Parameter Extraction Of Photovoltaic Models Research Articles

An adaptive operator selection cuckoo search for parameter extraction of photovoltaic models

Accurate, reliable, and efficient extraction of photovoltaic (PV) model parameters is an essential step towards PV system simulation, control, and optimization. Nevertheless, this problem is still facing great challenges because of its intrinsic nonlinear, multivariate, and multimodal properties. In this paper, a new variant of cuckoo search (CS), adaptive operator selection CS (AOSCS), is advanced for the PV model parameter extraction problems. AOSCS includes two major improvements: (1) an adaptive operator selection mechanism is developed to automatically assign the workloads of exploration and exploitation operators, and (2) the exploration and exploitation operators used in the original CS are modified to promote the exploration capability and reduce the blindness of search, respectively. The performance of AOSCS is firstly validated on CEC 2017 test suite and then it is utilized to solve the parameter extraction problems of five PV models. Moreover, further experiments on two commercial PV modules under distinct irradiance and temperature levels are also conducted to evaluate the practicality of the proposed algorithm. It is manifested that the results yielded by AOSCS are very competitive relative to other parameter extraction approaches. Accordingly, the proposed AOSCS is able to be served as an up-and-coming candidate algorithm for PV model parameter extraction problems.

Correction: A new modified version of mountain gazelle optimization for parameter extraction of photovoltaic models

Correction: A new modified version of mountain gazelle optimization for parameter extraction of photovoltaic models

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.

Archimedes optimization algorithm based parameter extraction of photovoltaic models on a decent basis for novel accurate RMSE calculation

Solar photovoltaic (PV) technology stands as a promising alternative to conventional fossil fuel-based power generation, offering pollution-free and low-maintenance energy production. To harness its potential effectively, understanding the power generation process and accurately modeling solar PV systems are essential. Unfortunately, manufacturers often do not provide the necessary parameters for modeling solar cells, making it challenging for researchers. This research employs the Archimedes Optimization Algorithm (AOA), an optimization technique, to determine unknown parameters for the PVM752 GaAs thin film solar cell and the RTC France solar cell. The modeling of these solar cells utilizes both a Single Diode Model (SDM) and a Double Diode Model (DDM). Performance evaluations are conducted using the sum of individual absolute errors (SIAE) and a novel root mean square error (RMSE) method. Comparing the effectiveness of the AOA with other optimization methods, The RMSEs for the AOA applied to the SDM and DDM of RTC France solar cell were 3.7415 × 10–3 and 1.0033 × 10–3. Similarly, for PVM752 GaAs thin film solar cell were 1.6564 × 10–3, and 0.00106365, respectively. The SIAE values for both solar diode models of RTC France cells were 0.071845 and 0.021268, respectively. For the PVM752 GaAs thin film, the corresponding SIAE values were 0.031488 and 0.040224. The results highlight the efficiency of the AOA-based approach, showcasing consistent convergence and a high level of accuracy in obtained solutions. The suggested approach produces superior results with a lower RMSE compared to other algorithms, demonstrating its efficacy in determining solar PV parameters for modeling purposes.

Parameter Extraction of Photovoltaic Models by Honey Badger algorithm and Wild Horse Optimizer

Analyzing the processes ranging from the determination of the installation configuration of the photovoltaic (PV) systems to the operation at the maximum power, from the technical and economic feasibility study to the positive contribution to the region where the production is planned are just possible with the accurate and efficient simulation models of the PV systems. PV parameter extraction, which is a topic frequently discussed recently, is crucial for the detailed modeling of PV cells and modules and simulating the behavior of these systems. For this reason, the current study examined PV parameter extraction and solved this optimization problem with the honey badger algorithm (HBA) and wild horse optimizer (WHO). PV cells and modules were modeled with the single diode model (SDM) and double diode model (DDM) and tested with actual measurement data. The root-mean-square error (RMSE) was chosen as the objective function, and the results were compared with the evaluation metrics for computational accuracy and time. Based on four PV model results, RMSE values were calculated between 9.9318E-04 to 1.7011E-03 for HBA and between 9.8602E-04 and 1.7298E-03 for WHO. As a result, even though both algorithms produce successful, stable, and fast results in PV parameter extraction, the WHO yielded better results.

Parameter extraction of photovoltaic models using atomic orbital search algorithm on a decent basis for novel accurate RMSE calculation

In the available literature, there are several methods and techniques are employed to extract the unknown parameters of solar cell. The performance of the method used is validated on the basis of Root Mean Square Error (RMSE) value. This work aims to promote a decent basis for proper investigation and the implementation of an optimization algorithm to extract solar unknown parameters. In this work, the true value of RMSE is calculated for the single diode model of the RTC France solar cell using 30 different methods published in several literatures. The obtained results show that the approach of calculating RMSE is not precise in most of the published literature. The Atomic Orbital Search algorithm is proposed as a new metaheuristic optimisation method to extract the parameters of a single diode, double diode and three diode models for RTC France and PVM752 GaAs thin film solar cell. Last, the performance of the proposed algorithm is compared with other different optimisation algorithms. It is observed that the proposed algorithm gives best results with less RMSE as compared to other algorithms. This work intends to provide a solid foundation for thorough analysis and implementation of an optimization technique to extract unknown solar parameters.

An Enhanced Moth-Flame Optimization with Multiple Flame Guidance Mechanism for Parameter Extraction of Photovoltaic Models

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.

A Chaotic Second Order Oscillation JAYA Algorithm for Parameter Extraction of Photovoltaic Models

In order to identify the parameters of photovoltaic (PV) cells and modules more accurately, reliably and efficiently, a chaotic second order oscillation JAYA algorithm (CSOOJAYA) is proposed. Firstly, both logical chaotic map and the mutation strategy are brought in enhancing the population diversity and improving exploitation. Secondly, in order to better balance exploration and exploitation, the second order oscillation factor is added, which not only improves the diversity of the population, but also has strong exploration at the beginning of the iteration and strong exploitation at the end of the iteration. In order to balance the two abilities, the self-adaptive weight is introduced into the CSOOJAYA algorithm to regulate individuals the tendency of moving toward the optimal solution and escaping from the worst solution, so as to enhance the search efficiency and exploitation. In order to validate the behavior of CSOOJAYA, it is employed to the parameter identification problem of PV models. Finally, the experimental results show that CSOOJAYA delivers excellent behavior in the aspects of convergence, reliability and accuracy.

A chaotic self-adaptive JAYA algorithm for parameter extraction of photovoltaic models.

In order to have the highest efficiency in real-life photovoltaic power generation systems, how to model, optimize and control photovoltaic systems has become a challenge. The photovoltaic power generation systems are dominated by photovoltaic models, and its performance depends on its unknown parameters. However, the modeling equation of the photovoltaic model is nonlinear, leading to the difficulty in parameter extraction. To extract the parameters of the photovoltaic model more accurately and efficiently, a chaotic self-adaptive JAYA algorithm, called AHJAYA, was proposed, where various improvement strategies are introduced. First, self-adaptive coefficients are introduced to change the priority of information from the best search agent and the worst search agent. Second, by combining the linear population reduction strategy with the chaotic opposition-based learning strategy, the convergence speed of the algorithm is improved as well as avoid falling into local optimum. To verify the performance of the AHJAYA, four photovoltaic models are selected. The experimental results prove that the proposed AHJAYA has superior performance and strong competitiveness.

An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and JAYA algorithm for parameter extraction of photovoltaic models.

In order to maximize the acquisition of photovoltaic energy when applying photovoltaic systems, the efficiency of photovoltaic system depends on the accuracy of unknown parameters in photovoltaic models. Therefore, it becomes a challenge to extract the unknown parameters in the photovoltaic model. It is well known that the equations of photovoltaic models are nonlinear, and it is very difficult for traditional methods to accurately extract its unknown parameters such as analytical extraction method and key points method. Therefore, with the aim of extracting the parameters of the photovoltaic model more efficiently and accurately, an enhanced hybrid JAYA and Rao-1 algorithm, called EHRJAYA, is proposed in this paper. The evolution strategies of the two algorithms are initially mixed to improve the population diversity and an improved comprehensive learning strategy is proposed. Individuals with different fitness are given different selection probabilities, which are used to select different update formulas to avoid insufficient using of information from the best individual and overusing of information from the worst individual. Therefore, the information of different types of individuals is utilized to the greatest extent. In the improved update strategy, there are two different adaptive coefficient strategies to change the priority of information. Finally, the combination of the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed of the algorithm and ability to escape from local optimum can be improved. The results of various experiments prove that the proposed EHRJAYA has superior performance and rank in the leading position among the famous algorithms.

Parameter extraction of photovoltaic models using a memory-based improved gorilla troops optimizer

The parameter extraction of the PV model is a challenging issue owing to its multi-model and nonlinear characteristics. Moreover, these characteristics of the problem render the algorithms tackling it susceptible to being stuck in local optima. Nevertheless, it is imperative to accurately estimate the parameters due to their significant impact on the efficiency of the Photovoltaic (PV) system in terms of current and power generation. Hence, this paper proposes a new metaheuristic algorithm called the Memory-based Improved Gorilla Troops Optimizer (MIGTO). Furthermore, the Explorative Gorilla with an Adaptive Mutation Mechanism (EGAMM) and the Gorilla memory-saving technique are two major improvements that support the MIGTO superiority. EGAMM primarily comprises two main operators (explorative gorilla operator, adaptive mutation operator). The explorative gorilla operator enhances the search capability to explore new gorillas’ positions in the search space to escape from local optima with a small probability of 0.1. Otherwise, MIGTO updates the new gorilla position using the adaptive mutation operator by selecting two random gorillas. Additionally, the GMS technique promotes the quality of the gorillas by keeping a history of the previous positions to be compared to the current ones. Several experiments are conducted between MIGTO and other well-known metaheuristic algorithms. The experimental results and statistical analyses prove the outperformance of MIGTO in effectively extracting the parameters of different PV models, including the single diode model, the double diode model, the triple diode model, and the photovoltaic module model. Additionally, the performance of the algorithms is tested using the manufacturer’s datasheet.

Parameter extraction of photovoltaic models using a comprehensive learning Rao-1 algorithm

The environmental impact and scarcity of fossil fuels have led to a tremendous increase in the demand and use of renewable energy resources. The use of photovoltaic (PV) energy is due to several benefits, such as low maintenance and operating costs. The modeling of the PV systems process requires the identification of PV cells parameters, which can be formulated in an optimization problem. This problem is a very challenging task as it is nonlinear and multimodal. Therefore, metaheuristic algorithms suggested recently may present unsatisfactory results at the level of solution quality and convergence speed. To ensure the effectiveness of any algorithm, the equilibrium between exploration and exploitation must be guaranteed. To achieve an accurate model of the photovoltaic cells, a comprehensive learning Rao-1 (CLRao-1) optimization algorithm is proposed in this work. Rao-1 is chosen due to its simplicity, easy comprehension, and implementation. The suggested modifications based on three mutually exclusive search equations do not affect its easiness. The effectiveness of our algorithms is assessed through PV cells and three modules. The results of the experimental and simulated data were in high agreement. Moreover, our algorithm achieved the best results in terms of reliability and accuracy.

An improved gaining-sharing knowledge algorithm for parameter extraction of photovoltaic models

The optimal operation of solar cells depends on the accurate determination of parameters in the Photovoltaic (PV) models, such as resistance and currents, which may vary due to unstable weathers conditions and equipment aging. The precise selection of these parameters resembles a multi-variable, nonlinear and multi-modal problem. Despite a few parameter extraction techniques being available to solve such a problem, more-accurate and advanced solutions still present a challenging research question. This paper therefore proposes an improved gaining-sharing knowledge (IGSK) algorithm to accurately and precisely extract the parameters of PV models. The improvement in the classical GSK algorithm is incorporated by introducing an adaptive mechanism to automatically adjust the value of the knowledge rate parameter. This adaptive mechanism ensures the balance between the number of dimensions updated by the junior gaining-sharing phase and the number of dimensions updated by the senior gaining-sharing phase. A bound-constraint handling method is also presented and a linear population size reduction technique is used to boost the speed of convergence and to maintain a tared-off between the exploration and exploitation properties. The efficacy of the proposed IGSK has been demonstrated by considering three different PV modules models, i.e., single diode, double diode, and PV modules and two other commercial ones (Thin Film ST40 and Mono-crystalline SM55). For those modules, the proposed IGSK receptively produces the following outcomes: 0.00098602188, 0.0009827277, 0.0024250749, 0.0017298137, and 0.016600603. The statistical obtained results demonstrate that the IGSK indicates competitive or even better performance on convergence speed, accuracy and reliability compared with other competing techniques. Therefore, the proposed approach is believed to be an effective and efficient alternative for parameter extraction of PV models.

A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models

Photovoltaic (PV) cells are widely used for their clean and sustainable advantages, forcing researchers to accurately model their characteristics. The behavior of PV cells can be derived from their current–voltage characteristics, depending on their unknown circuit model parameters. Due to the simulation, evaluation, control, and optimization of PV systems, it is essential to accurately and reliably extract the parameters of PV models. However, because of the non-linear, multi-variable, and multi-modal characteristics, it is still a very challenging task. With the rapid development of intelligent computing, various meta-heuristic algorithms have been devoted to extracting the parameters of different PV models. The purpose of this paper is to comprehensively review the meta-heuristic algorithms and their related variants that have been used to extract the parameters of different PV models. Different from the existing research works, this paper presents a comprehensive review based on the reliability, robustness, computational resources, and time complexity of the algorithm. These features are essential to design an algorithm for efficient parameter extraction of PV models. Based on the conducted review, some useful recommendations are provided, which have important reference significance when designing the new parameter extraction methods of PV models and are of great significance for further improving the performance, control, and design of PV cells.

Parameter Extraction of Photovoltaic Models Using a Dynamic Self-Adaptive and Mutual- Comparison Teaching-Learning-Based Optimization

Parameter extraction of solar cell models plays an important role in the simulation, evaluation, control, and optimization of the photovoltaic (PV) system. Although many meta-heuristic algorithms have been proposed to solve the parameter extraction, it is necessary to further improve the accuracy and reliability of these algorithms. In this paper, an optimized teaching-learning-based optimization (TLBO) is proposed, namely dynamic self-adaptive and mutual-comparison teaching-learning-based optimization (DMTLBO). DMTLBO enhances the basic TLBO by improving its teacher phase and learner phase: (i) In the teacher phase, two differentiated and personalized teaching strategies are proposed according to learners’ learning status. In these two strategies, an adaptive state transition weight factor $\omega $ and a dynamic gap weight factor $\beta $ are introduced to reflect the dynamic transformation of the learners’ learning state in the actual teaching situation. (ii) In the learner phase, a new learning strategy is proposed. The learner can communicate and learn with three different learners who are randomly selected and ranked. To verify the performance of the DMTLBO algorithm, it is used to extract the parameters of different PV models, such as the single diode model, the double diode model, and three PV modules. Among these PV models, the root mean square error values between the measured data and the calculated data of DMTLBO are 9.8602E-04 ± 2.07E-17, 9.8248E-04 ± 1.53E-06, 2.4251E-03 ± 2.15E-17, 1.7298E-03 ± 5.74E-14, and 1.6601E-02 ± 4.55E-10, respectively. Compared with other optimization algorithms, the experimental results show that DMTLBO can provide better or highly competitive convergence speed and extraction accuracy. Besides, the influence of the improved teacher phase and learner phase on DMTLBO and the changing process of both weight factors $\omega $ and $\beta $ are investigated.

Generalized normal distribution optimization and its applications in parameter extraction of photovoltaic models

The accuracy of extracting the unknown parameters of photovoltaic models is closely related with the effectiveness of modeling, simulating, and controlling photovoltaic systems. Metaheuristics have been widely used for improving the accuracy of extracting the unknown parameters of photovoltaic models. Despite the success of such techniques in this application area, they require parameter adjustment, which will restrict their applications especially for non-expert users. This is the motivation of this work, in which a novel metaheuristic is proposed called generalized normal distribution optimization, the proposed method is inspired by the generalized normal distribution model; each individual uses a generalized normal distribution curve to update its position. Unlike the majority of metaheuristics, the proposed method only needs the essential population size and terminal condition to solve optimization problems. In order to benchmark the performance of the proposed method, it is employed to extract the unknown parameters of three photovoltaic models including single diode model, double diode model and photovoltaic module model. The solutions obtained by the proposed method are compared with those of ten state-of-the-art metaheuristic algorithms and some recent reported solutions. Experimental results demonstrate the excellent performance of the proposed method for parameter extraction of the applied photovoltaic models in terms of quality and stable of the obtained solutions.11The souce code of GNDO is publicly available at https://au.mathworks.com/matlabcentral/fileexchange/79526-the-source-code-for-gndo.

Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models

Given strong global search ability and less sensitive to initial solutions, many metaheuristic algorithms have been successful used to extract the unknown parameters of photovoltaic (PV) models. However, most applied metaheuristic algorithms need extra control parameters except the essential population size and terminal condition. For unknown optimization problems, how to set these control parameters to get the optimal solutions is a great challenge. To overcome this challenge, this paper presents a novel metaheuristic algorithm called comprehensive learning Jaya algorithm (CLJAYA) for parameter extraction of PV models. CLJAYA is a new variant of Jaya algorithm, which enhances global search ability of Jaya algorithm by the designed comprehensive learning mechanism. CLJAYA has a simple structure and only needs the essential population size and terminal condition for optimization. To verify the effectiveness of the improved strategies, CLJAYA is first employed to solve the well-known CEC 2015 test suite. Then the performance of CLJAYA is investigated by extracting the unknown parameters of three PV models including single diode model, double diode model and PV module model. Experimental results prove the superiority of CLJAYA on these test cases in terms of accuracy and efficiency by comparing with Jaya algorithm and other competitive algorithms.

Parameter extraction of photovoltaic models using an enhanced Lévy flight bat algorithm

In this paper, a modified version of the bat algorithm (BA), called enhanced Lévy flight bat algorithm (ELBA), is proposed for accurate and efficient parameter extraction of different photovoltaic (PV) models from experimental data. Typically, it is formulated as a multimodal nonlinear optimization problem in which the objective function is to minimize the root mean square error verified between the real data and the simulated ones by the PV model at hand, considering certain values for its parameters. In addition, the constraints are associated to the lower and upper bounds of these parameters. From the computational perspective, the main innovations of ELBA lies in the: (i) introduction of a specific mathematical expression to enhance the diversification of new solutions; (ii) adoption of a mathematical expression based on the Lévy flight to perform an effective local search; and (iii) selection of new equations for updating certain control parameters, which provide a better balance between the exploration and exploitation mechanisms of the algorithm. Simulation results comprehensively demonstrate that ELBA has a very competitive performance in terms of effectiveness, robustness, stability, convergence speed and time of simulation, in relation to other state-of-the-art metaheuristic algorithms. Therefore, the major contribution of this paper is the ELBA, a modified metaheuristic algorithm which proves to be a promising tool for parameter extraction of different PV models from experimental data.

An enhanced adaptive differential evolution algorithm for parameter extraction of photovoltaic models

Parameter extraction of photovoltaic models based on measured current-voltage data plays an important role in the simulation, control, and optimization of photovoltaic systems. Although many parameter extraction techniques have been devoted to solving this problem, they may suffer from some deficiencies. In this paper, an enhanced adaptive differential evolution algorithm is proposed to extract photovoltaic parameters fast, accurately and reliably. In proposed method, the crossover rate sorting mechanism is introduced to assign each individual to an adapted crossover rate value according to their fitness values, which allows good elements to be more inherited in next generation. In addition, a dynamic population reduction strategy is used to improve the convergence speed and balance the exploration and exploitation. The performance of proposed method is confirmed by extracting parameters of different photovoltaic models, i.e., single diode, double diode, and photovoltaic modules. The simulated results show that the proposed method exhibits competitive performance on accuracy, reliability and convergence speed compared with other state-of-the-art algorithms. Further, the test results on experimental data from the manufacturers data sheet also indicate that the proposed algorithm can obtain superior solutions at different irradiance and temperature. Therefore, the proposed method can be an effective and efficient alternative for parameter extraction of photovoltaic models.

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

Extracting accurate values for involved unknown parameters of solar photovoltaic (PV) models is very important for modeling PV systems. In recent years, the use of metaheuristic algorithms for this problem tends to be more popular and vibrant due to their efficacy in solving highly nonlinear multimodal optimization problems. The whale optimization algorithm (WOA) is a relatively new and competitive metaheuristic algorithm. In this paper, an improved variant of WOA referred to as MCSWOA, is proposed to the parameter extraction of PV models. In MCSWOA, three improved components are integrated together: (i) Two modified search strategies named WOA/rand/1 and WOA/current-to-best/1 inspired by differential evolution are designed to balance the exploration and exploitation; (ii) a crossover operator based on the above modified search strategies is introduced to meet the search-oriented requirements of different dimensions; and (iii) a selection operator instead of the “generate-and-go” operator used in the original WOA is employed to prevent the population quality getting worse and thus to guarantee the consistency of evolutionary direction. The proposed MCSWOA is applied to five PV types. Both single diode and double diode models are used to model these five PV types. The good performance of MCSWOA is verified by various algorithms.