Estimation of photovoltaic model parameters using the Rao-1 optimization algorithm

The modelling of photovoltaic (PV) solar panels requires electrical parameters which are dependent on the manufacturing materials and their physical properties. Manufacturers typically do not disclose detailed physical properties of the PV module, except for some electrical quantities such as open circuit voltage ( V oc ), short‐circuit current ( I sc ), maximum power point voltage ( V m ), maximum power point current ( I m ) and maximum power ( P M ). However, to model the PV panels comprehensively, it is necessary to determine other physical parameters, e.g., series resistance of PV cell ( R s ), shunt resistance of PV cell ( R Sh ) and diode ideality factor ( n ). This paper presents a generalised mathematical model of a PV panel utilising only the quantities provided in manufacturer's datasheet. The proposed modelling technique determines all the PV panel parameters without any explicit repetitive iteration. Although the developed model is general and can be implemented on any software platform, its implementation is demonstrated on a commercial electromagnetic transients simulation software electro magnetic transient including direct current power systems computer aided design. The electrical parameters obtained from the proposed PV panel model are validated for six different commercially available PV panels from their datasheet values and also from measurements provided by National Institute of Standards and Technology for solar irradiation and temperature at nonstandard test conditions.

This work presents a Simulink-based model of a photovoltaic (PV) system using a single-diode and two-diode model of solar cell. A comparison between the two-diode and single-diode model of PV cell has been illustrated. In addition, the output of series-parallel connection of PV cells has been examined. In the model, series and shunt resistances are calculated by an efficient iteration method based on open-circuit voltage, short-circuit current and irradiance values. The PV module implemented in Simulink/MATLAB considers five parameters. The parameters are series and shunt resistance, reverse saturation current, photocurrent and ideality factor. Approximate parameters are obtained from the manufacturers datasheets. The model includes light intensity and ambient temperature as input. Power, cell temperature and voltage as well as any measurements of interests are the outputs. For the grid connection of solar cell, inverter, filter and a step-up transformer is utilized. The performance of the model is found satisfactory.

It is known that compound parabolic concentrators (CPCs) can improve electrical performance of a photovoltaic (PV) flat-plate system. However, a lumped electrical model of a PV cell/module with CPC for assessing performance under different operating conditions is unavailable. In this paper, a six-parameter based model is developed and applied to a PV cell, two PV models with CPC, and a PV module with 2D asymmetric CPC (trough). For validation, CPC with a single PV cell and two CPC modules with 2×2 and 9×9 PV cells are fabricated and measured in an indoor laboratory under standard test conditions. Results show that the optimised algorithm precisely predicts the six model parameters. A sensitivity analysis is performed to identify the importance of each parameter in the model. Ideality factor, circuit current and reverse saturation current are found to be the most dominant factor, while shunt resistance is the least important with CPC gain coefficient and series resistance are in between. Transient performance of a PV cell with CPC under variable outdoor climate conditions is also examined by coupling optical, thermal and electrical effects.

This paper presents the estimation method of solar irradiance in W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> based on the short circuit current output produce by the photovoltaic module. The proposed solar irradiance estimation method is derived using photovoltaic mathematical model. Therefore, photovoltaic module modeling is required to develop the proposed estimation method. The modeling includes the identification of photovoltaic module diode ideality factor using recursive iteration and series resistance identification using photovoltaic model equation. The reminding modeling parameters can be obtained from the given photovoltaic module manufacturer datasheet. With all the required parameters in place the solar irradiance can be estimated using equation derived from the photovoltaic model. The proposed solar irradiance estimation method is evaluated using a 80W 36 cells polycrystalline photovoltaic module in the field and the results are validated with a World Meteorological Organization certified second class pyranometer. This paper demonstrates that the solar irradiance can be estimated by the short circuit current output measured from the photovoltaic module with acceptable accuracy. This contributes to the elimination of the installation of expensive pyranometer.

Solar cell/module modeling involves the formulation of the non-linear current versus voltage (I-V) curve. Determination of parameters plays an important role in solar cell/module modeling. This paper presents an application of the improved PSO search method and Particle Swarm Optimization technique for identifying the unknown parameters of solar cell and photovoltaic module models, namely, the series resistance, shunt resistance, generated photocurrent, saturation current, and ideality factor that govern the current-voltage relationship of a solar cell/module. For the confirmation of accuracy of the proposed method, a measurement data of 57 mm diameter commercial (R.T.C. France) silicon solar cell and a module consisting of 36 polycrystalline silicon cells (Photowatt-PWP 201) has been selected and the best optimal value of each parameter has been obtained using Improved PSO. Comparative study among different parameter estimation techniques is presented to demonstrate to verify the accurateness and the effectiveness of the proposed approach.

While addressing the issue of improving the performance of Photovoltaic (PV) systems, the simulation results are highly influenced by the PV model accuracy. Building the PV module mathematical model is based on its I-V characteristic, which is a highly nonlinear relationship. All the PV cells’ data sheets do not provide full information about their parameters. This leads to a nonlinear mathematical model with several unknown parameters. This paper proposes a new application of the Grasshopper Optimization Algorithm (GOA) for parameter extraction of the three-diode PV model of a PV module. Two commercial PV modules, Kyocera KC200GT and Solarex MSX-60 PV cells are utilized in examining the GOA-based PV model. The simulation results are executed under various temperatures and irradiations. The proposed PV model is evaluated by comparing its results with the experimental results of these commercial PV modules. The efficiency of the GOA-based PV model is tested by making a fair comparison among its numerical results and other optimization method-based PV models. With the GOA, a precise three-diode PV model shall be established.

The increasing demand for electrical energy has made it inevitable to bring forth an environment-friendly energy resource which has to be robust as well as economic. Solar energy fulfils these criteria significantly, and to cultivate such energy, a reliable photovoltaic (PV) model is required. But most of the typical PV system undergoes a low energy conversion ratio because of improper choice of PV parameters. To build a proficient PV model, the estimation of precise and accurate parameters is mandatory. This paper portrays the Sine Cosine Algorithm (SCA) for the estimation of photovoltaic (PV) module parameters. Unknown parameters of the PV model of a single diode PV module are estimated under the standard test condition (STC). PV parameter estimation using SCA has shown a significant minimum value of the fitness function hence maximizing the convergence. A comparative study has been done between SCA and other existing popular techniques named as the nonlinear least square (NLS) method and the modified Newton–Raphson (N-R) method. From the power–voltage (P-V) and current–voltage (I-V) characteristics, it’s found that the SCA model matches more accurately with the datasheet values.KeywordsRenewable energyPhotovoltaicSingle diode modelSine cosine algorithmParameter estimation

As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm’s efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.

Herein, the voltage and current output characteristics of a laser photovoltaic (PV) module applied to a wireless power transmission system using a laser beam are analyzed. First, an experiment is conducted to obtain the characteristic data of the voltage and current based on the laser output power of the laser PV module, which generates the maximum power from the laser beam at a wavelength of 1080 nm; subsequently, the small-signal voltage and current characteristics of the laser PV module are analyzed. From the analysis results, it is confirmed that the laser PV module has a characteristic in which the maximum power generation point varies according to the power level of the laser beam. In addition, similar to the solar cell module, it is confirmed that the laser PV module has a current source and a voltage source region, and it shows a small signal resistance characteristic having a negative value as the operating point goes to the current source region. In addition, in this paper, by reflecting these electrical characteristics, a method for designing the controller of a power converter capable of charging a battery while generating maximum power from a PV module is proposed. Since the laser PV module corresponds to the input source of the boost converter used as the power conversion unit, the small-signal transfer function of the boost converter, including the PV module, is derived for the controller design. Therefore, by designing a controller that can stably control the voltage of the PV module in the current source, the maximum power point, and voltage source regions defined according to the output characteristics of the laser PV module, the maximum power is generated from the PV module. Herein, a systematic controller design method for a boost converter for laser wireless power transmission is presented, and the proposed method is validated based on the simulation and experimental results of a 25-W-class boost converter based on a microcontroller unit control.

Advanced three-stage photovoltaic system phasor model for grid integration dynamic studies

Photovoltaic (PV) model is used in simulation study to validate the system design of a PV system. With an accurate PV model for use in circuit orientated software, the behavior of PV array under different environmental conditions can be observed for study. In this paper, the unknown parameters of a single diode PV model are identified using the Particle Swarm Optimization (PSO) approach with log barrier constraint. The proposed method has been applied to a PV module KC65T and has shown accurate modeling result.

Particle swarm optimisation with adaptive mutation strategy for photovoltaic solar cell/module parameter extraction

Thermal–electrical model for energy estimation of a water cooled photovoltaic module

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. The ICO algorithm’s search phase is controlled based on the leader’s position. The step length is adjusted following the sorted population. As a result of this updated operator, the algorithm can perform global and local searches. Furthermore, the interaction factor during the attack phase is adjusted based on the position of the prey, and a random value controls the turning factor. Single-, double-, and PV module models are investigated to test the ICO’s parameter estimation performance. Statistical analysis uses the minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in the test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared with other state-of-the-art optimization algorithms, the CO and ICO algorithms are proven to be highly reliable and accurate when identifying PV parameters. According to the results, the ICO and CO obtained the first- and second-best sum ranking results for the studied PV models among 12 applied algorithms. Despite this, the ICO algorithm reduces the CO’s computation time by 40% on average. Additionally, ICO’s convergence speed is high, reaching an optimal solution in less than 25,000 function evaluations in most cases.

A two stage successive estimation based maximum power point tracking technique for photovoltaic modules