statistical analysis show that the proposed algorithms outperform the comparison algorithms and demonstrate the strong potential for solving numerical optimization and engineering design problems.

A novel method for modeling of photovoltaic modules based on arithmetic optimization algorithm and cuckoo search

A quick, precise, and dependable parameters obtaining of photovoltaic is an important task to evaluate, simulate, and control photovoltaic systems. Accordingly, several numerical, analytical, and meta-heuristic approaches were proposed for the purpose of the estimation of the photovoltaic models’ parameters according to data obtained experimentally. A novel method depending on the hybridization of arithmetic optimization algorithm along with cuckoo search (AOACS) is utilized in the present work for obtaining parameters related to single-diode PV module depending on data obtained experimentally according to several climate conditions. The reliability of the newly introduced AOACS to maintain equilibrium between exploitation and exploration throughout the evolution process is one of its main benefits. The findings exhibit the AOACS beats some approaches that have been widely published in previous studies in terms of preciseness, steadiness, as well as the rate of convergence under diverse statistical criteria. The proposed method exhibits root mean square error (RMSE) and average absolute error (AAE) values lower than alternative approaches by about 72% and 50%, respectively.

Parameter Extraction of Photovoltaic Models by Honey Badger algorithm and Wild Horse Optimizer

Analyzing the processes ranging from the determination of the installation configuration of the photovoltaic (PV) systems to the operation at the maximum power, from the technical and economic feasibility study to the positive contribution to the region where the production is planned are just possible with the accurate and efficient simulation models of the PV systems. PV parameter extraction, which is a topic frequently discussed recently, is crucial for the detailed modeling of PV cells and modules and simulating the behavior of these systems. For this reason, the current study examined PV parameter extraction and solved this optimization problem with the honey badger algorithm (HBA) and wild horse optimizer (WHO). PV cells and modules were modeled with the single diode model (SDM) and double diode model (DDM) and tested with actual measurement data. The root-mean-square error (RMSE) was chosen as the objective function, and the results were compared with the evaluation metrics for computational accuracy and time. Based on four PV model results, RMSE values were calculated between 9.9318E-04 to 1.7011E-03 for HBA and between 9.8602E-04 and 1.7298E-03 for WHO. As a result, even though both algorithms produce successful, stable, and fast results in PV parameter extraction, the WHO yielded better results.

Implementation of Intelligent Controller for Maximum Power Point Tracking Control of Solar Power Generation System

This paper presents the improved model of solar photovoltaic module and back propagation neural network based maximum power point tracking (MPPT) for boost converter in a standalone photovoltaic system under variable temperature and insulation in static and dynamic conditions. Solar panel is a power source having nonlinear internal resistance. As the intensity of light falling on the panel varies, its voltage as well as its internal resistance both varies. To extract maximum power from the panel, the load resistance should be equal to the internal resistance of the panel. Maximum power point trackers are used to operate a photovoltaic panel at its maximum power point in order to increase the system efficiency. This is done, with the aid of MATLAB and Artificial Neural Network (ANN). Maximum Power Point Tracking (MPPT) algorithms is important in PV systems becauseit reduces the PV array cost by reducing the number of PV panels required to achieve the desired outputpower. This paper presents à comparative simulation study of two important MPPT algorithms specifically perturb and observe and incremental conductance. These algorithms are widely used because of its low-cost and ease of realization. Some important parameters such as voltage, current and power output for each different combination have been traced for both algorithms. KEYWORDS: :- Maximum Power Point Tracking (MPPT), Solar Photovoltaic system (SPV), ANN, SNN, DNN, Boost converter, MATLAB.

Mathematical models to forecast temporal variations of power law shape parameters of a PV module working in real weather conditions: Prediction of maximum power and comparison with single-diode model

In this study, an innovative approach has been proposed to generate real time current–voltage characteristics and forecast peak power of photovoltaic (PV) modules working outdoors beneath real meteorological conditions. Power-Law Model (PLM) including four unknown parameters, two PV metrics (ISC, VOC), and two profile parameters (γ, m), has been used for fast, accurate and reliable modelling of PV modules. Explicit expressions giving γ and m parameters as functions of cardinal point coordinates (IMPP,VMPP, ISC and VOC) have been established. A daily monitoring of PV quantities, throughout one reference day, has been used to derive novel mathematical models of γ and m depending on module temperature and solar irradiance. To describe PV module behavior under changing meteorological conditions during an arbitrary day, mathematical models have been called to reproduce dynamical values of PLM shape parameters γ and m. Reliability of the approach has been assessed by comparing current–voltage characteristics and maximum power coming from PLM and Single-Diode Model (SDM) to experimental data of twenty-two PV modules measured at NREL. Comparison also reveals that I-V characteristics generated and predicted separately by PLM and SDM parameters are very close. In some cases, I-V characteristics generated by PLM parameters are more accurate. Furthermore, NRMSE values for generated and predicted I-V characteristics for all kinds of PV modules evaluated for different working conditions do not exceed 4% for both PLM and SDM, and determination coefficient values for long-term prediction of maximum power are close to one and annual average value is greater than 0,95.

A Comparative Study on Accurate Parameter Estimation of Solar Photovoltaic Models Using Metaheuristic Optimization Algorithms

Fossil fuel prices are increasing day by day due to growing demand and resource limitations. This situation shifts conventional electricity generation to renewable power generation. Among renewables, solar photovoltaic (PV) energy is very promising as the third fastest growing one. However, PV system modeling requires the estimation of unknown parameters of PV cells and modules. This problem remains a compelling task due to its multi-dimensional, multi-model and non-linear characteristics. In order to contribute to the solution of this optimization problem, this work makes a detailed comparison of artificial hummingbird algorithm, artificial rabbits optimization, enhanced Jaya algorithm, flow direction algorithm and artificial gorilla troops optimizer for determining the unknown parameters of PV models. Experimental results demonstrate that artificial hummingbird algorithm in single diode modeling of PV cell, flow direction algorithm in double diode modeling of PV cell and artificial rabbits optimization in single diode modeling of PV module are found to be capable of estimating accurate and efficient design coefficients for PV systems. In addition, the mentioned metaheuristic algorithms show reasonable convergence characteristics for the corresponding PV models.

A novel hybrid method for modeling of photovoltaic module I–V characteristic curve by using artificial intelligence-based solver and multi-criteria decision making

In this research, hybrid method is proposed to model the I–V characteristic curve of a photovoltaic (PV) module. The method is represented by a multi-objective arithmetic optimization and cuckoo search with multi-criteria decision-making approach. The proposed model generates first a number of I–V curves as candidates. This phase is conducted through multi-objective optimization algorithm. The optimization algorithm is assessed by a non-dominated ranking scheme and crowding distance framework. After that, the best I–V curve candidate is chosen from the result of Pareto front by using the VIKOR multi-criteria decision-making method. Moreover, the analytic hierarchy approach is employed to select the appropriate weight for each criterion. The proposed method is validated by using an experimental data under various operational conditions. This validation is done by extracting different I–V characteristic for PV modules. The proposed method is compared to a number of methods in the literature. Results show that the proposed method exceeds other methods in the literature considering the accuracy of generating the I–V curves. In addition, results show that the proposed method requires less computational power as compared to other hybridized methods.

A simplified three-diode model for photovoltaic module: cell modeling and performance analysis

Purpose Estimation of solar cell parameters, mathematical modeling and the actual performance analysis of photovoltaic (PV) cells at various ecological conditions are very important in the design and analysis of maximum power point trackers and power converters. This study aims to propose the analysis and modeling of a simplified three-diode model based on the manufacturer’s performance data. Design/methodology/approach A novel technique is presented to evaluate the PV cell constraints and simplify the existing equation using analytical and iterative methods. To examine the current equation, this study focuses on three crucial operational points: open circuit, short circuit and maximum operating points. The number of parameters needed to estimate these built-in models is decreased from nine to five by an effective iteration method, considerably reducing computational requirements. Findings The proposed model, in contrast to the previous complex nine-parameter three-diode model, simplifies the modeling and analysis process by requiring only five parameters. To ensure the reliability and accuracy of this proposed model, its results were carefully compared with datasheet values under standard test conditions (STC). This model was implemented using MATLAB/Simulink and validated using a polycrystalline solar cell under STC conditions. Originality/value The proposed three-diode model clearly outperforms the earlier existing two-diode model in terms of accuracy and performance, especially in lower irradiance settings, according to the results and comparison analysis.