Double Diode Model Research Articles

Parameter Identification of Solar Photovoltaic Systems Using an Augmented Subtraction-Average-Based Optimizer

Solar photovoltaic system parameter identification is crucial for effective performance management, design, and modeling of solar panel systems. This work presents the Subtraction-Average-Based Algorithm (SABA), a unique, enhanced evolutionary approach for solving optimization problems. The conventional SABA works by subtracting the mean of searching solutions from the position of those in the population in the area of search. In order to increase the search capabilities, this work proposes an Augmented SABA (ASABA) that incorporates a method of collaborative learning based on the best solution. In accordance with manufacturing, the suggested ASABA is used to effectively estimate Photovoltaic (PV) characteristics for two distinct solar PV modules, RTC France and Kyocera KC200GT PV modules. Through the adoption of the ASABA approach, the simulation findings improve the electrical characteristics of PV systems. The suggested ASABA outperforms the regular SABA in terms of efficiency and effectiveness. For the R.T.C France PV system, the suggested ASABA approach outperforms the traditional SABA technique by 90.1% and 87.8 for the single- and double-diode models, respectively. Also, for the Kyocera KC200GT PV systems, the suggested ASABA approach outperforms the traditional SABA technique by 99.1% and 99.6 for the single- and double-diode models, respectively. Furthermore, the suggested ASABA method is quantitatively superior to different current optimization algorithms.

Parameters Extraction of Single Diode and Double Diode Models Using Analytical and Numerical Approach: A Comparative Study

ABSTRACT This review paper provides a comparative study between analytical and numerical approaches. The purpose is to estimate the unknown parameters of the single diode and dual diode models. Nine of the most common models are examined in depth, detailing the parameter calculated, the most important approximations characterizing each model, and the calculation process. The effectiveness of the methods studied is assessed under various temperatures and solar irradiation conditions for three types of PV modules using various technologies, such as single-crystalline (CS6V225M), multi-crystalline (CS6P265), and thin film (ST40). The quality of the models fitting to the measured data is assessed by statistical analysis. Results of the study indicate that (1) the temperature exhibits a fairly low impact on the key points of the I–V characteristics of the solar panel, particularly under short and open-circuit conditions, and that (2) the open-circuit voltage at low solar irradiance, as shown in I–V curves, is underestimated. The results of the simulation show that the performance in terms of accuracy varies from one technology to another and the same technology varies with the conditions of temperature and solar irradiation. It is expected that this work will be useful to update the knowledge in the field of PV modeling and simulation.

An effective QUATRE algorithm based on reorganized mechanism and its application for parameter estimation in improved photovoltaic module

The traditional parameter estimation methods for photovoltaic (PV) module are strictly limited by the reference standards. On the basis of the double diode model (DDM), this paper proposes a modified PV module that is independent of the reference conditions and can be used for the transformation and reconfiguration of PV module. With respect to the issue of the slow convergence precision and the tendency to trap in the local extremum of the QUATRE algorithm, this research incorporates the QUATRE algorithm with recombination mechanism (RQUATRE) to tackle the problem of parameter estimation for the improved PV modules described above. Simulation data show that the RQUATRE wins 29, 29, 21, 17 and 15 times with the FMO, PIO, QUATRE, PSO and GWO algorithms on the CEC2017 test suite. In addition, in a modified PV module for the parameter extraction problem, the final experimental results achieved a value of 2.99 × 10−3 at RMSE, all better than the accuracy values of the compared algorithms. In the fitting process of IAE, the final values are also all less than 10%, which can satisfy the fitting needs.

Parameter Extraction of Solar Photovoltaic Model Based on Nutcracker Optimization Algorithm

In order to improve the accuracy and reliability of the photovoltaic (PV) model, this paper explores a novel nature-inspired metaheuristic algorithm, i.e., the nutcracker optimizer algorithm (NOA), for the parameter extraction of a PV model, such as a single diode model (SDM), double diode model (DDM), and triple diode model (TDM) of PV components. The Aleo Solar S79Y300 monocrystalline silicon solar panel was tested at 1000 W/m2 solar irradiance and 25 °C temperature, and the results of the proposed NOA algorithm were compared with three popular algorithms, i.e., particle swarm optimization (PSO), firework algorithm (FWA), and whale optimization algorithm (WOA), in terms of algorithm accuracy and running time, and non-parametric tests were performed. The results show that the NOA can improve the efficiency of PV parameter extraction, and its performance is the best among the tested algorithms. It has the best root mean square error (RMSE) values in the SDM, being 7.92587 × 10−5 and 6.02460 × 10−5 in the DDM and 6.23617 × 10−5 in the TDM, and the shortest average execution time according to the overall ranking, making it well suited for extracting PV model parameters.

A Hunter-Prey Algorithm Coordinating Mutual Benefit and Sharing and Interactive Learning for High-Efficiency Design of Photovoltaic Models

It is crucial for the photovoltaic system to have an accurate model and well-estimated parameters to further increase conversion efficiency. Most existing methods for identifying photovoltaic model parameters have problems such as high computational cost, local optimum trouble, or difficulty in providing the best performance due to complex adjustments of algorithm parameters. To improve these defects, a hunter-prey optimization algorithm coordinating mutual benefit and sharing and interactive learning activities (EHPO) is proposed. First, based on hunter-prey optimization, timely information sharing with the mutual benefit and sharing mechanism was achieved when the algorithm was applied to search for prey, thus improving the searching precision and convergence rate of the algorithm. Second, hunters used the history optimization information and useful information from peers to guide the search direction, thus balancing the global and local development abilities of the algorithm. Finally, a lens imaging backward learning strategy was adopted to prevent the algorithm from falling into the local optimum, thus increasing the diversity of varieties and the probability of finding the global optimal solution. The simulation results of the single-diode model (SDM), double-diode model (DDM), triple-diode model (TDM), and other PV models in different environmental conditions show that the improved EHPO algorithm is more advantageous for parameter extraction than other advanced metaheuristic algorithms. This study demonstrates that EHPO is an accurate and reliable tool for predicting the parameters of PV models.

Radial Basis Function Based Meta-Heuristic Algorithms for Parameter Extraction of Photovoltaic Cell

Accurate parameter estimation of photovoltaic (PV) cells is crucial for establishing a reliable cell model. Based on this, a series of studies on PV cells can be conducted more effectively to improve power output; an accurate model is also crucial for the operation and control of PV systems. However, due to the high nonlinearity of the cell and insufficient measured current and voltage data, traditional PV parameter identification methods are difficult to solve this problem. This article proposes a parameter identification method for PV cell models based on the radial basis function (RBF). Firstly, RBF is used to de-noise and predict the data to solve the current problems in the parameter identification field of noise data and insufficient data. Then, eight prominent meta-heuristic algorithms (MhAs) are used to identify the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM) parameters of PV cells. By comparing the identification accuracy of the three models in two datasets in detail, the final results show that this method can effectively achieve parameter extraction, with advantages such as high extraction accuracy and stability, greatly improving the accuracy and reliability of parameter identification. Especially in the TDM, the I-V data and P-V data obtained from the PV model established through the identified parameters have very high fitting accuracy with the measured I-V and P-V data, reaching 99.58% and 99.65%, respectively. The research can effectively solve the low accuracy problem caused by insufficient data and noise data in the traditional method of identifying PV cells and can greatly improve the accuracy of PV cell modeling. It is of great significance for the operation and control of PV systems.

Parameter Extraction of Solar Photovoltaic Modules Using a Novel Bio-Inspired Swarm Intelligence Optimisation Algorithm

For extracting the equivalent circuit parameters of solar photovoltaic (PV) panels, a unique bio-inspired swarm intelligence optimisation algorithm (OA) called the dandelion optimisation algorithm (DOA) is proposed in this study. The suggested approach has been used to analyse well-known single-diode (SD) and double-diode (DD) PV models for several PV module types, including monocrystalline SF430M, polycrystalline SG350P, and thin-film Shell ST40. The DOA is adopted by minimizing the sum of the squares of the errors at three locations (short-circuit, open-circuit, and maximum power points). Different runs are conducted to analyse the nature of the extracted parameters and the V–I characteristics of the PV panels under consideration. Obtained results show that for Mono SF430M, the error in the SD model is 2.5118e-19, and the error in the DD model is 2.0463e-22; for Poly SG350P, the error in the SD model is 9.4824e-21, and the error in the DD model is 2.1134e-20; for thin-film Shell ST40, the error in the SD model is 1.7621e-20, and the error in DD model is 7.9361e-22. The parameters produced from the suggested method yield the least amount of error across several executions, which suggests its better implementation in the current situation. Furthermore, statistical analysis of the SD and DD models using DOA is also carried out and compared with two hybrid OAs in the literature. Statistical results show that the standard deviation, sum, mean, and variance of various PV panels using DOA are lower compared to those of the other two hybrid OAs.

Sunflower optimization algorithm for multi-strategy-assist parameter identification of solar cell models

A novel optimization method, namely, the elite opposition learning and polynomial steps-based sunflower optimization (EOPSFO) algorithm, has been proposed to solve engineering problems. To speed up the convergence, the elite opposition-based learning and polynomial steps strategy is applied to automatically determine the search step adapted in each iteration. To verify the performance of EOPSFO, the feasibility of EOPSFO is first verified using various benchmarking and some standard optimization problems. In addition, EOPSFO is used to determine the parameters of the single diode model and double diode model. Results show that EOPSFO can be regarded as a competitive algorithm in optimization problems.

A new physics-based solar reconfigured model to enhance efficiency under partial shading conditions: Experimental feasibility

ABSTRACT Maximum power generation from solar modules is adversely affected by partial shading conditions. To strengthen and obtain feasible output under any complex shadings, a new double diode model with deterioration factor (DDM-DF) is proposed for the optimal selection of shading patterns. It is used to validate the effectiveness of the novel heuristic approach called time-distance-constrained topology (D-CT). The novel D-CT is compared with conventional techniques (bridge link (BL), series-parallel (S-P), total cross-tied (TCT)), conventional puzzles (sudoku (SDK), magic-square (M-SP)), and recently reconfigured techniques (permutation-combination (PCR), knight-tour (KTR)). In this paper, the experimental study validates the outcomes obtained by MATLAB/SIMULINK results under various realistic shadings. Experimental results prove that the novel proposed D-CT enhances the efficiency by 5.45%, 3.57%, and 3.54% over TCT, PCR, and KTR under inverted-L shading. Further, the minimum and maximum mismatch loss reduced by the proposed D-CT is up to 1.90% and 97.61% under top shading against SDK and S-P respectively. The real-time validation for the proposed novel 4×4 D-CT under pi and diagonal shading exhibits greater performance parameter enhancement of a solar array subjected to shadings in correlation with other models.

Parameters identification of photovoltaic solar cells using FIPSO-SQP algorithm

In this paper a heuristic algorithm called flexible improved particle swarm optimization (FIPSO) and a gradient based algorithm called sequential quadratic programming (SQP) are combined together to be used for global optimal searching of PV parameters. Some significant features of the proposed method are: balancing between exploration and exploitation phases, providing results with higher accuracy, having higher convergence speed and performing better global search. To validate the performance of the proposed technique, it is used to identify parameters of a single diode (SD), a double diode (DD) and a triple diode (TD) PV model and an actual PV module. Results are compared with those obtained by some recent and well-established methods. Simulation results verify the superiority of the proposed FIPSO-SQP method to the other methods in terms of the convergence time (2.77 s) and accuracy (root mean square error of 9.8602e-4 for SD model, 9.8177e-4 for DD model, 9.81164e-4 for TD model and 0.016450 for PV module).

Emerging Parameters Extraction Method of PV Modules Based on the Survival Strategies of Flying Foxes Optimization (FFO)

Nowadays, the world is encountering multiple challenges of energy security, economic recovery, and the effect of global warming. Investing in new fossil fuels only locks in uneconomic practices, sustains existing risks and increases the threats of climate change. In contrast, renewable energies, such as photovoltaic energy, constitute one of the most promising technologies in combating global increase in temperatures. Given its simplicity and low maintenance costs, photovoltaic energy is the most effective alternative to address the issues above. However, the standard test conditions (STCs) of PV modules are, in most cases, different from the real working conditions of a solar module. For instance, high levels of incident irradiation in an arid climate may cause the temperature of a module to rise by many degrees above the STC temperature of 25 °C, lowering the module’s performance. To effectively simulate and control PV systems for a given location, it has become paramount to develop a robust and accurate model that considers how PV modules behave. This study seeks to introduce an emerging metaheuristic optimization algorithm to estimate the unknown parameters of PV modules. The strategies deployed by flying foxes in the event of high temperatures have given birth to the development of a new metaheuristic algorithm called FFO. Contrary to previous methods, this new modeling procedure makes it possible to calculate all the parameters, regardless of temperature or irradiance. Four PV modules, having different technologies, were tested to evaluate the accuracy of the algorithm in question. The effectiveness of FFO is then contrasted with other well-known metaheuristics where single and double diode models are deployed. The results show that the FFO optimizer represents a substantial and compelling substitute for PV module extraction methods.

Ranking teaching–learning-based optimization algorithm to estimate the parameters of solar models

As one of the most promising renewable energies, solar energy can be converted to electricity through photovoltaic (PV) systems. It is indispensable to identify the parameters of PV systems with the aim of controlling and simulating. Thanks to the complexity of PV systems, parameter identification is still a challenging task. In this paper, we develop a Ranking Teaching–Learning-Based​ Optimization (RTLBO) to solve the problem, in which Teaching–Learning-Based​ Optimization (TLBO) is a population-based swarm algorithm and mimics the learning process in a classroom. RTLBO ranks learners into superior and inferior groups, in which the outstanding learners learn from the top three agents to boost the local search. In contrast, the low learners learn from each other by guidance. The two phases are in parallel to balance the local and global search. The proposed RTLBO is used to extract parameters of different models, including the single diode model, double diode model and three PV module models. TLBO, four TLBO variants, and fifteen meta-heuristic algorithms are selected as the rivals of RTLBO. Several experiments have shown that our method is a reliable and effective algorithm when addressing the parameters of PV systems.

Parameters extraction of photovoltaic cells using swarm intelligence based optimization technique: research on single diode model and double diode model

Solar-energy is a clean source of energy and photovoltaic (PV) panels are constructed from solar cells (SC) which convert energy of light into electricity without any environmental effect. The researchers and policy makers focus on the huge scale adoption of solar panels due to its cleaner production. However, there is non-linear behavior in current-voltage characteristics of solar panels and shortage of data in manufacturer’s datasheet. In order to enhance the efficiency of solar panels it is mandatory to develop the PV panels scheme accurately by extracting the basic parameters. In this research study a mathematical model of two different solar cell models is used such as Single Diode Model (SDM) and Double Diode Model (DDM). The Particle Swarm Optimization (PSO) is used to extract the five and seven unknown parameters of SDM and DDM. The algorithm runs with one thousand iterations to minimize the Root Mean Square Error (RMSE) where the RMSE is the vector of five unknown parameters for SDM and seven for DDM. The superiority of proposed PSO algorithm is proved by the optimized results of unrevealed parameters with minimized RMSE of up to 10-3. Optimum parameter values for the solar cell models are applied on the real time data of a 55 mm diameter commercial RTC-France SC. Finally, the results reveal that P-V and I-V curves exhibit smallest deviation between estimated and real time values. The results reveal that the proposed PSO converges to optimal solution with least number of iterations compared to the existing metaheuristic algorithms.

Comparative performance analysis of metaheuristic search algorithms in parameter extraction for various solar cell models

In today's technology, solar energy continues to be one of the leading renewable energy sources in electricity production methods due to its abundance on the Earth's surface and its non-polluting nature. In solar photovoltaic (PV) systems, it is relatively easy to produce electricity from the sun, and their efficiency can be enhanced by accurately estimating the electrical parameters of solar cells. From this point of view, this paper has focused on solar cell design to improve the performance of solar PV systems. To achieve this target, fitness-distance balance-based stochastic fractal search (FDB-SFS), particle swarm optimization (PSO), student psychology-based optimization (SPBO), and adaptive guided differential evolution (AGDE) algorithms are employed. The experimental analysis is performed on single-diode solar cell, double-diode solar cell, and three PV modules. Considering the experimental results, it is observed that the best estimation accuracy is reached by the FDB-SFS algorithm. Accordingly, root mean square error (RMSE) values between measured and estimated data were calculated to be 9.86E-04 for the single-diode solar cell model, 9.84E-04 for the double-diode solar cell model, 2.42E-03 for the Photowatt-PWP201 module, 1.72E-03 for STM6–40/36 module, and 1.67E-02 for STP6–120/36 module. Consequently, the present paper reports that FDB-SFS is an efficient and powerful method for parameter extraction of solar photovoltaic models.

Intelligent Digital Twin Modelling for Hybrid PV-SOFC Power Generation System

Hydrogen (H2) energy is an ideal non-polluting renewable energy and can achieve long-term energy storage, which can effectively regulate the intermittence and seasonal fluctuation of solar energy. Solid oxide fuel cells (SOFC) can generate electricity from H2 with only outputs of water, waste heat, and almost no pollution. To solve the power generation instability and discontinuity of solar photovoltaic (PV) systems, a hybrid PV-SOFC power generation system has become one feasible solution. The “digital twin”, which integrates physical systems and information technology, offers a new view to deal with the current problems encountered during smart energy development. In particular, an accurate and reliable system model is the basis for achieving this vision. As core components, the reliable modelling of the PV cells and fuel cells (FCs) is crucial to the whole hybrid PV-SOFC power generation system’s optimal and reliable operation, which is based on the reliable identification of unknown model parameters. Hence, in this study, an artificial rabbits optimization (ARO)-based parameter identification strategy was proposed for the accurate modelling of PV cells and SOFCs, which was then validated on the PV double diode model (DDM) and SOFC electrochemical model under various operation scenarios. The simulation results demonstrated that ARO shows a more desirable performance in optimization accuracy and stability compared to other algorithms. For instance, the root mean square error (RMSE) obtained by ARO are 1.81% and 13.11% smaller than that obtained by ABC and WOA algorithms under the DDM of a PV cell. Meanwhile, for SOFC electrochemical model parameter identification under the 5 kW cell stack dataset, the RMSE obtained by ARO was only 2.72% and 4.88% to that of PSO for the (1 atm, 1173 K) and (3 atm, 1273 K) conditions, respectively. By establishing a digital twin model for PV cells and SOFCs, intelligent operation and management of both can be further achieved.

Analytical study of the effects of dust on photovoltaic module performance in Tehran, capital of Iran

BackgroundThe majority of photovoltaic modules are installed outdoors. The module's performance depends on environmental conditions such as solar radiation and ambient temperature. Additionally, dust accumulation on the surface of the photovoltaic modules also affects their performance. Dust deposition on the surface of the modules reduces irradiance transmittance. Regardless all efforts, dust will settle. The mass density and characteristics of the dust affect how much it affects the performance of photovoltaic modules. Researchers have tried to develop mathematical relation between mass deposition density and how it affects photovoltaic module performance in various locations of the world. MethodsThis paper presents a new mathematical way for combining the photovoltaic double-diode model and the previously established mathematical relations. The given model could be utilized to predict the influence of dust on photovoltaic modules performance. The provided model was used to five locations in Tehran, Iran, to examine how dust impacts the performance of a 200 W solar module. Significant findingsThe findings suggest that, when dust accumulation is taken into account, the south of Tehran offers better conditions for installing solar modules than other places. Dust accumulation on the photovoltaic module significantly reduces the amount of electricity produced.

Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm

AbstractIn the modeling and designing of PhotoVoltaic (PV) systems, parameter characterization in PV cell/module models remains a crucial field of research. Diode‐based models, such as single‐diode model (SDM), double‐diode model (DDM), and the three‐diode model, are frequently employed, and SDM and DDM are the most significant models. As a result, the difference between the estimated and experimental current can be minimized by using an objective function to solve the parameter characterization of such models. Metaheuristic optimization algorithms have recently been employed to get around the difficulty of finding accurate and highly reliable outcomes quickly. As a result, this research modifies the fundamental SDM and DDM and considers an objective function based on the modified models. Additionally, an improved version of a novel metaheuristic algorithm called White Shark Optimizer (WSO) is proposed by modifying the force control parameters of the WSO, and a chaotic generator is infused to improve the exploitation ability of WSO. The modified algorithm is named IWSO and applied to extract the PV parameters. This paper uses the new objective function to compare the conventional and the modified PV models. The outcomes of the experiment demonstrated IWSO's dominance over competing algorithms. With an average Freidman's ranking test value of 1.171, the proposed IWSO is superior to all selected algorithms. The average accuracy of modified SDM and DDM is 12% better than the traditional PV models. According to the findings, IWSO's estimated parameter values are the best, with the smallest difference between estimated and experimental current.

A fast and precise double-diode model for predicting photovoltaic panel electrical behavior in variable environmental conditions

A precise understanding of photovoltaic output behaviour leads to better organising of investment resources and more efficient development planning. Besides under standard test conditions, the presented model can predict the output of a photovoltaic module under variable real-time situations. The proposed hybrid analytical-numerical method declined the calculation and running time while offering an acceptable predicting accuracy. Models’ predictions were validated with the information reported by the manufacturer and experimental analysis and real-time measurements. The dust impacts were also investigated. The model accuracy was compared with a single diode model under the same conditions and several other similar works. Finally, the model was used to investigate the effect of any variation in cell temperature and irradiation levels on the output of photovoltaic modules. The proposed hybrid model can be a benchmark for future studies. It can be modified and developed to foresee the electrical output of a photovoltaic system in other regions if the system specifications and weather conditions and diversities are applied accordingly.

Black Widow Optimization Algorithm Used to Extract the Parameters of Photovoltaic Cells and Panels

The metaheuristic algorithms and their hybridization have been utilized successfully in the past to extract the parameters of photovoltaic (PV) cells and panels. The novelty of the paper consists of proposing the black widow optimization algorithm (BWOA) for the first time to identify the parameters of the two photovoltaic cells RTC France, amorphous silicon (aSi), and two photovoltaic panels PWP201, PVM 752 GaAs. The single-diode model (SDM) and double-diode model (DDM) for analyzing the PVs are considered. The performance of the BWOA is verified using four statistical tests: the root mean square error, which is the primary tool, the mean relative error, the mean bias error, and the coefficient of determination. The research results of this study are as follows: BWOA gave the same results, or very slightly better, for RTC and PWP201 for SDM in comparison with the best algorithms from the specialized literature; for all the other cases, BWOA has substantially better results, especially for PVM 752 GaAs, where the improvements in RMSE are: 16.5%, for PWP201: 6.25%, and for aSi: 5.3%, all for the DDM; the computing time is around 2 s, which is one of the lowest durations. A consistent study is made to optimize the accuracy and computational time in function of the number of iterations and population.

Optimal Parameter Estimation of Solar Photovoltaics Through Nature Inspired Metaheuristic and Hybrid Approaches

Estimation of solar photovoltaic (PV) equivalent model parameters through the manufacturer's datasheet or experimental performance current voltage (I-V) characteristics is an apprehension among researchers. In this paper, two approaches are instigated and compared i.e. metaheuristic pheromone value black widow optimisation (pv-BWO) and hybrid sailfish optimisation (h-SFO) for the estimation of a single-diode model (SDM) and double-diode model (DDM) parameters for the accurate solar PV modelling. Four distinct case studies that incorporate four different PV modules i.e. RTC France, PWP 201, SHARP ND-R250A5, and EIL 75W PV are investigated, where the validation of the proposed methods is carried out through the experimental performance characteristics of solar PV under test i.e. the I-V and power-voltage (P-V) performance characteristics. Moreover, the performance is evaluated on various error functions, and experimental I-V and P-V characteristics, where the results are also compared with the various techniques of parameter estimation in the literature. It is important to underline that the closeness of the objective function to zero exhibits the congruence of estimated parameters with the exact parameters, where the exact parameter value is not accessible. Therefore, the preciseness rests on the experimental data only, where any significant reduction in OF i.e. RMSE considered as an improvement towards the exactness of real unknown parameter values. The pv-BWO exhibits an average of 69.89% of lower error results when compared with few existing methods, whereas h-SFO recorded a higher RMSE of about 93% when compared with the pv-BWO which suggests the suitability of pv-BWO over h-SFO and few existing methods.