Parameter Estimation of Three Diode Photovoltaic Model Using Grasshopper Optimization Algorithm

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

Among all renewable energy sources, solar cells are considered the most popular solution for a clean source of energy and have a wide range of applications from few watts to Megawatt industrial and domestic loads. Building a precise mathematical model based on nonlinear equations for solar cells as well as photovoltaic (PV) modules is an essential issue for reasonable performance assessment, control and optimal operation of PV energy systems. In the current study, a novel optimization algorithm, Tree Growth Algorithm (TGA), is applied for accurate and efficient extraction of the unknown solar cell and PV module parameters. TGA is applied for estimating the unidentified parameters of PV models. Single diode model (SDM), double diode model (DDM) and three diode model (TDM) are investigated in the mathematical models of both solar cells and PV modules. The obtained results from the application of TGA to achieve this objective are compared with different algorithms reported in the literature. Moreover, the results demonstrated that the proposed algorithm of TGA superior to other reported methods. The good matching of the I-V characteristic curve of the computed parameters with those of the measured data from the manufacturer’s PV modules/cells datasheet proved that the proposed TGA may function as a competitor to the methods provided in literature for parameters’ identification of PV of solar cells.

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.

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.

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

Different types of photovoltaic (PV) cell technologies have been commercially used for various applications. These cell technologies can be categorized as multi-crystalline, mono-crystalline and thin film. Due to the differences in PV cell technologies, a single PV model is unable to model all the output characteristics. The single and double diode PV models have been widely used for modeling the output characteristic of a PV module. This paper introduces a generalized multi-dimension diode PV model which can be used to select the most suitable dimension for a particular PV cell technology. The results confirm that the single diode PV model is suitable for the multi-crystalline and mono-crystalline PV modules. For the thin film PV module, a multi-dimension diode PV model is required to achieve a low modeling error.

A general algorithm for the optimization of photovoltaic modules layout on irregular rooftop shapes

A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters

Photovoltaic (PV) solar cell which converts solar energy directly into electrical energy is one of the feasible alternative sources to fossil fuel. This study aims at predicting the energy recovery from a typical photovoltaic solar cell, BP 3 series 235 W solar panel, by developing a reliable mathematical model of the solar panel which could represent the real systems. The model equation was solved using the ‘solve block’ tool in MathCAD 14 software and validated by physical data obtained from literature. Using the model developed the effects of temperature and solar irradiance on performance of PV solar panel was investigated using nominal conditions of 298 K and 1000 W/m 2 as basis. Temperature was varied between 273, 298, 323, 348 and 373 K at constant irradiance of 1000 W/m 2 . Solar irradiance was also varied using 200, 400, 600, 800 and 1000 W/m 2 while maintaining temperature at 298 K. The energy recovery from the PV model was evaluated using the fill factor concept. From the analysis of results, there is some agreement between the simulation results and the reported experimental performance of the PV solar panel. The performance of the PV system increased with increase in temperature and solar irradiance. In purview of values of solar irradiation (200 – 1000 W/m 2 ) and temperature (273 - 373 K) that were studied, the energy recovery was maximum at 79.98% which agrees with values of between 75 and 85% obtained in practical solar cells. Keywords: Photovoltaic, Mathematical model, Energy recovery, Simulation

Photovoltaic (PV) technology is gaining much interest as a clean, sustainable, noise-free source of energy. However, the non-linear behavior of PV modules and their dependency on varying environmental conditions require thorough study and analysis. Many PV modeling techniques have been introduced in the literature, yet they exhibit several complexity levels for parameter extraction and constants estimation for PV power forecast. Comparatively, a simple, accurate, fast, and user friendly PV modeling technique is proposed in this paper featuring the least computational time and effort. Based on function representation of PV curves’ available in PV datasheets, an empirical mathematical equation is derived. The proposed formula is considered a generic tool capable of modeling any PV device under various weather conditions without either parameter estimation nor power prediction. The proposed model is validated using experimental data of commercial PV panels’ manufacturers under various environmental conditions for different power levels. The obtained results verified the effectiveness of the proposed PV model.

How to accurately and efficiently extract photovoltaic (PV) model parameters is the primary problem of photovoltaic system optimization. To accurately and efficiently extract the parameters of PV models, an enhanced moth-flame optimization (EMFO) with multiple flame guidance mechanism is proposed in this study. In EMFO, an adaptive flame number updating mechanism is used to adaptively control the flame number, which enhances the local and global exploration capabilities of MFO. Meanwhile, a multiple flame guidance mechanism is designed for the full use of the position information of flames, which enhances the global diversity of the population. The EMFO is evaluated with other variants of the MFO on 25 benchmark functions of CEC2005, 28 functions of CEC2017, and 5 photovoltaic model parameter extraction problems. Experimental results show that the EMFO has obtained a better performance than other compared algorithms, which proves the effectiveness of the proposed EMFO. The method proposed in this study provides MFO researchers with ideas for adaptive research and making full use of flame population information.

Recently, renewable energy resources have been widely penetrated because of their technical advantages and environmental benefits. So, modeling and simulation of this resource are very important. Obtaining accurate photovoltaic (PV) solar cell's parameters is very significant to monitor maximum power point, performance assessment, regulation, efficiency measurements, and diagnose problems. In this paper, three photovoltaic parameters estimation models are considered. These three models, single-diode, two-diode, and triple-diode models, are presented in this work to investigate the effect of the model on precision. In this, work the Elephant Herding Optimization (EHO) algorithm is employed for the PV three models' parameters estimation. Real experimental data of a 55 mm diameter commercial Radiotechnique-Coprim (RTC France) solar cell was used for the EHO implementation. The suggested algorithm was assessed compared with several recent optimizers like the crow search algorithm, spotted hyena optimization, moth flame optimization, grasshopper optimization algorithm, and Harris hawk's optimization. The results revealed that the estimated I-V and P-V curves are very close to the experimental data at very low error. Also, the results demonstrate different models' effects in fitting the curves and proved the efficiency of the suggested EHO algorithm over other algorithms.

The paper presents grid connected Photovoltaic (PV) models for three-phase distribution load flow analysis. The models comprises of single-diode PV model with and without the effect of the series and parallel resistances, I-V nonlinear question, I-V database and single-phase PV modeled as singlephase real power injection. The three-phase load flow program with Photovoltaic models has been tested using IEEE 13 node feeder. The solution of the base case is compared with the radial distribution analysis package (RDAP) before used to analyze distribution networks. The analysis is carried out with various photovoltaic mathematical models. The simulation results show that the grid-connected three-phase photovoltaic system in distribution network can improve the voltage profile as well as reduce the total system losses. However, single-phase PV DG model does not always guarantee voltage improvements.

This work introduces a electric-thermal model for a photovoltaic (PV) cell which combines electrical and thermal parameters, and also incorporates dynamic characteristics and accurate modeling in the reverse-bias region. Incoming irradiance and ambient temperature are used to calculate the PV cell temperature based on a five-layer thermal model. The cell temperature in the electrical model is updated according to the thermal model to accurately adjust the PV cell output electrical characteristics. To test and validate the electrical and thermal characteristics of the PV cell, a custom setup was built that allows temperature measurement at the PV cell and glass surfaces. Performance of the electric-thermal model in both forward- and reverse-bias region is verified using experimental data in realistic scenarios. This PV model can be scaled up and used to simulate PV systems in a wide variety of applications, extreme environmental conditions, and fault conditions.

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.