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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.

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.

To generate electricity, solar photovoltaic (PV) systems are among the best, most eco-friendly and most cost-effective solutions available. Extraction of maximum possible electricity from the solar PV system is complicated by a number of factors brought on by the ever-changing weather conditions under which it must operate. Many conventional and evolutionary algorithm-based maximum power point tracking (MPPT) techniques have the limitation of not being able to extract maximum power under partial shade and rapidly varying irradiance. Hence, the purpose of this paper is to propose a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid bridge link-honey comb (BL-HC) configured PV system to enhance the better maximum power during dynamic and steady state operations within less time.

In this method, a hybridization of two algorithms is proposed to track the true with faster convergence under PSCs. Initially, the slime mould optimization (SMO) algorithm is initiated for exploration of optimum duty cycles and later P&O algorithm is initiated for exploitation of global duty cycle for the DC–DC converter to operate at GMPP and for fast convergence.

The effectiveness of the proposed HSMO MPPT is compared with adaptive coefficient particle swarm optimization (ACPSO), flower pollination algorithm and SMO MPPT techniques in terms of tracked GMPP, convergence time/tracking speed and efficacy under six complex partial shading conditions. From the results, it is noticed that the proposed algorithm tracks the true GMPP under most of the shading conditions with less tracking time when compared to other MPPT techniques.

This paper proposes a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid BL-HC configured PV system enhance the better maximum power under partial shading conditions (PSCs). This method operated in two stages as SMO for exploration and P&O for exploitation for faster convergence and to track true GMPP under PSCs. The proposed approach largely improves the performance of the MPP tracking of the PV systems. Initially, the proposed MPPT technique is simulated in MATLAB/Simulink environment. Furthermore, an experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution.

The aim of this paper is to find the electrical representation of a solid oxide fuel cell (SOFC) that enables the application of typical exploitation characteristics of fuel cells for estimation of fuel cell parameters (for example, exchange current) and easy analysis of phenomena occurred during the fuel cell operation.

Three-layer structure of an SOFC, where a thin semi-conducting layer of electrolyte separates the anode from the cathode, shows a strong similarity to typical semiconductor devices built on the basis of P-N junctions, like diodes or transistors. Current–voltage (I-V) characteristics of a fuel cell can be described by the same mathematical functions as I-V plots of semiconductor devices. On the basis of this similarity and analysis of impedance spectra of a real fuel cell, two electrical representations of the SOFC have been created.

The simplified electrical representation of SOFC consists of a voltage source connected in series with a diode, which symbolizes a voltage drop on a cell cathode, and two resistors. This model is based on the similarity of Butler-Volmer to Shockley equation. The advanced representation comprises a voltage source connected in series with a bipolar transistor in close to saturation mode and two resistors. The base-emitter junction of the transistor represents voltage drop on the cell cathode, and the base-collector junction represents voltage drop on the cell anode. This model is based on the similarity of Butler-Volmer equation to Ebers-Moll equation.

The proposed approach based on the Shockley and Ebers-Moll formulas enables the more accurate estimation of the ion exchange current and other fuel cell parameters than the approach based on the Butler-Volmer and Tafel formulas. The usability of semiconductor models for analysis of SOFC operation was proved. The models were successively applied in a new design of a planar ceramic fuel cell, which features by reduced thermal capacity, short start-up time and limited number of metal components and which has become the basis for the SOFC stack design.

These implementations not only generate excessive voltage levels to enhance the quality of power but also include a detailed investigating of the various modulation methods and control schemes for multilevel inverter (MLI) topologies. Reduced harmonic modulation technology is used to produce 11-level output voltage with the production of renewable energy applications. The simulation is done in the MATLAB/Simulink for 11-level symmetric MLI and is correlated with the conventional inverter design.

This paper is focused on investigating the different types of asymmetric, symmetric and hybrid topologies and control methods used for the modular multilevel inverter (MMI) operation. Classical MLI configurations are affected by performance issues such as poor power quality, uneconomic structure and low efficiency.

The variations in both carrier and reference signals and their performance are analyzed for the proposed inverter topologies. The simulation result compares unipolar and bipolar pulse-width modulation (PWM) techniques with total harmonic distortion (THD) results. The solar-fed 11-level MMI is controlled using various modulation strategies, which are connected to marine emergency lighting loads. Various modulation techniques are used to control the solar-fed 11-level MMI, which is connected to marine emergency lighting loads. The entire hardware system is controlled by using SPARTAN 3A field programmable gate array (FPGA) board and the least harmonics are obtained by improving the power quality.

The simulation result compares unipolar and bipolar PWM techniques with THD results. Various modulation techniques are used to control the solar-fed 11-level MMI, which is connected to marine emergency lighting loads. The entire hardware system is controlled by a SPARTAN 3A field programmable gate array (FPGA) board, and the power quality is improved to achieve the lowest harmonics possible.

The purpose of this paper is to investigate the chemical, optical and photovoltaic properties of titanium dioxide/reduced graphene oxide (TiO₂/rGO) photoanodes immersed in natural Roselle and synthetic (N719) dyes for dye-sensitized solar cell (DSSC) application.

TiO₂ mixed with rGO were doctor-bladed on fluorine doped tin oxide (FTO) glass substrate. The chemical and optical properties of TiO₂/rGO photoanodes immersed in Roselle and N719 dyes were characterized using Fourier-transformed infrared (FTIR) and ultraviolet–visible (UV–vis) spectroscopies, respectively. The DSSC’s photovoltaic performances were tested using Visiontec Solar I-V tester at standard illumination of AM1.5 and irradiance level of 100 mW/cm².

The presence of anthocyanin dye from Roselle flower was detected at 604 nm and 718 nm. TiO₂/rGO+Roselle dye sample revealed the smallest energy gap of 0.17 eV for ease of electron movement from valence band to conduction band. The TiO₂/rGO-based DSSC fabricated with Roselle dye had a power conversion efficiency, ƞ of 0.743 per cent higher than TiO₂/rGO photoanode sensitized with N719 dye (0.334 per cent). The obtained J-V curves were analyzed by a single-diode model of Lambert W-function and manual optimization to determine the internal electrical parameters of the DSSC. The average and uncertainty values of J_sc and ƞ were evaluated at different R_sh range of 1362 Ω to 32 k Ω.

R_s values were kept constant during optimization work.

New ideality factor of TiO₂/rGO-based DSSC was re-determined around 0.9995.

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.

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.

This paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV.

To extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically.

In this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature.

The proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper.

The purpose of this paper is to focus on two major areas of concern for the Photovoltaic (PV) system, i.e. power quality and maximum power point tracking (MPPT). Novel control strategies have been proposed for both these issues, and their respective superiorities over the existing techniques have been established. On the other hand, as far as MPPT is concerned, two limitations are found in the available techniques. One is the inability of effective MPPT in dynamic conditions where the environmental parameters changes very rapidly. Second one is the ineffective tracking of global maxima under partial shading conditions.

Here, modified Kalman filtering approach has been applied for estimating the reference current of active power filter, incorporated for power quality improvement. The proposed Kalman algorithm introduces a weighted matrix, which advances the estimated values of state variables. This paper presents a simple and enhanced model-based (MB) MPPT algorithm that has the capability of tracking MPPT effectively in both these working conditions. The proposed MB algorithm uses the mathematical modelling, and based on precised estimation of parameters, it pre-determines the MPP analytically.

It has been tested successfully for dynamic variations of insolation, temperature and partial shading, where all these three parameters are rigorously varied over the full scale of practical values. The results have been also investigated experimentally and compared with the simulated one. A close matching of both the results has been shown through the plots, which validates the effectiveness of proposed algorithms.

This research paper is part of the original research work carried out in Lab. Simulated results are obtained in MATLAB/Simulink platform, whereas these are further validated experimentally on 2-KW panel constituted with all types of commercial products, namely, mono, poly and thin-film.