Single Diode Photovoltaic Model Research Articles

Hybrid Brown-Bear and Hippopotamus Algorithms with Fractional Order Chaos Maps for Precise Solar PV Model Parameter Estimation

The rise in photovoltaic (PV) energy utilization has led to increased research on its functioning, as its accurate modeling is crucial for system simulations. However, capturing nonlinear current–voltage traits is challenging due to limited data from cells’ datasheets. This paper presents a novel enhanced version of the Brown-Bear Optimization Algorithm (EBOA) for determining the ideal parameters for the circuit model. The presented EBOA incorporates several modifications aimed at improving its searching capabilities. It combines Fractional-order Chaos maps (FC maps), which support the BOA settings to be adjusted in an adaptive manner. Additionally, it integrates key mechanisms from the Hippopotamus Optimization (HO) to strengthen the algorithm’s exploitation potential by leveraging surrounding knowledge for more effective position updates while also improving the balance between global and local search processes. The EBOA was subjected to extensive mathematical validation through the application of benchmark functions to rigorously assess its performance. Also, PV parameter estimation was achieved by combining the EBOA with a Newton–Raphson approach. Numerous module and cell varieties, including RTC France, STP6-120/36, and Photowatt-PWP201, were assessed using double-diode and single-diode PV models. The higher performance of the EBOA was shown by a statistical comparison with many well-known metaheuristic techniques. To illustrate this, the root mean-squared error values achieved by our scheme using (SDM, DDM) for RTC France, STP6-120/36, and PWP201 are as follows: (8.183847 × 10−4, 7.478488 × 10−4), (1.430320 × 10−2, 1.427010 × 10−2), and (2.220075 × 10−3, 2.061273 × 10−3), respectively. The experimental results show that the EBOA works better than alternative techniques in terms of accuracy, consistency, and convergence.

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.

Five parameters extraction of single diode PV model by metaheuristic optimization method by identified built-up data

Precision calculation of unknown photovoltaic (PV) modules or single diode models for PV cell specifications under various environmental conditions is needed to build a sunlight-based PV framework. Installing a PV system requires knowledge of all parameters, modeling, and optimization techniques because PV system analysis and configuration help generate renewable energy. This concept requires accurate modeling and calculation of identified and unknown parameters. The single-diode model is simple and accurate for different mathematical equations. Streamlining calculations requires distinguishing this nonlinear model. The current investigation calculated five unknown parameters and compared them with particle swarm optimization (PSO) and wind-driven optimization (WDO) optimization results. The said approach utilizes MATLAB software, analytical as well as optimization methods, and manufacturing data. The suggested method is simple, fast, and accurate for calculating diode ideality factor (A), output currents (Io), series resistance (Rs), Shunt resistance (Rsh), and photocurrent (Iph).

Optimum sizing of components for photovoltaic maximum power point tracking buck converter

A photovoltaic (PV) maximum power point tracking (MPPT) converter consists of MPPT algorithm and converter. Although there has been a lot of improvement for MPPT algorithm, there is still much to improve for the converter. This paper proposed optimum sizing for the ideal PV MPPT buck converter with resistive and battery loads. The optimum sizing ensures the MPPT converter operates in the continuous current mode (CCM) operation and within the defined output voltage ripple for the specified range of irradiance. These conditions are achieved by deriving the equations of the inductances and capacitances required for the MPPT buck converter. The CCM operation is designed to be closed to the DCM to ensure a small inductance value. The specified output voltage ripple ensures the quality of the output of the MPPT buck converter. The verification of the derivation is done using MATLAB/Simulink circuit simulation. The conventional single diode PV model and perturb and observe (PnO) MPPT is used in the system. The result shows that the range of output resistance is highly affecting the duty cycle range, which leads to MPPT failure if the output resistance is not calculated properly. The converters for both resistive and battery loads can operate in the CCM and close to the DCM operation based on the proposed design. These converters also maintain output voltage ripple factor at 1% or lower, which the proposed derivation follows the specification. In conclusion, the proposed derivation of the MPPT buck converter is accurate.

A Novel Adaptive Filtering Algorithm Based Parameter Estimation Technique for Photovoltaic System

This paper offers a hybrid analytical and estimation based technique to determine the photovoltaic (PV) system parameter in a systematic way. The new model formulation is based on the datasheet parameters under standard test condition and normal operating cell temperature environment, forming the analytical approach's foundation. The estimation part is based on the adaptive filtering algorithm, which shows superiority in estimated parameters compared to existing techniques applied in the photovoltaic system. The proposed approach is made available for a single diode PV model and scaled up aggregated model in extracting the model parameters of two real PV modules in the market, representing the exact <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> characteristics of the manufacturer datasheet. A rigorous comparative analysis is carried out between two adaptive and two optimization based estimations for performance evaluation and recommendation purposes.

A Parameter Estimation Method for a Photovoltaic Power Generation System Based on a Two-Diode Model

This study presents a parameter estimation method that uses an enhanced gray wolf optimizer (EGWO) to optimize the parameters for a two-diode photovoltaic (PV) power generation system. The proposed method consists of three stages. The first stage converts seven parameters for the two-diode model into 17 parameters for different environmental conditions, which provides more precise parameter estimation for the PV model. A PV power generation model is then established to represent the nonlinear relationship between inputs and outputs. The second stage involves a parameter sensitivity analysis and uses the overall effect method to remove the parameters that have smaller effect on the output. The final stage uses an enhanced GWO that is associated with measurement data to optimally estimate the parameters that are selected in the second stage. When the parameters are estimated, the predicted value for the PV power output is calculated for specific values of solar irradiation and module temperature. The proposed method is verified on a 200 kWp PV power generation system. To confirm the feasibility of the proposed method, the parameter estimation before and after optimization are compared, and these results are compared with other optimization algorithms, as well as those for a single-diode PV model.

Analysis of Single-Diode PV Model and Optimized MPPT Model for Different Environmental Conditions

The performance of photovoltaic (PV) systems must be predicted through accurate simulation designs before proceeding to a real-time application to avoid errors. However, predicting the cohesive relationship between current and voltage and estimating the parameters of a single diode model become a perplexing task due to insufficient data in the datasheet of PV panels. This research work presents single-diode solar PV system simulation analysis under different conditions, and the performance is improved by introducing an optimization-based maximum power point tracking (MPPT) strategy. Before simulation, a mathematical model for a single diode and optimization approaches are presented in this research work. Particle swarm optimization (PSO), genetic algorithm (GA), BAT optimization, and grey wolf optimization (GWO) model-based MPPT circuits are designed, and the performances are comparatively analyzed. The simulation results identify the nonlinear relationship between current and voltage and between power and voltage as characteristic curves for different temperature and irradiance values. For maximum power (Pmax), the maximum peak point tracking power and efficiency are analyzed to verify the optimization-based MPPT system. The simulation results demonstrate that the GWO model obtains a maximum tracking efficiency (TE) of 98%, which is much better than that of other optimization techniques.

A novel way of parameter estimation of solar photovoltaic system

PurposeThis paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV.Design/methodology/approachTo extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically.FindingsIn this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature.Originality/valueThe proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper.

Parameter Estimation and Analysis of Photovoltaics through a Hybrid Emperor Penguin Optimisation Approach under Different Environmental Constraints

The estimation of mathematical model parameters of solar photovoltaic (PV) establishes as one of the most prominent problems highlighted in recent researches. Various techniques in the literature employ experimental data and information from the manufacturer’s datasheet for the solution, where precise and highly consistent solution identification is still a multifaceted issue to be addressed. In this paper, a novel Emperor Penguin Optimiser (EPO)-based hybrid approach is inducted for the estimation of single-diode PV model (SDM) parameters that are analysed under diverse sets of environmental constraints. The validity of the proposed method is reflected in the analysis of the operating characteristics namely power–voltage (P–V) and current–voltage (I–V) characteristics of multicrystalline and polycrystalline modules under numerous simulations conditions. In addition, the measured results are compared with the test data and a few speculative strategies developed in the literature and reflect the proposed process with more direct results that reduced the computational cost of the PV parameter extraction problem.

Estimation of Single-Diode Photovoltaic Model Using the Differential Evolution Algorithm with Adaptive Boundaries

This study proposes a calculation methodology that determines the optimal boundary parameters of the single-diode photovoltaic model. It allows the calculation of the single-diode photovoltaic model when no reference parameter boundaries are available. The differential evolution algorithm, integrated with a step-by-step boundary definition module, is used to calculate the optimal parameters of the single-diode photovoltaic model, improving the performance of the classic algorithm compared with other studies. The solution is validated by comparing the results with well-established algorithms described in the state-of-the-art, and by estimating the five important points (cardinal points) of an IV curve, namely short-circuit, maximum power, and open circuit points, using a database composed of 100 solar photovoltaic modules. The results show that an optimal set of parameter boundaries enables the differential evolution algorithm to minimize the error of the estimated cardinal points. Moreover, the proposed calculus methodology is capable of producing high-performance response photovoltaic models for different technologies and rated powers.

Photovoltaic panel parameters determination using two numerical methods

This paper proposes new simple mathematical approach based on the Trust-Region-Dogleg Algorithm (TRDLA) in order to accurately determine the electrical parameters from the experimental current–voltage characteristics of photovoltaic (PV) panels.The simple model for the photovoltaic panel is developed based on the single-diode PV model known as five parameters model incorporating the shunt resistance, the series resistance, the photocurrent, the saturation current and the ideality factor.The parameters extraction method based on the Trust-Region-Dogleg Algorithm includes a new simple analytical equation obtained at the maximum power point. This new established equation does not involve any complex calculation. Furthermore, it makes possible to reduce the number of non-linear equations to be solved in order to determine the parameters of the PV panel.A typical MSX60 polycrystalline silicon solar panel is chosen for simulation in MATLAB/MathWorks environment under standard test conditions (STC).The main results of simulation show a good agreement between the experimental current–voltage characteristics and the theoretical one which is reproduced by considering the five parameters values extracted by using the TRDLA based method or the Least Square Method (LSQM) for the purpose of comparison. This good agreement is identified also by a lowest root mean squared error (RMSE) of 4.5 10−3 obtained for TRDLA method compared to 1.4 10−2 obtained for LSQM, and lowest mean absolute error (MAE) of about 3.5 10−3 for TRDLA method and 1.01 10−2 for LSQM. Thus, the obtained results prove the effectiveness and accuracy of the TRDLA based method. Its accuracy has been proven also by a lower statistical error of root mean square error compared to recent methods reported in the literature.

Non-Iterative Methods for the Extraction of the Single-Diode Model Parameters of Photovoltaic Modules: A Review and Comparative Assessment

The extraction of the photovoltaic (PV) model parameters remains to this day a long-standing and popular research topic. Numerous methods are available in the literature, widely differing in accuracy, complexity, applicability, and their very nature. This paper focuses on the class of non-iterative parameter extraction methods and is limited to the single-diode PV model. These approaches consist of a few straightforward calculation steps that do not involve iterations; they are generally simple and easy to implement but exhibit moderate accuracy. Seventeen such methods are reviewed, implemented, and evaluated on a dataset of more than one million measured I-V curves of six different PV technologies provided by the National Renewable Energy Laboratories (NREL). A comprehensive comparative assessment takes place to evaluate these alternatives in terms of accuracy, robustness, calculation cost, and applicability to different PV technologies. For the first time, the irregularities found in the extracted parameters (negative or complex values) and the execution failures of these methods are recorded and are used as an assessment criterion. This comprehensive and up-to-date literature review will serve as a useful tool for researchers and engineers in selecting the appropriate parameter extraction method for their application.

High Gain Power Generation Based On Hybrid Renewable Energy for AC Load Application

This study presents the distributed energy source based current fed inverter has generated high voltage with less passive elements. The hybrid renewable energy generation such as solar and wind has used. The single stage power generation based switched inverter has combined characteristics of both impedance source inverter and switched boost inverter. In conventional voltage source inverter has less boost ability and high electromagnetic noise immunity to the modified current fed inverter circuit. The single diode photovoltaic model and wind-based energy generation have fed into current fed switched inverter for improving high gain and high power factor. The maximum power point tracking such as incremental conductance algorithm has fast-tracking of solar power and generates continuous power to the inverter circuit. 12V solar power and wind power is designed for dc power production, and the output of dc link capacitor is 118V using current fed switched inverter. The current fed switched inverter is designed and implemented in MATLAB/Simulink environment.

Design of boost converter based on maximum power point resistance for photovoltaic applications

The design of the boost converter for the maximum power point tracking (MPPT) is complex due to the nonlinear characteristics of Photovoltaic (PV) modules. In addition, PV modules are irradiance and temperature dependent, which further increases the complexity of the boost converter design. This paper proposes a new approach that eases the design of the boost converter specifically for MPPT applications. This approach represents the maximum power point of the PV module as resistance to simplify the boost converter design, which specifies the design according to the PV module parameters. The derived equations require nine parameters to determine the inductance, input capacitance, and output capacitance of the MPPT boost converter. In this paper, the single diode PV model and the hill climbing MPPT algorithm have been applied in the simulation using MATLAB/Simulink®. The results show that the simulations of the boost converter followed the desired requirements. This proves that the calculated inductance, input capacitance, and output capacitance using the proposed method are accurate.

Photovoltaic system using Lambert W function-based technique

This article presents a successful simulink model by using the photovoltaic (PV) product along with DC/DC buck converter, lithium ion battery, maximum power point tracker (MPPT) along with loads. The current-voltage (I-V) and power-voltage (P-V) characteristics of a Kyocera KC200GT product is validated using single diode PV model in conjunction with Lambert W function. Fuzzy logic algorithm is incorporated in MPPT for high tracking accuracy at steady state and better performance to the varying atmospheric conditions. The complete model is simulated under two testing scenarios i.e., sunny and cloudy day conditions. Exact analytical solution technique by using Lambert W function is followed by expressing I-V and P-V characteristic of PV module by converting into discrete equations. The proposed Lambert W function based mathematical model is accurate and there is a good agreement between calculated and simulated outputs.

Simple PV Performance Equations Theoretically Well Founded on the Single-Diode Model

There are several photovoltaic (PV) performance models in the literature, but most of them either employ complex and tedious calculations or require additional measurements apart from datasheet information. In this paper, a new set of performance equations to evaluate the short-circuit current, open-circuit voltage, and maximum power point at any operating conditions is introduced. The proposed expressions are simple functions of the irradiance and temperature, while they are generally applicable to any crystalline PV module and require only datasheet information as input data. This is achieved by introducing new formulas to determine the irradiance and temperature coefficients that are not provided in the datasheet, thus avoiding empirical constants or additional measurements. The novelty of the performance equations is their solid theoretical background, as they are in excellent agreement with the single-diode PV model, combined with simple and easy application. The proposed PV model is validated and compared with other methods found in the literature through simulations in MATLAB and outdoor measurements on commercial PV modules.

An improved single-diode model parameters extraction at different operating conditions with a view to modeling a photovoltaic generator: A comparative study

Abstract This paper proposes advanced analytical, numerical and genetic algorithm (GA) approaches for retrieving the parameters of photovoltaic (PV) panel. A comparative study for extracting the five parameters of a single diode PV model is presented. Based on the datasheet values, a numerical based Newton-Raphson algorithm is investigated for solving the current-voltage relation of a single diode solar PV model. To highlight the rigorous performance of our models, a second analytical model is proposed. For improving the accuracy of solar panel parameters, a technique based on GA is established. This approach is based on the problem of research and optimization of the extracted parameters as an objective function. To account for variation in solar radiation and temperature, these models are presented under the reference and real operating conditions. The performances of the proposed algorithms are compared by using MATLAB scripts programming, and the theoretical advantages of GA model were demonstrated. The different models are validated experimentally by various tests of temperature and solar irradiance variation. The experimental results indicate that the GA model has a very satisfactory performance compared with the two other models and it offers good compromise between accuracy and fastness.

Improved control strategy for photovoltaic emulator using resistance comparison method and binary search method

The current Resistance Comparison Method uses a fixed-step size in the iteration process to determine the operating point of the photovoltaic emulator. The weakness of the fixed-step size is the inaccuracy of the produced operating point and that it requires a high number of iterations to converge, which can burden the controller processor. This paper proposed a Resistance Comparison Method with an adaptable step size using the Binary Search Method for the photovoltaic emulator. The advantages of this control strategy are the fast operating point tracking capability, highly accurate operating point, and no readjustment is needed for the controller of the closed-loop converter system (robust). The simulation of the photovoltaic emulator system is conducted in the MATLAB/Simulink® using current-mode controlled buck converter with a Proportional-Integral compensator and a single diode photovoltaic model with a series resistor. The results obtained are compared to the conventional control strategy of the photovoltaic emulator. The proposed control strategy demonstrates an average of iteration convergence of 26 times better and 21 times more accurate compared to the conventional Resistance Comparison Method.

Experimental Parameter Extraction in the Single-Diode Photovoltaic Model via a Reduced-Space Search

The behavior of a photovoltaic (PV) module may be captured via its current–voltage ( $I$ – $V$ ) characteristic. The single-diode model is able to adequately fit this characteristic while featuring limited parameterization difficulty, and is thus widely adopted to represent the performance of a PV module. However, the identification of the model's parameters is a complex task due to the nonconvex nature of the underlying optimization problem. In this paper, an efficient method for the parameter extraction of the single-diode PV model from experimental $I$ – $V$ curves is developed. This method features two advantages with respect to the existing procedures. On the one hand, it is able to find high-quality solutions at a reduced computational complexity. On the other hand, it does not rely on any preliminary data selection and it can thus be fully automated without user intervention. Numerical results obtained for case studies common in the literature and a large-scale repository show its performance. A computer program implementing the proposed methodology is available upon request via an e-mail to all interested researchers.

Battery sizing and rule-based operation of grid-connected photovoltaic-battery system: A case study in Sweden

The optimal components design for grid-connected photovoltaic-battery systems should be determined with consideration of system operation. This study proposes a method to simultaneously optimize the battery capacity and rule-based operation strategy. The investigated photovoltaic-battery system is modeled using single diode photovoltaic model and Improved Shepherd battery model. Three rule-based operation strategies—including the conventional operation strategy, the dynamic price load shifting strategy, and the hybrid operation strategy—are designed and evaluated. The rule-based operation strategies introduce different operation parameters to run the system operation. multi-objective Genetic Algorithm is employed to optimize the decisional variables, including battery capacity and operation parameters, towards maximizing the system’s Self Sufficiency Ratio and Net Present Value. The results indicate that employing battery with the conventional operation strategy is not profitable, although it increases Self Sufficiency Ratio. The dynamic price load shifting strategy has similar performance with the conventional operation strategy because the electricity price variation is not large enough. The proposed hybrid operation strategy outperforms other investigated strategies. When the battery capacity is lower than 72kWh, Self Sufficiency Ratio and Net Present Value increase simultaneously with the battery capacity.