Simulation Of Photovoltaic Modules Research Articles

Rapid parameter identification of three diode photovoltaic systems using the Cheetah optimizer

This study focuses on accurate parameter identification for solar cells and photovoltaic module simulation using experimental data. To tackle the challenge of modelling these highly nonlinear systems, we propose the novel use of the Cheetah Optimizer (CO) algorithm, inspired by cheetah hunting strategies. The CO algorithm employs mathematical models and randomization parameters to balance exploration and exploitation, avoiding local optima by considering energy limitations. We demonstrate the CO algorithm's effectiveness by applying it to the three-diode model in solar photovoltaic systems, specifically the STP6-120/36 and Photowatt-PWP201 PV modules. Impressively, the CO algorithm achieves remarkably low root mean square error values of 0.0145 A and 0.0019 A, outperforming state-of-the-art methods and ensuring high accuracy. Additionally, it delivers the lowest power errors of 0.16054 W and 0.01484 W for the respective modules, highlighting its exceptional performance. The CO algorithm proves to be a promising tool for precise parameter extraction and optimization, leading to improved modelling and performance of solar photovoltaic systems.

Modeling and Simulation of Photovoltaic Modules Using Bio-Inspired Algorithms

This research aims to employ and qualify the bio-inspired algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution Algorithm (DE) in the extraction of the parameters of the circuit equivalent to a photovoltaic module in the models of a diode and five parameters (1D5P) and two diodes and seven parameters (2D7P) in order to simulate the I-V characteristics curves for any irradiation and temperature scenarios. The peculiarity of this study stands in the exclusive use of information present in the module’s datasheet to carry out the full extraction and simulation process without depending on external sources of data or experimental data. To validate the methods, a comparison was made between the data obtained by the simulations with data from the module manufacturer in different scenarios of irradiation and temperature. The algorithm bound to the model with the highest accuracy was DE 1D5P, with a maximum relative error of 0.4% in conditions close to the reference and 3.61% for scenarios far from the reference. On the other hand, the algorithm that obtained the worst result in extracting parameters was the GA in the 2D7P model, which presented a maximum relative error of 9.59% in conditions far from the reference.

Hourly forecasting of the photovoltaic electricity at any latitude using a network of artificial neural networks

Nowadays, special attention is paid to the importance of using photovoltaic (PV) systems to tackle the problem of climate change and the energy crisis. Artificial intelligence is currently used in different science fields for its great potential and accuracy in forecasting problems.In this work, a network of artificial neural networks (ANNs) was trained and validated to forecast the hourly worldwide electrical power produced by various PV modules, with different electrical characteristics. Each ANN describes the worldwide performance of each PV module on the optimal inclination angle. The training data consists of the hourly air temperature, horizontal total solar radiation as input data and electrical power produced as output. The power is obtained from the hourly simulation of PV modules with an electrical circuit model in 24 localities at very different latitudes.The validation and generalization of the network were obtained by considering the six PV modules in further 24 localities and by considering two further PV modules in all 48 localities considered.The excellent results in terms of accuracy metrics confirmed that the network of ANNs is a reliable, simple and accurate tool that can be used to predict the hourly performance of any PV module in any location worldwide.

Modeling, Simulation, and Experimental Validation of a Novel MPPT for Hybrid Renewable Sources Integrated with UPQC: An Application of Jellyfish Search Optimizer

Changes in climatic circumstances, as well as intermittency, which has a significant impact on the overall energy system output from renewable energy sources (RESs), require the development of control strategies for extracting the maximum power available from RESs. To accomplish this task, several techniques have been developed. An efficient maximum power point tracking (MPPT) technique should be utilized to guarantee that both wind-generation and PV-generation systems provide their full advantages. In this paper, a new MPPT approach with jellyfish search optimization (JSO) is developed; in addition, a unified power-quality conditioner (UPQC) is utilized to enhance the performance of the microgrid (MG) and to solve the power-quality issues for the sensitive load. The MG, which includes a photovoltaic (PV), a wind turbine, and a fuel cell battery, is examined and modeled for uniform and nonuniform wind speed and solar irradiance. A comparison between the developed algorithm and different maximum power tracking algorithms is presented. Additionally, four case studies are carried out to verify the effectiveness of the introduced UPQC in enhancing power-quality problems. The research outcome shows high performance from the developed algorithm when assessed with additional algorithms. MATLAB/Simulink software is utilized for the simulation of the wind, PV, and FC control systems. However, experiment validation tests are given under the same condition of PV irradiation to validate the simulation results. The experimental validation is executed by utilizing the PV module simulation model, threefold, 23 V/2A CO3208-1A with solar altitude emulator CO3208-1B board, and the results are compared to the simulation results.

Maximum Power Tracking of Photovoltaic Modules Based on Fuzzy Logic Employing Four-Phase Intervaled Boost Converter

Currently, there are significant investments in the study of distributed generation, including solar energy by the photovoltaic conversion method. Basically, a cell directly converts solar energy to electricity. For this, static converters are required. However, relevant issues arise in this process: point of maximum efficiency of module generation, strategy of control of the flow of energy to the network. The aim of this work is to monitor the main variables of a photovoltaic system, specifically the voltage and current module and their derivates. The goal is to implement the maximum power tracking technique using Fuzzy logic. In addition, the energy provided by the cell will be employed in an inverter stage that can operate as an active filter, voltage regulator, or generator of reactive and active power. The feasibility of using Fuzzy logic will also be studied. The first stage of this work involves parameterization and simulation of photovoltaic modules. The initial study examines the compatibility of a commercial module and its catalog data with the results of simulation. The simulated I-V characteristics show almost identical results to the catalog data. In sequence, a boost or lift DC-DC converter is employed to emulate variable load for maximum power transfer.

Thermal effect on curved photovoltaic panels: Model validation and application in the Tabuk region.

This paper aimed to investigate the temperature effect on photovoltaic (PV) cell parameters. The PV cell parameters such as series and parallel resistances, diode ideality factor, and diode saturation current, are not considered in the reported stepwise modeling. The present work aims to improve available models used in the modeling and simulation of PV modules to support the researcher and power project developer. All the required temperature-dependent parameters are determined to model the simulated PV module with high accuracy using Simulink/MATLAB software. To validate the method, a 36-cell-50W solar panel with different radii of curvature is set up to assess solar power outputs under varying irradiance and temperature conditions. For the present application, the Tabuk region (Saudi Arabia) is chosen based on its location and climatic conditions. The method provided conformity to the measured power outputs for varying Global Horizontal Irradiance (GHI) and temperature conditions. The maximum power output of the PV module increases from 14.4 W to 25.8 W when the received solar power density varies from 307 W/m2 to 526 W/m2 depending on the level of curvature starting from a semi-cylindrical shape to a vaulted shape to a flat shape. The curved PV module shows slightly higher power variation with temperature as compared to the flat one. Above 25°C, the power output is about 20% less at a maximum temperature of 65°C. When the temperature drops below 25°C, the power outputs increase about 6% and 11.5% for corresponding temperatures of 15°C and 5°C, respectively.

Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja

This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) are the techniques used to forecast the outputs of the PV. Various types of activation function will be applied such as Linear, Logistic Sigmoid, Hyperbolic Tangent Sigmoid and Gaussian. The simulation results show that the Logistic Sigmoid is the best technique which produce minimal root mean square error for the system.

An Adaptive Sensorless Control Technique for a Flyback-Type Solar Tile Microinverter

This article presents a novel sensorless control system for a solar tile microinverter employing the flyback topology. The elimination of current sensors from the circuit lowers the overall cost and reduces measurement noise introduced into the control system. The proposed control system eliminates both the high frequency inductor current measurement as well as the photovoltaic (PV) module current measurement. Additionally, an inductance observer is combined with the proposed technique to increase the accuracy of the high frequency inductor current estimation. A stability analysis proves the system to be stable. The proposed control system is experimentally validated on a microinverter prototype. Simulation and experimental results affirm the feasibility and performance of the control system. The experimental setup includes a grid simulator and a PV module simulator to test the system at varying irradiance levels. The proposed control system is also compared to methods presented in previous literature to demonstrate its superior characteristics.

MPPT Charge Controller Design in a Solar PV System Under Rapidly Changing Climate Condition

This paper presents a detailed theoretical study of photovoltaic (PV) systems and their operation using the MPPT (Maximum Power Point Tracking) method and presents the simulation of photovoltaic modules validated by computer software simulation followed by an experimental setup of MATLAB R2017a. The first approach to build the performance of a photovoltaic solar panel is to use a maximum power point tracker in rapidly changing climatic conditions and use a DC-DC converter to maximize the output power. This framework can operate at the maximum power point MPP and produces its highest power in different irradiance conditions when the solar panels are partially shaded. The main perspectivesis design and simulation of a simple but efficient charge controller by utilizing maximum power point tracker for photovoltaic system and analysis results show that this MPPT system with perturb & observe (P&O) method and the DCDC Boost converter can significantly increase the efficiency and the performance of PV.

Mechanical Models and Finite-Element Approaches for the Structural Analysis of Photovoltaic Composite Structures: a Comparative Study

In general, photovoltaic composite structures are three-layer laminates with a thin soft core layer. Due to the high contrast between the mechanical properties of skin and core layers, such structures have been studied by different theories. Finite-element models, continuum-based theories, and two-dimensional plate/shell theories are used in the analysis of laminated structures. The present study deals with the modeling and computational simulation of photovoltaic modules in the context of global structural mechanics. The focus is on the implementation of different elements in both two- and three-dimensional approaches to find the most efficient one for analyzing photovoltaic composite structures.

Passive Thermal Management of Photovoltaic Modules—Mathematical Modeling and Simulation of Photovoltaic Modules

Operation of solar photovoltaic (PV) systems under high temperatures and high humidity represents one of the major challenges to guarantee higher system’s performance and reliability. The PV conversion efficiency degrades considerably at higher temperatures, while dust accumulation on PV module together with atmospheric water vapor condensation may cause a thick layer of mud that is difficult to be removed. Therefore, thermal management in hot climates is crucial for reliable application of PV systems to prevent the efficiency to drop due to temperature rise. This research focuses on the utilization of phase-change materials (PCM) for passive thermal management of solar systems. The main focus is to explore the effect of utilization of PCM-based cooling elements on the thermal behavior of solar PV modules. This paper presents the mathematical modeling and validation of PV modules. Both simulation and experimental data showed that the significant increase in PV peak temperature in summer affects the module’s efficiency, and consequently produced power, by 3% compared to standard testing condition (STC) as an average over the entire day, while it goes up to 8% and 10% during peak noon hours in winter and summer, respectively.

An Improved Generalized Method for Evaluation of Parameters, Modeling, and Simulation of Photovoltaic Modules

This paper proposes an improved generalized method for evaluation of parameters, modeling, and simulation of photovoltaic modules. A new concept “Level of Improvement” has been proposed for evaluating unknown parameters of the nonlinear I-V equation of the single-diode model of PV module at any environmental condition, taking the manufacturer-specified data at Standard Test Conditions as inputs. The main contribution of the new concept is the improvement in the accuracy of values of evaluated parameters up to various levels and is based on mathematical equations of PV modules. The proposed evaluating method is implemented by MATLAB programming and, for demonstration, by using the values of parameters of the I-V equation obtained from programming results, a PV module model is build with MATLAB. The parameters evaluated by the proposed technique are validated with the datasheet values of six different commercially available PV modules (thin film, monocrystalline, and polycrystalline) at Standard Test Conditions and Nominal Operating Cell Temperature Conditions. The module output characteristics generated by the proposed method are validated with experimental data of FS-270 PV module. The effects of variation of ideality factor and resistances on output characteristics are also studied. The superiority of the proposed technique is proved.

Modeling of photovoltaic modules using a gray-box neural network approach

This paper proposes a gray-box approach to modeling and simulation of photo-voltaic modules. The process of building a gray-box model is split into two components (known, and unknown or partially unknown). The former is based on physical principles while the latter relies on functional approximator and data-based modeling. In this paper, artificial neural networks were used to construct the functional approximator. Compared to the standard mathematical model of photovoltaic module which involves the three input variables - solar irradiance, ambient temperature, and wind speed- a gray-box model allows the use of additional input environmental variables, such as wind direction, atmospheric pressure, and humidity. In order to improve the accuracy of the gray-box model, we have proposed two criteria for the classification of the daily input/output data whereby the former determines the season while the latter classifies days into sunny and cloudy. The accuracy of this model is verified on the real-life photo-voltaic generator, by comparing with single-diode mathematical model and artificial neural networks model towards measured output power data.

The error analysis of the reverse saturation current of the diode in the modeling of photovoltaic modules

In the modeling and simulation of photovoltaic modules, especially in calculating the reverse saturation current of the diode, the series and parallel resistances are often neglected, causing certain errors. We analyzed the errors at the open circuit point, and proposed an iterative algorithm to calculate the modified values of the reverse saturation current, series resistance and parallel resistance of the diode, in order to reduce the errors. Assuming independent irradiation and temperature effects, the irradiation-dependence and the temperature-dependence of the open circuit voltage were introduced to obtain the modified formula of the open circuit voltage under any condition. Experimental results show that this modified formula has high accuracy, even at irradiance as low as 40 W/m2. The errors of open circuit voltage were significantly reduced, indicating that this modified model is suitable for simulations of photovoltaic modules.

Optical simulation of photovoltaic modules with multiple textured interfaces using the matrix-based formalism OPTOS.

The OPTOS formalism is a matrix-based approach to determine the optical properties of textured optical sheets. It is extended within this work to enable the modelling of systems with an arbitrary number of textured, plane-parallel interfaces. A matrix-based system description is derived that accounts for the optical reflection and transmission interaction between all textured interfaces. Using OPTOS, we calculate reflectance and absorptance of complete photovoltaic module stacks, which consist of encapsulated silicon solar cells featuring textures that operate in different optical regimes. As exemplary systems, solar cells with and without module encapsulation are shown to exhibit a considerable absorptance gain if the random pyramid front side texture is combined with a diffractive rear side grating. A variation of the sunlight's angle of incidence reveals that the grating gain is almost not affected for incoming polar angles up to 60°. Considering as well the good agreement with alternative simulation techniques, OPTOS is demonstrated to be a versatile and efficient method for the optical analysis of photovoltaic modules.

Modelling of Photovoltaic Module Using Matlab Simulink

Photovoltaic (PV) module consists of numbers of photovoltaic cells that are connected in series and parallel used to generate electricity from solar energy. The characteristics of PV module are different based on the model and environment factors. In this paper, simulation of photovoltaic module using Matlab Simulink approach is presented. The method is used to determine the characteristics of PV module in various conditions especially in different level of irradiations and temperature. By having different values of irradiations and temperature, the results showed the output power, voltage and current of PV module can be determined. In addition, all results from Matlab Simulink are verified with theoretical calculation. This proposed model helps in better understanding of PV module characteristics in various environment conditions.

Modeling of PV Module Performance under Influence of Surrounding and Essential Factors Variation using Matlab Simulink

of the characteristic of photovoltaic module is a prerequisite for designing and dimensioning a PV power supply. This is the reason for the development of PV module models useful for electrical applications. 7 variables studying model was proposed in this paper as a simple method of modelling and simulation of photovoltaic module using Matlab Simulink. The method is used to determine the characteristic of PV module and studying models successfully reproduce Ipv - Vpv and Ppv - Vpv curves and matches real module data under different scenarios of the influence of different values of solar radiation at different temperatures and wind speed into consideration as environment effects and Saturation Current, Ideal Factor, Series and Shunt resistances as a PV module construction variables, the output current and power characteristic of photovoltaic module are simulated using the proposed model. KeywordsModule; Modelling and Simulation; Matlab

Modeling and Simulation of Photovoltaic Module and Array Based on One and Two Diode Model Using Matlab/Simulink

This paper presents the modeling and simulation of photovoltaic module and array based on one and two diode model using the software Matlab/Simulink. Also, two fast and accurate methods are used to obtain the parameters of photovoltaic panel. The experimental validation of one and two diode model under STC condition and the simulation of P(V) and I(V) Characteristics of ISOFOTON 75 panel under different values of temperature and irradiation are presented.

A circuit-based photovoltaic module simulator with shadow and fault settings

The main purpose of this study was to develop a photovoltaic (PV) module simulator. The proposed simulator, using electrical parameters from solar cells, could simulate output characteristics not only during normal operational conditions, but also during conditions of partial shadow and fault conditions. Such a simulator should possess the advantages of low cost, small size and being easily realizable. Experiments have shown that results from a proposed PV simulator of this kind are very close to that from simulation software during partial shadow conditions, and with negligible differences during fault occurrence. Meanwhile, the PV module simulator, as developed, could be used on various types of series–parallel connections to form PV arrays, to conduct experiments on partial shadow and fault events occurring in some of the modules. Such experiments are designed to explore the impact of shadow and fault conditions on the output characteristics of the system as a whole.

High concentrator photovoltaic module simulation by neuronal networks using spectrally corrected direct normal irradiance and cell temperature

The electrical modelling of HCPV (high concentrator photovoltaic) modules is a key issue for systems design and energy prediction. However, the electrical modelling of HCPV modules shows a significantly level of complexity than conventional photovoltaic technology because of the use of multi-junction solar cells and optical devices. In this paper, a method for the simulation of the I–V curves of a HCPV module at any operating condition is introduced. The method is based on three different ANN (artificial neural networks)-based models: one to spectrally correct the direct normal irradiance, one to predict the cell temperature and one to generate the I–V curve of the HCPV module. The method has the advantage that is fully based on atmospheric parameter and outdoor measurements. The analysis of results shows that the method accurately predicts the I–V curve of a HCPV module for a wide range of atmospheric operating conditions with a RMSE (root mean square error) ranging from 0.19% to 1.66% and a MBE (mean bias error) ranging from −0.38% to 0.40%.