Search results

This paper aims to present a detailed investigation on the parameter estimation of a photovoltaic (PV) module by using a simplified two-diode model.

The studied PV module in this paper resembles an ideal two-diode model, and to reduce the computational time, the proposed model has a photocurrent source and two ideal-diodes and neglects the series and shunt resistances. Hence, for calculating the unknown parameters, only four parameters are required from the datasheet. Moreover, the studied model is simple and uses an easy modeling approach which is free from complexities.

The performance of the PV module is analyzed under non-standard test conditions by considering partial shading at different shaded levels, and it is found that the model has less computational time and gives accurate results.

The usefulness of this PV model is demonstrated with the help of several illustrative figures, and the performance of the PV module is evaluated.

Estimation of solar cell parameters, mathematical modeling and the actual performance analysis of photovoltaic (PV) cells at various ecological conditions are very important in the design and analysis of maximum power point trackers and power converters. This study aims to propose the analysis and modeling of a simplified three-diode model based on the manufacturer’s performance data.

A novel technique is presented to evaluate the PV cell constraints and simplify the existing equation using analytical and iterative methods. To examine the current equation, this study focuses on three crucial operational points: open circuit, short circuit and maximum operating points. The number of parameters needed to estimate these built-in models is decreased from nine to five by an effective iteration method, considerably reducing computational requirements.

The proposed model, in contrast to the previous complex nine-parameter three-diode model, simplifies the modeling and analysis process by requiring only five parameters. To ensure the reliability and accuracy of this proposed model, its results were carefully compared with datasheet values under standard test conditions (STC). This model was implemented using MATLAB/Simulink and validated using a polycrystalline solar cell under STC conditions.

The proposed three-diode model clearly outperforms the earlier existing two-diode model in terms of accuracy and performance, especially in lower irradiance settings, according to the results and comparison analysis.

Parameter identification of photovoltaic (PV) modules plays a vital role in modeling PV systems. This study aims to propose a novel hybrid approach to identify the seven parameters of the two-diode model of PV modules with high accuracy.

The proposed hybrid approach combines an improved particle swarm optimization (IPSO) algorithm with an analytical approach. Three parameters are optimized using IPSO, whereas the other four are analytically determined. To improve the performance of IPSO, three improvements are adopted, that is, evaluating the particles with two evaluation functions, adaptive evolutionary learning and adaptive mutation.

The performance of proposed approach is first verified by comparing with several well-established algorithms for two case studies. Then, the proposed method is applied to extract the seven parameters of CSUN340-72M under different operating conditions. The comprehensively experimental results and comparison with other methods verify the effectiveness and precision of the proposed method. Furthermore, the performance of IPSO is evaluated against that of several popular intelligent algorithms. The results indicate that IPSO obtains the best performance in terms of the accuracy and robustness.

An improved hybrid approach for parameter identification of the two-diode model of PV modules is proposed. The proposed approach considers the recombination saturation current of the p–n junction in the depletion region and makes no assumptions or ignores certain parameters, which results in higher precision. The proposed method can be applied to the modeling and simulation for research and development of PV systems.

To design, control and evaluate photovoltaic (PV) systems, an accurate model is required. Accuracy of PV models depends on model parameters. This study aims to use a new algorithm called improved social spider algorithm (ISSA) to detect model parameters.

To improve performance of social spider algorithm (SSA), an elimination period is added. In addition, at the beginning of each period, a certain number of the worst solutions are replaced by new solutions in the search space. This allows the particles to find new paths to get the best solution.

In this paper, ISSA is used to estimate parameters of single-diode and double-diode models. In addition, effect of irradiation and temperature on I–V curves of PV modules is studied. For this purpose, two different modules called multi-crystalline (KC200GT) module and polycrystalline (SW255) are used. It should be noted that to challenge the performance of the proposed algorithm, it has been used to identify the parameters of a type of widely used module of fuel cell called proton exchange membrane fuel cell. Finally, comparing and analyzing of ISSA results with other similar methods shows the superiority of the presented method.

Changes in the spider’s movement process in the SSA toward the desired response have improved the algorithm’s performance. Higher accuracy and convergence rate, skipping local minimums, global search ability and search in a limited space can be mentioned as some advantages of this modified method compared to classic SSA.

This paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function.

In this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. Based on the input electrical data for the analysed bifacial PV module as well as the mathematical model of the two PV cell presentations, the values for the current and the voltage at the maximum power point for a given solar irradiation and working temperature are determined by the algorithm for each solution in the population and generation.

From the presented results and the performed analysis, it can be concluded that GA is quite appropriate for this purpose and gives adequate results for both models and for all working conditions. The two-diode model was found to be more suitable compared with the simplified model due to its complexity. Therefore, although the power difference for each of the scenarios for the two compared models does not differ significantly among the two models, it is in favour of the two-diode model. Which implicates that the for fast and simple calculation the simplified model can also do the job.

This approach can be very successfully applied in the design process of a PV plant to forecast the output characteristics of the PV system if there is enough information about the weather conditions for a given location. This procedure can be very helpful in the process of selection of right PV module and inverter for a given location.

An optimisation technique using GA as an optimisation tool has been developed and successfully applied in the determination of the maximum power point for a bifacial PV module using to different models of solar cell. The results are compared with the analytically determined values as well as with the values given from the producer and they show good agreement.

Partial shading causes significant power decreases in the PV systems. The purpose of this paper is to address this problem, connectivity regulation is designed to reduce partial shading problems.

In this approach, the partial shading was estimated and dispersed evenly on the whole array by global shade dispersion technique (GSD). The grey wolf algorithm was implemented for the interconnection of arrays by an efficient switching matrix.

After the implementation of the GSD technique using a grey wolf algorithm, the performance under different shading conditions was analyzed using the MatLab simulation tool. The results were compared with total cross-tied (TCT), Su Do Ku and the proposed method of reconfiguration, where the proposed method improves the maximum power of the PV system appropriately.

This methodology uses any size of PV systems.

Replacement of conventional energy systems with renewable energy systems such as solar helps the environment clean and green.

The GSD interconnection scheme using the grey wolf optimization algorithm has proved an improved output performance compared with the existing TCT and Sudoku based reconfiguration techniques. By comparing with existing techniques in literature, the proposed method is more advantageous for reducing mismatch losses between the modules of any size of the PV array with less operating time.

This paper aims to propose a simple, derivative-free novel method named as Nelder–Mead optimization algorithm to estimate the unknown parameters of the photovoltaic (PV) module considering the environmental conditions.

At a particular temperature and irradiation, experimental current-voltage (I-V) and power-voltage (P-V) characteristics are drawn and considered as a reference model. The PV system model with unknown model parameters is considered as the adaptive model whose unknown model parameters are to be adapted so that the simulated characteristics closely matches with the experimental characteristics. A single diode (Rsh) model with five unknown model parameters is considered here for the parameter estimation.

The key advantages of this method are that parameters are estimated considering environmental conditions. Experimental characteristics are considered for parameter estimation which gives accurate results. Parameters are estimated considering both I-V and P-V curves as most of the applications demand extraction of the actual power from the PV module.

The proposed model is compared with other three well-known models available in the literature considering various statistical errors. The results show the superiority of the proposed model with a minimum error for both I-V and P-V characteristics.

The purpose of this paper is to present a novel approach to the determination of the maximum power point (MPP) in the photovoltaic system using genetic algorithm (GA). The optimization is realised on two types of photovoltaic (PV) modules: monocrystalline and polycrystalline solar modules, with the same rated peak power (400 Wp) but different electrical output data.

The proposed algorithm is a nature-based algorithm that uses genetic operators such as reproduction, crossover and mutation to realise the search through the investigated area of solutions. To determine the MPP of the PV modules, a two-diode model of a PV cell is used. Based on the input electrical data for the analysed PV module, as well as the mathematical model of the PV, the algorithm can estimate the current and voltage at the MPP for given solar irradiation and cell temperature. The analysis is made for several different irradiations, but in work, the results are presented for irradiations of: 100, 500 and 1,000 W/m² and cell temperatures of 0, 25 and 40 °C.

From the presented results and performed analysis, it can be concluded that GA gives adequate results for both modules and for all working conditions. From the obtained results, it can be concluded that the optimization algorithm performs better when applied to the monocrystalline module works better especially in conditions with larger cell temperature, in comparison with the performance of the optimization algorithm applied to the polycrystalline module. On the other hand, the optimization algorithm applied to the polycrystalline module works better for the other working scenarios with smaller cell temperatures.

From the performed analysis, it can be concluded that the use GA as an optimization tool for the determination of the MPP can be successfully implemented. In addition, to improve the overall performance of the PV system, it is also necessary to forecast the weather conditions of the location where the PV system would be installed to forecast the cell temperature and the solar irradiation. This is necessary to choose the right PV module and inverter for the given location.

An optimization technique using GA as an optimization tool has been developed and successfully applied in the determination of the MPP for a PV system. The results are compared with the analytically determined values as well as with the values given by the producer, and they show good agreement.

The purpose of this paper is to develop a simulation tool which permits reducing the cost of long time‐range simulation of complex converters and running at high frequency.

A different method is used to represent a simplified converter but the adopted technique uses the average representation of the cell converter.

The paper shows that the use of averaged representation of the pulse width modulation switch in multilevel converters is staying applied. The main advantage of the proposed averaged model is its simplified representation when only electrical behaviour is considered.

The analytical algorithm of the averaged model can be introduced in different simulator as it has a description language, enabling study of the Compatibilité Electromagnétique and electrothermal phenomena.

This paper presents an averaged model of the multilevel converter which can be implemented in any simulator as it has a description language.

The purpose of this paper is to describe an optimization methodology based on a mixed (analytical‐numerical) design model and evolutionary algorithms.

In this paper, optimal sizing is used to design the new architecture of an embedded electrical system. An approach is presented for defining the specifications and a method for modeling an embedded electrical network. This will enable the optimization problem to be set.

This paper shows that the proposed methodology is useful for sizing an embedded electrical system. It is illustrated by its application to the sizing of an aircraft electrical power channel (auto‐transformer‐rectifier unit type).

An original approach is proposed for limiting the search space before using the evolutionary algorithms. The advantage of this approach is to increase convergence speed of the evolutionary algorithms.