Solar Photovoltaic Models Research Articles

A new method for parameter extraction of solar photovoltaic models using gaining–sharing​ knowledge based algorithm

For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively establish an equivalent model of PV cell and extract the relevant unknown model parameters accurately. This paper introduces a new metaheuristic algorithm, i.e., gaining-sharing knowledge based algorithm (GSK) to solve the solar PV model parameter extraction problem. This algorithm simulates the process of knowledge acquisition and sharing in the human life cycle and is with strong competitiveness in solving optimization problems. It includes two significant phases. The first phase is the beginner–intermediate or junior acquisition and sharing stage, and the second phase is the intermediate–expert or senior acquisition and sharing stage. In order to verify the effectiveness of GSK, it is applied to five PV models including the single diode model, double diode model, and three PV modules. The influence of population size on the algorithm performance is empirically investigated. Besides, it is further compared with some other excellent metaheuristic algorithms including basic algorithms and advanced algorithms. Among the five PV models, the root mean square error values between the measured data and the calculated data of GSK are 9.8602E−04 ± 2.18E−17, 9.8280E−04 ± 8.72E−07, 2.4251E−03 ± 1.04E−09, 1.7298E−03 ± 6.25E−18, and 1.6601E−02 ± 1.44E−16, respectively. The results show that GSK has overall better robustness, convergence, and accuracy.

A new emperor penguin optimisation‐based approach for solar photovoltaic parameter estimation

Mathematical model parameter estimation of solar photovoltaic (PV) cell, module and array represent a prodigious challenge in recent researches, where analytical, metaheuristic and hybrid techniques play a vital role for parameter extraction by using manufacturer's datasheet and experimental data, although the precise and highly reliable solution identification is still a complex problem to be solved. In this paper, a new Emperor Penguin Optimisation- (EPO) based parameter estimation approach for a single-diode Rp solar PV model is presented that is cohesively analysed under different sets of temperature and irradiance (G). Validation of the proposed technique is perceived on the analysis of performance characteristics, that is, current-voltage (I-V) and power-voltage (P-V) of KC200GT, PWP201, and STP6 120-36 PV modules under various simulation conditions. Moreover, estimated results are compared with the experimental data and several established estimation techniques in the literature for validation and demonstrate the proposed technique with highly precise outcomes having reduced computational cost for the PV model parameter extraction problem.

Triple diode parameter estimation of solar PV cell using hybrid algorithm

Solar photovoltaic (PV) systems are now one of the most prominent green energy technologies for producing a significant proportion of electricity. With the increased attention towards solar PV-based systems, the effective and precise estimation of PV cell parameters has received considerable attention from researchers. Extracting the parameters of the solar PV model is quite important for precise modelling, assessment and control of the PV system. Mostly theoretical, computational and meta-heuristic in the last few years have been proposed which extract parameters of PV cell based on the experimental results. Extracting PV model parameters, however, remains a major challenge. The application of a new hybrid algorithm that relies on the Grey Wolf Optimizer and Cuckoo Search Algorithm is being proposed in this manuscript to extract the parameters of various PV cell models. The parameter optimization results are obtained using GWOCSA and are further compared with those obtained with five other algorithms, i.e. PSO, MVO, SCA, CSA, and GWO. The complete error analysis is carried out for TDM of PV cells to establish the superiority of GWOCSA. The superiority of proposed algorithm is established using ranking test, statistical error analysis, and sensitivity temperature variation.

A framework for optimization of energy efficiency and integration of hybridized-solar energy in agro-industrial plants: Bioethanol production from cassava in Ghana

This study presents a methodological framework for auditing, optimization as well as the integration of hybridized solar energy in Ghana’s agro-industrial sector. The Functional Analysis System Technique was introduced for systematically translating plant-wide energy requirements into detailed functional and performance design criteria for energy efficiency, optimization, and solar energy integration. The results of the case study indicated that insulating the fermenter and distiller as well as using energy-efficient rasper, pumps and cables reduced energy consumption by 41.8%. The cost of integrating solar thermal energy was 31.8% higher than solar photovoltaic power due to a lack of subsidies in the sector. A 250 kW solar photovoltaic model was designed with a performance ratio of 75% and a solar hot water system with a thermal fraction of 91% for electricity and process water respectively. The framework, therefore, presents an invaluable tool for optimizing energy efficiency and integrating solar thermal and solar photovoltaic energy sources for the agro-industrial sector.

Design of Intelligent Controller for Hybrid PV/Wind Energy Based Smart Grid for Energy Management Applications

This article deals in the modelling of intelligent controller for the Hybrid photovoltaic (PV)/Wind based smart grid system. With the development of solid state electronics also power systems intelligent techniques like neural networks, fuzzy logic, expert systems take part an essential contribution in design of controller for the smart grid. The above techniques give potential tools for design and development of fault detection controller in the advanced smart grid (SG). The intelligent techniques attained in rapid evolution during previous decades and their utilization now increased rapidly in current industrial environment. Renewable energy integration, participation of customers, performance optimization is important key features of digital grid power systems. In this article presents in new method of digital grid for hybrid photovoltaic/Wind based smart grid system were developed. It consists of solar PV array systems, wind power systems, asynchronous generator, converters, MPPT systems, fuzzy logic controllers (FLC). The model was performed through MATLAB/SIMULINK environment. The dynamic performance in presented model was tested in various operating conditions. Sunlight Irradiation, heat and rate of wind statistics was collected in a power grid combined solar PV model placed in main system. In this proposed simulation model also in supervision scheme give an appropriate tool for optimization of smart grid performance.

A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models

Photovoltaic (PV) generation systems are vital to the utilization of the sustainable and pollution-free solar energy. However, the parameter estimation of PV systems remains very challenging due to its inherent nonlinear, multi-variable, and multi-modal characteristics. In this paper, we propose a state-of-the-art optimization method, namely, directional permutation differential evolution algorithm (DPDE), to tackle the parameter estimation of several kinds of solar PV models. By fully utilizing the information arisen from the search population and the direction of differential vectors, DPDE can possess a strong global exploration ability of jumping out of the local optima. To verify the performance of DPDE, six groups of experiments based on single, double, triple diode models and PV module models are conducted. Extensive comparative results between DPDE and other fifteen representative algorithms show that DPDE outperforms its peers in terms of the solution accuracy. Additionally, statistical results based on Wilcoxon rank-sum and Friedman tests indicate that DPDE is the most robust and best-performing algorithm for the parameter estimation of PV systems.

Modeling of the ND 240QCJ SHARP photovoltaic solar module and study the influence of the variation of the parameters

Solar energy is inexhaustible on a human scale and freely available in very large quantities. In addition, during the operational phase, the production of electricity by means of photovoltaic panels is not polluting. The modeling, simulation and analysis of the solar photovoltaic (PV) generator is a vital phase before mounting the PV system at any location, which helps to understand the behavior and characteristics under real climatic conditions of this location and even physical too. This paper presents an easy and accurate method of modeling photovoltaic arrays. The photovoltaic array model can be simulated with any circuit simulator, in this article we have applied the Simulink Matlab as software for PV model simulation in all stages. The objective of this study is the modeling and simulation of the electrical operation of a solar PV model in the standard conditions and variants whether environmental such as temperature and irradiation or physical such as series resistance and shunt and clarification influence of these variants on system behavior. In our analysis, we designed a PV system where the PV generator is the ND-240QCJ 240 watt module manufactured by SHARP Solar Electricity producing, under standard test conditions (STC), a peak power of 240 W, an optimum current of 8.75 A and an optimum voltage of 37.5 V, is used as a reference.

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I) – voltage (V) and power (P) – voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer’s datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1.8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite .

A fuzzy rule based control algorithm for MPPT to drive the brushless dc motor based water pump

In this paper, a solar photovoltaic model integrated with brushless DC motor via DC to DC zeta converter is controlled in two stage. In first stage, a fuzzy rule based maximum power point tracking (PPT) is proposed to generate the pulse for DC to DC zeta converter. It is efficient intelligent control approach to extract maximum power from the solar PV system and enhance the speed to track the maximum power. The basic three process of fuzzy logic controller (FLC) are fuzzifier, inference and defuzzifier where the defuzzification process is used center of gravity (COG) method to convert its original value. The FLC to extract maximum PPT for solar PV based brushless DC motor can be examined the performance under transient and dynamic condition with different solar insolation. Moreover, in second stage a trapezoidal control approach based electronic commutation is chosen to generate the pulses of voltage source inverter (VSI) and it offers the smooth control of the brushless DC motor which can easily applicable for water pumping or irrigation purpose. A second stage, trapezoidal control approach is close loop control algorithm using sensorless drive. The performance of proposed fuzzy rule based control algorithm is shown using simulation results on MATLAB platform.

Multidisciplinary design of high altitude airship based on solar energy optimization

Solar energy is a key factor for high altitude airships to achieve long endurance flight. In order to receive more solar energy and improve the endurance performance of airships, this paper presents a multidisciplinary design methodology to obtain an optimal configuration of the high altitude airship considering the energy optimization. First, a parameterized shape of the airship is proposed and the aerodynamic characteristics are obtained and integrated through a kriging model. Then the solar radiation and photovoltaic (PV) array models are introduced and validated by a ground experiment. After elaborating the structure, propulsion and energy subsystems, an external-internal loop optimization process is carried out to minimize the total weight considering the maximum solar power from PV array. The result of the multidisciplinary design shows an obvious increase on the received solar energy to 3.36 GJ compared with 0.45 GJ for the traditional design, which has a great potential to save the weight of the PV array laid on the airship surface.Further, the effects of the latitudes, dates and wind conditions on airship endurance performance are analyzed and in order to achieve long endurance flight, an adaptability discussion is carried out through a multi-objective optimization process. The result indicates that the improved design has a higher adaptability ratio in different environments compared with the single input design. It suggests that an adaptability design of high altitude airships might be an effective approach in airship engineering applications.

Parameter Extraction of Solar Photovoltaic Models Using Enhanced Levy Flight Based Grasshopper Optimization Algorithm

Recently, solar photovoltaic (PV) systems are becoming the tendency theme motivating researchers focus. The appropriate design of PV cells is an important task, challenged by the development of a useful model able to simulate the current vs voltage characteristics of the real solar cell and the accurate estimation of the PV cell’s parameter values. This paper proposes an improved Levy flight based grasshopper optimization algorithm (LGOA) to estimate the parameters of three PV models, i.e., single diode, double diode, and PV module. The incorporation of Levy flight trajectory to the basic grasshopper optimization algorithm (GOA), ensure solutions diversity and enhances the exploration and exploitation capabilities as well. To further validate its effectiveness LGOA is applied to the Sharp ND-R250A5 module under different operating conditions of irradiance and temperature. Experimental results demonstrate that LGOA has the ability to extract the parameters of PV models with high performance and good accuracy compared to the standard GOA.

Parameter extraction of solar photovoltaic models with an either-or teaching learning based algorithm

This paper presents an advanced variant of teaching learning based algorithm (TLBO) called either-or teaching learning based algorithm (EOTLBO) to extract accurate and reliable parameters of solar photovoltaic (PV) models. EOTLBO synergizes three enhanced strategies to accelerate the convergence rate and boost the search efficiency of TLBO. (i) A median learner based teacher phase excluding the mean position used in the original TLBO is designed to avoid infeasible and inefficient learners and to form a rational and advisable moving mechanism around the teacher. (ii) A higher-achieving learner based learner phase using three sorted learners is devised to directionally guide the target learner to jump out of local optima and to move towards a more promising region. (iii) A chaotic map based either-or teaching-learning strategy is developed to give each dimension of each learner a chance to go through either the median learner based teacher phase or the higher-achieving learner based learner phase. EOTLBO is applied to three PV cells/modules including seven cases. Compared with the original TLBO, four non-TLBO variants, and five TLBO variants, experimental results verify the superior performance of EOTLBO in terms of both the quality of final solutions and the convergence speed on all cases. In addition, the current-voltage characteristics yielded by EOTLBO agree well with the measured data independently of different PV models at different operating conditions.

Horizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models

An improved Harris hawks optimization is proposed in this work to facilitate the simulation of an efficient photovoltaic system and extraction of unknown parameters, which combines horizontal and vertical crossover mechanism of the crisscross optimizer and Nelder-Mead simplex algorithm, named CCNMHHO. In CCNMHHO, the cores appeared in the crisscross optimizer are utilized to enrich the information exchange between the individuals and avoid the problem of dimensional stagnation of individuals all through the iterations. Hence, it enhances to change to improve the population quality and prevent the shortcoming of falling into a local optimum. In contrast, the Nelder-Mead simplex algorithm is employed in the proposed CCNMHHO methodology. Nelder-Mead simplex helps to improve individual searching capabilities in performing the local search phase and showing a faster convergence to optimal values. Compared to some algorithms that have a competitive performance in dealing with this type of problem, CCNMHHO has a faster convergence speed, and it shows high stability. In different environments, the experimental data obtained by this improved Harris hawks Optimization can reveal a high agreement with the measurement data. The experimental results show that the proposed method not only is very competitive in extracting the unknown parameters of different PV models compared to other state-of-the-art algorithms but also perform well in dealing with the complex outdoor environments such as different temperature and radiance. Therefore, we observed that the CCNMHHO could be considered as a reliable and efficient method in solving a class of cases for the assessment of unknown parameters of solar cells and photovoltaic models. For post-publication guidance, supports, and materials for this research, please refer to the supporting homepage: http://aliasgharheidari.com.

Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-based differential evolution

Photovoltaic (PV) system as a vital element in the utilize of solar energy, its optimization, control, and simulation are significant. The performance of the PV system is mainly influenced by its model parameters that are varying and unavailable, thus identifying these model parameters is always desired. However, accurate and robust parameters estimation of PV models brings great challenges to the existing methods, since the complicated characteristics when estimating the parameters. Hence, to efficiently provide accurate parameters for the PV model, this study develops a self-adaptive ensemble-based differential evolution algorithm. Three different mutation strategies with different properties are combined into two groups for updating each individual. Furthermore, in order to make the best of different mutation strategies, a self-adaptive scheme is suggested to equilibrate population diversity and convergence, by adjusting the proportion of the mutation strategies used in the population. To evaluate the performance of SEDE, it is used to obtain the parameters of three PV models and compared with other well-established algorithms. Systematic comparison results indicate that SEDE is capable of estimating the model parameters with higher efficiency.

Verification of Utility-Scale Solar Photovoltaic Plant Models for Dynamic Studies of Transmission Networks

In recent years, there has been a growing need for accurate models that describe the dynamics of renewable energy sources, especially photovoltaic sources and wind turbines. In light of this gap, this work focuses on the validation of standard dynamic models developed by the Western Electricity Coordinating Council (WECC), using actual measurements from the Western Texas and Southern California transmission networks. The tests are based on the North American Electric Reliability Corporation compliance standards and include dynamic stability tests for volt-varcontrol and primary frequency response. Through an extensive set of field tests, we show that the WECC generic models can be used to simulate real dynamic phenomena in large-scale solar photovoltaic power plants, and we propose guidelines for correct usage of these models. The results show that the WECC models are especially accurate when the photovoltaic system is connected with a low impedance to the main network. We also show that the tested WECC models successfully predict the frequency response of an actual grid event that occurred in the Electric Reliability Council of Texas and which resulted in a loss of nearly 1.365 GW. This result supports the use of these models in the study of large-scale dynamic phenomena that include renewable energy sources.

Extraction of uncertain parameters of a single-diode model for a photovoltaic panel using lightning attachment procedure optimization

The aim of this work is to improve the estimation of parameters of solar photovoltaic models. An approach based on lightning attachment procedure optimization, which takes into account the uncertainties of measurements, is proposed. The approach includes three steps: the extraction of the parameters in a conventional manner, the determination of the partial uncertainties of all the parameters, and finally the determination of the instantaneous parameters based on the results of the first two steps. To validate the proposed theoretical developments, the approach is applied to four different photovoltaic parameter estimation problems. The results obtained are compared with those given by well-established algorithms to confirm the effectiveness of the proposed method, revealing that the proposed method exhibits very effective performance with a root-mean-square error on the order of 1 × 10−17, compared with 1 × 10−4 for existing literature methods.

A new metaphor-less algorithms for the photovoltaic cell parameter estimation

The performance of the solar photovoltaic (PV) system can be improved by an accurate modelling of the solar cells, but cell modelling is inaccurate due to the lack of precise solar cell parameters. To model a reliable solar PV cell, the required parameters will not be provided in the manufacturer’s datasheet. Therefore, it’s necessary to estimate the required parameters adequately. Thereby, with this observation, in this paper, proposed a simple multi-objective optimization algorithm to estimate the cell parameters. Various optimization algorithms address this issue; however, most of the algorithms produce suboptimal results due to local minima and premature convergence. So, this paper proposes two simple metaphor-less algorithms named as Rao-2 (R-II), and Rao-3 (R-III) algorithm to estimate the PV cell parameters. The performance of the proposed algorithms is compared with other well-known optimization algorithms to show the proficiency of the proposed algorithms. To validate the performance of the proposed algorithms, the estimated parameters are compared with experimental results, including statistical analysis. Moreover, from the results, it can be judged that the proposed algorithms are more suitable for the estimation of three types of solar PV models effectively.

A Novel Method for Optimizing Power Efficiency of a Solar Photovoltaic Device

Most recently, photovoltaic energy has made an incredible technological advancement for the forthcoming decades towards mitigating the ever-increasing energy demand worldwide through generating electric power. Present paper proposes a novel solar photovoltaic (SPV) device model that achieves optimal power efficiency from simulation and graphical performance analysis of SPV device characteristics. First of all, power as well as current performances is compared for varying irradiance and temperatures circumstances. Then, output current characteristics of the SPV device for the proposed as well as existing model with variable temperatures is plotted. Later, power versus voltage performances of a SPV device for the proposed model with varying irradiance and temperature criterions is compared. Finally, power–voltage characteristics are plotted graphically for the existing as well as proposed SPV device model that achieves significant amount of output power for the proposed model than the existing model and optimal power efficiency is obtained for the novel SPV device model.

Extraction of uncertain parameters of single-diode model of a photovoltaic panel using simulated annealing optimization

In this article, our goal is to improve the estimation of the parameters of solar photovoltaic models, we propose a method based on Simulated Annealing (SA) Optimization, the proposed algorithm takes into account the uncertainties of measurements. This algorithm consists of three steps such as the extraction of the parameters in a conventional manner, then the determination of the uncertainties of each parameter, finally the determination of the instantaneous parameters taking into account the results of the first two steps. For the validation of the proposed theoretical developments, the algorithm is applied to four different PV parameter estimation problems. The results obtained are compared with well-established algorithms to confirm its effectiveness. These comparisons have shown that the proposed method exhibits largely effective performances than existing methods in the literature.

Extraction of uncertain parameters of double-diode model of a photovoltaic panel using Ant Lion Optimization

This article addresses the problem of estimating the uncertain parameters of solar photovoltaic models using Ant Lion Optimization. The parameter extraction phase consists of three stages, the first stage is devoted to estimating the parameters without considering the uncertainties, the second stage deals with estimating the uncertainties of each parameter, with the aim of reducing of search interval for parameters to extract. Finally, the parameters are extracted instantaneously taking into account the results of the first two steps. For validation of proposed theoretical developments, the algorithm is applied to the academic example of the real data of silicon solar cell RTC France. The results obtained are compared with well-established algorithms. These comparisons have shown that the proposed method presents largely effective performances compared to existing methods in the literature.