Single Photovoltaic Module Research Articles

A novel method of MPPT of photovoltaic power generation system based on MSINGO

Abstract The speed and accuracy of Maximum Power Point Tracking (MPPT) have a significant impact on photovoltaic power generation. In this paper, a novel method is proposed for fast and accurate MPPT of photovoltaic power generation systems. First, the Northern Goshawk Optimization algorithm (NGO) is introduced in the MPPT. Second, a multi strategy is applied to improve the NGO, and MSINGO is proposed. In the MSINGO algorithm, the optimal individual leadership strategy enhances the optimization accuracy and convergence speed, the enhanced Levy flight strategy leads it to escape from the local optimum, and the pinhole imaging learning strategy guides the population towards the global optimum. Finally, MSINGO was applied to MPPT, and a simulation analysis of a single photovoltaic module and photovoltaic array under different working conditions was conducted and compared with other intelligent optimization algorithms. The results show that the proposed method can achieve MPPT with minimal time consumption and better tracking efficiency.

Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm

The growing demand for solar energy conversion underscores the need for precise parameter extraction methods in photovoltaic (PV) plants. This study focuses on enhancing accuracy in PV system parameter extraction, essential for optimizing PV models under diverse environmental conditions. Utilizing primary PV models (single diode, double diode, and three diode) and PV module models, the research emphasizes the importance of accurate parameter identification. In response to the limitations of existing metaheuristic algorithms, the study introduces the enhanced prairie dog optimizer (En-PDO). This novel algorithm integrates the strengths of the prairie dog optimizer (PDO) with random learning and logarithmic spiral search mechanisms. Evaluation against the PDO, and a comprehensive comparison with eighteen recent algorithms, spanning diverse optimization techniques, highlight En-PDO’s exceptional performance across different solar cell models and CEC2020 functions. Application of En-PDO to single diode, double diode, three diode, and PV module models, using experimental datasets (R.T.C. France silicon and Photowatt-PWP201 solar cells) and CEC2020 test functions, demonstrates its consistent superiority. En-PDO achieves competitive or superior root mean square error values, showcasing its efficacy in accurately modeling the behavior of diverse solar cells and performing optimally on CEC2020 test functions. These findings position En-PDO as a robust and reliable approach for precise parameter estimation in solar cell models, emphasizing its potential and advancements compared to existing algorithms.

Impact of the cell temperature on the energy efficiency of a single glass PV module: thermal modeling in steady-state and validation by experimental data

This paper deals with a simplified and meaningful thermal model used to extract the cell temperature of a photovoltaic (PV) module. This model that takes into account the ambient temperature, incident irradiance and wind velocity is calibrated using a complete experimental database. The results of the thermal modeling provide a heat transfer coefficient law that could be implemented into a PV simulation tool. Two key elements of this approach may be highlighted. The results enable to better calculate the solar energy production of a PV module and to predict the energy efficiency of a PV system.

A Fast Reconfiguration Technique for Boost-Based DMPPT PV Systems Based on Deterministic Clustering Analysis

Mismatching operating conditions affect the energetic performance of PhotoVoltaic (PV) systems because they decrease their efficiency and reliability. The two different approaches used to overcome this problem are Distributed Maximum Power Point Tracking (DMPPT) architecture and reconfigurable PV array architecture. These techniques can be considered not only as alternatives but can be combined to reach better performance. To this aim, the present paper presents a new algorithm, based on the joint action of the DMPPT and reconfiguration approaches, applied to a reconfigurable Series-Parallel-Series architecture, which is suitable for domestic PV application. The core of the algorithm is a deterministic cluster analysis based on the shape of the current vs. voltage characteristic of a single PV module combined with its DC/DC converter to perform the DMPPT function. Experimental results are provided to validate the effectiveness of the proposed algorithm and to demonstrate evidence of its major advantages: robustness, simplicity of implementation and time-saving.

The Impact of Roof Material Profile and Pigmentation on the Performance of Photovoltaic Modules

This study combines simulations and experiments to study the heat interactions between various types of roofs and the photovoltaic (PV) modules installed on them. Specifically, the performance of PV modules on a clay roof was compared with their performance on two types of metal roofs, a Box-profile metal roof and an Orientile metal roof, which differ in shape and geometry. Additionally, this study examined the cooling potential of three common metal roof pigments, iron (iii) oxide (Fe2O3), titanium dioxide (TiO2) and basalt, on roof-installed PV modules. An unpigmented roof was also studied for comparison purposes. Model development and simulation were implemented in COMSOL Multiphysics, and the simulation results were validated and compared with field experiments. The maximum open-circuit voltages of the PV installations were found to be 21.096 V for the clay roof, 20.945 V for the Box-profile metal roof and 20.718 V for the Orientile metal roof. This study revealed that the unpigmented roof had higher solar cell temperatures compared to the pigmented models, with temperature gains ranging from 2.2 °C to 2.71 °C. Moreover, the unpigmented model displayed significantly higher surface radiosity than the pigmented models. The performance output of the modules also varied depending on the metal roof sheet shape and geometry, with the Box-profile metal roof yielding better results than the Orientile metal roof sheet. These results indicate that a specific roof pigmentation may have a small impact on a single PV module, but it can become significant in a large array of modules, especially if cooling through natural convection is hindered.

Potentials of thermocapacitive heat engine for photovoltaic/thermal (PV/T) applications

Effective thermal management can significantly boost the photovoltaic module (PVM) performance due to the suppression of PVM temperature. In this paper, a thermally-driven thermocapacitive heat engine (TCHE) is proposed to harvest the long wavelengths of sunlight transmitted through PVM with the help of solar selective absorber for the aim of additional power production. The performance potentials of the conceptualized hybrid system are evaluated based on the models of PVM and TCHE considering multiple irreversible effects. Calculative results indicate that maximum power density and maximum energy efficiency of the PVM-TCHE hybrid system are, respectively, 38.74% and 38.56% higher than those of the single PVM, showing that TCHE is an effective thermal management approach for PVM. In addition, the influences of various design variables and operation conditions are studied to find clues for further improving the overall performance. Sensitivity analyses show that a larger operating temperature of PVM, diode ideality factor, ion concentration of electrolyte or charge cut-off voltage positively benefits the performance, while a larger ambient temperature or warm-up time worsens the performance. The charge during the charging process and discharging process can be optimally allocated.

A study on the effects of electromagnetic coupling mechanisms in the event of an indirect lightning strike near photovoltaic arrays

Indirect lightning strikes hitting near photovoltaic installations are much more frequent than direct hits, and can generate overvoltage in their electric circuitry. The authors developed a method to estimate the induced overvoltage on single photovoltaic modules. Using a model of the whole array also including Inter-Module Coupling Mechanisms (IMCMs), a new approach to estimate overvoltage induced on full arrays is presented in this paper. The procedure first estimates lumped parameters, modelling either the coupling between the lightning channel and each module in the array or between each couple of modules in array. Several circuital simulations are then run using suitable equivalent circuits. The aim is to consider different array configurations and groundings schemes. This allows extracting the most relevant configuration parameters influencing overvoltage/overcurrent. Results show that the groundings scheme impacts negligibly, but the single module electric stress is decreased by 35% in the case of ungrounded configurations. Among the considered array configurations, the honey-comb and total cross-tied showed the smallest impact on overvoltage. Statistical analysis on random LC tortuous geometry leads to a non-negligible rise of overvoltage peak values, up to 24%. Finally, the relevance of different IMCMs is analysed, showing a strong impact just on frame-ground voltages and currents.

A Hybrid GWO-MPO-Based Maximum Power Point Tracking for Photovoltaic System for a New MLI with Minimum Number of Switches

Photovoltaic (PV) systems provide a number of challenges, one of the most critical of which is determining how to extract the maximum amount of usable power from the PV system even when the system is running in conditions of fast change in irradiance. This study aims to demonstrate a novel hybrid approach to maximum power point tracking (MPPT), which is built on the grey wolf optimization algorithm (GWO) and modified perturb and observe (MP&O) techniques. The second goal of this method is to provide a robust MPPT method with minimum losses in the MPP point of the PV module. This technique is based on the connection of a single KC200GT PV module. The KC200GT PV module is connected to the DC bus via DC/DC boost converter to raise the voltage and implement the MPPT algorithm. After that, the PV system was integrated to supply a new multilevel inverter (MLI) with a reduced number of switches. Additionally, the MATLAB/Simulink environment was used to demonstrate and evaluate the efficacy of the proposed MPPT approach. Complex fast changes in solar irradiation are applied to the module to verify the feasibility and efficacy of the hybrid method by comparing its performance to that of the P&O, and MP&O algorithms. The obtained results demonstrated that the hybrid GWO-MP&O MPPT method achieved an excellent level of efficacy in terms of MPP accuracy, convergence speed (0.05 sec), and overall tracking efficiency (99.7%) compared to other approaches. Keywords: Grey Wolf Optimizer, maximum power point, boost converter, photovoltaic system, multilevel inverter, perturb and observe. https://doi.org/10.55463/issn.1674-2974.50.2.7

Evaluation of a nanofluid-based concentrating photovoltaic thermal system integrated with finned PCM heatsink: An experimental study

Concentrating photovoltaic is an efficient system to implement instead of single photovoltaic modules. Nevertheless, the PV performance and life span would decrease by increasing the cell temperature. This article aims to implement PV cooling methods with a novel hybrid configuration. Accordingly, five cooling methods including concentrating photovoltaic thermal (CPV-T) with water circulation, CPV-T with nanofluid circulation, CPV-T with PCM and nanofluid circulation, CPV-T with nanofluid circulation and finned-PCM, and CPV-T with water circulation and finned-PCM have been analyzed experimentally in a warm climate. After analyzing and comparing the outcomes, it was found that the CPV-T with nanofluid circulation and finned-PCM (CPV-T/NF/FPCM) technique had the most efficient cooling performance. It enhanced the daily average electrical and thermal efficiency of the CPV system, which improved to 17.02% and 61.25%, respectively. Also, the average PV module temperature decreased by about 26.6 °C compared to the CPV units’ temperature. Furthermore, the average system output power reached 20.18W, while the output power for the single CPV achieved a difference of about 4 W compared to the CPV-T/NF/FPCM unit. This study obtained the maximum cooling achievable in actual conditions and technical gaps coverage for a CPV system using commercially available PCM, metal fins, and nanofluids simultaneously.

Development of incremental average differential evolution algorithm for photovoltaic system identification

The determination of the behavior of photovoltaic (PV) systems is a current subject of research due to the increase in their share in electricity production. This identification problem is generally defined as the estimation of the unknown parameters of the equivalent circuit model. The parameters of the PV model are optimized by minimizing the error between the measured data from the actual PV cell and the results of the model. An efficient optimizer tool is required to obtain the best model’s parameter. This paper presents a novel metaheuristic named incremental average differential evolution algorithm (IncADE) for parameter estimation of PV models. The IncADE is a new variant of average differential evolution (ADE) that enhances the global search ability of ADE algorithm by the incremental population strategies. The performance of the developed IncADE is firstly evaluated on well-known benchmark functions, and the experimental results show that the proposed method improves the accuracy of the concluding solutions and the convergence performance of the basic ADE. Then, the IncADE is employed to estimate the optimal parameters of different PV models, which are single diode, double diode and PV module. Experimental results prove the superiority of IncADE on parameter estimation in terms of accuracy and computational efficiency by comparing with ADE and other metaheuristic algorithms.

A hybrid system integrating photovoltaic module and thermoelectric devices for power and cooling cogeneration

For ample utilization of the inlet sunlight, a novel coupled system composed of a photovoltaic module (PVM), a thermoelectric generator (TEG), and a thermoelectric cooler (TEC) is proposed. Short-wave sunlight is sent to PVM to generate electricity, while long-wave sunlight is converted by SSA into heat for TEG-TEC to provide additional cooling. Considering diverse irreversible losses of the cogeneration system, the current relationship between the PVM and the TEG-TEC is analyzed. Besides, the expressions for the performance indexes of the coupled system are deduced under distinct operating conditions. Numerical calculation results illustrate that the power output density and efficiency of the cogeneration system are 3.98% and 3.97% greater than those of a single PVM system in the working current range of TEG-TEC, respectively. Furthermore, the power output density and efficiency of the coupled system with the Thomson effect are, respectively, reduced by 2.47% and 2.46% compared with the proposed system without the Thomson effect. Finally, the impacts of solar irradiance, PVM operating temperature, thermoelectric elements number, environment temperature, and diode ideality factor on the overall module performance are explored. This study results may supply some new ideas for enhancing PVM performance.

Performance evaluation of an integrated photovoltaic module and cascading thermally regenerative electrochemical devices system

To capture the broadband sunlight, a novel coupled system composed of a photovoltaic module (PVM), the thermally regenerative electrochemical cycles (TRECs), and the thermally regenerative electrochemical refrigerators (TRERs) is proposed. The short-wave sunlight is sent to PVM to generate electricity, while the relatively long-wave sunlight (larger than 920 nm) is provided to the TRECs-TRERs for additional refrigeration. Considering the main irreversible dissipation in the cogeneration model, the expressions for the performance indexes of the combined model are deduced. Moreover, the numerical correlation between the PVM operating current and the TRECs-TRERs current is also derived. The mathematical results illustrate that the maximum energy efficiency (MEE) and maximum power density (MPD) of PVM are, respectively, 19.36% and 193.57Wm-2, and the MEE and MPD of the cogeneration system are, respectively, improved by 24.64% and 26.03% compared to a single PVM. Comprehensive parametric analyses are conducted for figuring out the dependences of the established model performance on main significant parameters as well as operating conditions, including solar irradiance, PVM operating temperature, diode ideality factor, specific charge capacity, regenerative efficiency, specific heat capacity, and internal resistance of TREC and TRER. This study results may supply some new ideas for designing and optimizing such actual coupled systems.

3D-thermal modelling of a bifacial agrivoltaic system: a photovoltaic module perspective

This study presents a 3D computational fluid dynamic model to evaluate the temperature distribution and energy performances of a vertical bifacial photovoltaic module for agrivoltaic applications. This last is compared to a conventionally tilted bifacial photovoltaic module for ground-mounted applications. The simulations are performed in SolidWorks Flow Simulation® and validated with measured data gathered from the first experimental agrivoltaic system in Sweden. Additionally, four more simulations scenarios were defined to compare the performances of vertically mounted and conventionally tilted bifacial photovoltaic modules under different operating conditionsThe validation of the computational fluid dynamic model shows that the model tends to underestimate the readings performed with the thermal camera in the order of 3°C to 4°C for the vertical bifacial PV module. The comparison of the results obtained from the computational fluid dynamic model with existing models available in literature shows a good agreement. The comparison of the heat distribution from the computational fluid dynamic model and the thermal images also shows a good agreement. In all the scenarios investigated, the vertical bifacial photovoltaic module's overall efficiency was higher than that of the ground-mounted module due to lower average operating temperatures. The use of the computational fluid dynamic approach for studying the performance of a single photovoltaic module showed promising results that can be extended to study the system performance and microclimatic conditions.

Modified Salp Swarm Optimization for Parameter Estimation of Solar PV Models

The identification of parameters in solar cell models is still a major challenge in photovoltaic (PV) system simulation and design. Because of its more basic ideas, efficiency, adaptability, swarm and evolutionary optimization algorithms, as well as simple procedural frameworks, have been generally used in industry with real-world problems. However, due to the nonlinearity and complication of the PV parameter identification, the obtained solutions from swarm and evolutionary optimizers were immature. An efficient metaheuristic approach for identifying PV model parameters based on the salp swarm algorithm (SSA) is presented in this paper. In the suggested modified salp swarm optimization (MSSA), the leaders and followers will be updated based on the new formulas. The algorithm’s exploration potential is increased by this modification while also preventing it from converge prematurely. The behavior of the suggested technique is verified using benchmark functions, and the outcomes are contrasted with those of SSA and other successful optimization approaches. The suggested MSSA detects numerous characteristics in the PV model include single diode, double diode, and PV modules, in the most efficient way possible. According to the simulation results, MSSA outperforms the competition and may produce better optimal solutions. The findings demonstrate that the best value of RMSE obtained by MSSA is up to 69 percent lower than other methods and is nearly 5.6 percent lower than that assessed by SSA.

An Effective Optimization Algorithm for Parameters Identification of Photovoltaic Models

Renewable energy is becoming more popular due to environmental concerns about the previous energy source. Accurate solar photovoltaic (PV) system model parameters substantially impact the efficiency of solar energy conversion to electricity. In this matter, swarm and evolutionary optimization algorithms have been widely utilized in dealing with practical problems due to their more straightforward concepts, efficacy, flexibility, and easy-to-implement procedural frameworks. However, the nonlinearity and complexity of the PV parameter identification caused swarm and evolutionary optimizers to exhibit Immaturity in the obtained solutions. In this study, an effective metaheuristic algorithm based on tunicate swarm optimization (TSA) is proposed for parameter identification of PV models. The proposed improved algorithm (ITSA) has two main phases at each iteration: searching all around the search space based on a randomly selected tunicate and improving the search using the position of the best tunicate. This modification improves the algorithm’s exploration ability while also preventing premature convergence. The suggested algorithm’s performance is confirmed using ten mathematical test functions and the outcomes are compared with TSA as well as some effective optimization algorithms. The proposed ITSA optimally identifies various parameters in the PV model, such as single diode (SDM), double diode (DDM), and PV modules. Based on the comprehensive comparisons, results indicate that the improved ITSA algorithm has higher convergence accuracy and better stability than the original TSA and other studied algorithms.

Solar photovoltaic model parameter estimation based on orthogonally-adapted gradient-based optimization

Solar photovoltaic (PV) model parameter estimation research is a growing field of interest. To establish accurate and reliable PV models, including single diode, double diode, three diode and PV module models, a new method is proposed in this paper. Gradient-based optimization (GBO) is designed to be used in conjunction with orthogonal learning, namely, orthogonal learning gradient-based optimization (OLGBO). In OLGBO, the orthogonal learning (OL) mechanism improves the speed rate and accuracy of GBO, achieving an adaptive conversion between exploitation and exploration. Therefore, the proposed OLGBO is evolved into a way for unknown parameter estimation of different PV models. Compared with the RLGBO and basic GBO methods, OLGBO achieves a smaller root-mean-square error (RMSE) and standard deviation (STD). In addition, OLGBO is compared with other algorithms, and the outcomes also demonstrate that the OLGBO method has a better excellent effect than selected state-of-the-art methods at certain temperatures and light conditions. Thus, all the evidences indicates that OLGBO can be a fast, promising, reliable, and feasible optimization method for dealing with unknown parameter identification problems in photovoltaic models.

Practical and simplified measurements for representative photovoltaic array temperatures robust to climate variations

A precise and practical power generation forecasting method for outdoor photovoltaic (PV) arrays and its performance evaluation are essential for advancements in solar power generation. Specifically, accurate temperature measurements of each of the PV modules in an array, the entire outdoor PV array, and the power generation system are crucial regardless of climatic and irradiance conditions. However, outdoor PV temperatures are often affected by environmental factors, such as wind direction, wind speed, solar irradiance, and outdoor air temperature fluctuations. Hence, the temperature data vary greatly depending on the measurement point of the module, which is affected by the above environmental factors. The variations of and uncertainties in temperature often extend over long arrays of PV modules common to large-scale PV systems. Therefore, it is necessary to establish a straightforward and reproducible inspection method to determine a single and robust representative PV module temperature for an array. In this study, we have established one such inspection method for outdoor PV arrays. It was observed that the temperature at the center of the array was closest to the average temperature regardless of the measurement period and climatic conditions; thus, it can be considered as the unbiased and robust representative temperature for the PV array. Moreover, lower irradiance conditions are better for accurate and repeatable PV module temperature measurements. The mean temperature differences of the PV array from its average measured at the center of the array were 0.2 °C for low irradiance and 0.5 °C for high irradiance. The temperature difference observed will have 0.1% to 0.2% impact on the power estimation for monitoring purposes.

Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation

Extracting the optimum parameters of solar photovoltaic (PV) model using the experimental data of current–voltage is very critical in simulating, controlling, and optimizing the PV systems. One of the important problems encountered in modeling and simulating is to find a model that can extract parameters from PV models quickly, accurately, and reliably. Based on this motivation, the goal of this study is to suggest an improved algorithm, namely genetic algorithm based on non-uniform mutation (GAMNU), in order to approximate efficiently the parameters of solar cells and PV modules. In GAMNU, non-uniform mutation operator is used to maintain diversity in the explored solutions and the crossover operator follows an adaptive search strategy, which consists of searching the entire space at the beginning while maintaining a focused search when the population tends to converge in a certain region of the search space. The performance of the method is comprehensively evaluated on different solar cell models, including single and double diode, and single diode PV modules, of a R.T.C France silicon solar cell, ESP-160 PPW PV, STP6-120/36 and Photowatt-PWP201 module. The results obtained from single and double diode models for R.T.C France are respectively 9.8618×10-4 and 9.8683×10-4, and for Photowatt-PWP201, STP6-120/36 and ESP-160 PPW are 2.3824×10-3 2.382420230900×10-3, 1.6735×10-21.6735786505085×10-2 and 8.2942×10-2 8.2942×10-2. The statistical obtained results show that the proposed method has very competitive performance in terms of accuracy and reliability when compared to other advanced algorithms. Therefore, the proposed algorithm is highly useful to extract the parameters of solar PV models.

Multiple learning JAYA algorithm for parameters identifying of photovoltaic models

Extracting the optimum parameters of the solar photovoltaic (PV) model based on measured current–voltage data accurately, reliably, and quickly is a vital step to simulating, evaluating, and optimizing the model PV. Although several meta-heuristics have been proposed to extract photovoltaic parameters using experimental data, it suffers from slow convergence and poor efficiency which leads to poor solution quality. In this paper, a multiple learning JAYA (MLJAYA) approach has been proposed to extract the PV parameters under different operating conditions. The main advantage of multiple learning strategies introduced in the MLJAYA proposed is its ability to balance exploration and exploitation capabilities. The chaos perturbation mechanism is employed to improve the quality of the population to escape the local optimum and improve the accuracy of the basic JAYA algorithm. The structure of MLJAYA is very simple and requires only the size of the population and stop criterion. The efficacy of the proposed MLJAYA has been verified by including different PV modules models such as single diode, double diode, and PV module, and the results obtained was compared with different variants of JAYA and other state-of-the-art optimization algorithms. The best results of root mean square error (RMSE), and absolute error generated by MLJAYA are (RMSE = 9.8602E−04, SIAE = 1.781248E−02), and (RMSE = 9.8248E−04, SIAE = 1.76E−02) for RTC solar cell single diode and double diode respectively, and (RMSE = 2.4250748E−03, SIAE = 4.686375E−02) for Photowatt-PWP201. The results show the superior performance in terms of accuracy and reliability. Since, the MLJAYA show is a potential approach for extracting parameters from photovoltaic models.

A novel levy flight trajectory-based salp swarm algorithm for photovoltaic parameters estimation

Simulation and optimization of photovoltaic (PV) systems are recently urging as a hotspot in the field of renewable energy. For this, different models have been introduced to simulate the behavior of real PV cells and modules. An accurate estimation of unknown parameters of PV models is necessary to achieve a good performance of photovoltaic systems. This paper presents a new metaheuristic algorithm called Levy flight trajectory-based salp swarm algorithm (LSSA), which estimated single diode (SD), double diode (DD) and PV module models parameters. Compared with the original salp swarm algorithm (SSA), LSSA benefits from enhanced population diversity due to Levy flight trajectory characteristic of long jump steps. In order to validate its efficiency, the proposed technique was compared with other well-known meta-heuristics for estimating PV parameters in the SD, DD and PV modules. The experimental and comparative results demonstrated that LSSA was able to find extremely accurate solutions with high reliability and offers very competitive results for the problem of estimating PV parameters.