In recent years, continuous depletion of conventional energy resources and the fluctuating fuel cost have motivated research on alternative source for power generation. To effectively harvest power from the green energy resources, extensive research have been carried out in literature. Solar energy being the foremost among all other renewable energy resources has evolved as a reliable substitute for the conventional power generation. Unlike other sources, real time research on Photovoltaic (PV) Systems is a difficult assignment as it necessitates an accurate PV emulator that can precisely replicate the nonlinear characteristics of a PV cell. PV Researchers have developed emulators of different type utilizing various configurations. This paper attempts to present a comprehensive review on different PV emulator topologies so as to gather all the research activities that are scattered in this arena under a common umbrella. Further this paper provides a detailed analysis of each technique emphasizing on its (i) Realization cost (ii) Accuracy (iii) Level of complexity (iv) Sensitiveness to varying environmental conditions (v) Hardware implementation and (vi) Efficiency.

Analysis on solar PV emulators: A review

International Review of Electrical Engineering (IREE)

Photovoltaic systems (PV) are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time.

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Maximum power point tracking techniques for photovoltaic systems: a comparative study

The stability of power production in photovoltaics (PV) power plants is an important issue for large-scale gridconnected systems This is because it affects the control and operation of the electrical grid An efficient forecasting model is proposed in this paper to predict the next-day solar photovoltaic power using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms and real-time weather data The correlations between the global solar irradiance, temperature, solar photovoltaic power, and the time of the year were studied to extract the knowledge from the available historical data for the purpose of developing a real-time prediction system The solar PV generated power data were extracted from the power plant installed on-top of the faculty of engineering building at Applied Science Private University (ASU), Amman, Jordan and weather data with real-time records were measured by ASU weather station at the same university campus Huge amounts of training, validation, and testing experiments were carried out on the available records to optimize the Neural Networks (NN) configurations and compare the performance of the LM and BR algorithms with different sets and combinations of weather data Promising results were obtained with an excellent realtime overall performance for next-day forecasting with a Root Mean Square Error (RMSE) value of 00706 using the Bayesian regularization algorithm with 28 hidden layers and all weather inputs The Levenberg-Marquardt algorithm provided a 00753 RMSE using 23 hidden layers for the same set of learning inputs This research shows that the Bayesian regularization algorithm outperforms the reported real-time prediction systems for the PV power production

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A Predictive Model for Solar Photovoltaic Power using the Levenberg-Marquardt and Bayesian Regularization Algorithms and Real-Time Weather Data

In this work, a proof of concept prototype for a photovoltaic array emulator based on a DC-DC Boost converter is presented. This design arises from the need to study in the laboratory new PV inverters based on Silicon Carbide (SiC) devices in order to evaluate their performances. The lack of space for installation of real photovoltaic panels and the high costs of the commercial emulators lead to alternative systems which allow the reproduction of the characteristic curves of the panel arrays as well as their dependence on changing environmental conditions. The control algorithm designed for the emulator prototype is implemented by means of a field programmable gate array (FPGA). Experimental results confirm the proper operation of the PV emulator which is applied to a full-bridge single phase grid-connected inverter.

FPGA-based design of a step-up photovoltaic array emulator for the test of PV grid-connected inverters

In order to maximize the electric energy production of a photovoltaic generator (PVG), the maximum power point tracking (MPPT) methods are usually used in photovoltaic systems. The principle of these techniques is to operate the PVG to the maximum power point (MPP), which depends on the environmental factors, such as solar irradiance and ambient temperature, ensuring the optimal power transfer between PVG and load. In this paper, we present the implementation of two digital MPPT commands using the Arduino Mega type. The two proposed MPPT controls are based on the algorithm of perturb and observe (P&O), the first one with fixed perturbation step and the second one with two perturbations step varying with some conditions. The experimental results show that the P&O algorithm with variable step perturbation gives good results compared to the P&O algorithm with fixed perturbation step in terms of the time response and the oscillations around the MPP.

Implementation in Arduino of MPPT Using Variable Step Size P&O Algorithm in PV Installations

In the present paper, we will attempt to predict the I pv -V pv output characteristic of a photovoltaic generator ( PVG ) and consequently the generated electric power. This will be possible through modeling, extracting the electrical parameters of the PVG under study and also, by estimating the global incident solar radiation, on a surface, first horizontally, and then tilted to a given angle. Mathematical models developed in Matlab, to characterize the studied PVG are validated by experimental data of the PVG manufacture. While models associated with global radiation are validated by measurements taken by the meteorological station installed on the laboratory site ERTAIER (Team for Research in Technology and Advanced Engineering of Renewable Energies) of Higher School of Technology Agadir (ESTA).

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Modelling and Predicting of the Characteristics of a Photovoltaic Generator on a Horizontal and Tilted Surface

Extracting the optimum parameters of solar photovoltaic (PV) model using the experimental data of current–voltage is very critical in simulating, controlling, and optimizing the PV systems. One of the important problems encountered in modeling and simulating is to find a model that can extract parameters from PV models quickly, accurately, and reliably. Based on this motivation, the goal of this study is to suggest an improved algorithm, namely genetic algorithm based on non-uniform mutation (GAMNU), in order to approximate efficiently the parameters of solar cells and PV modules. In GAMNU, non-uniform mutation operator is used to maintain diversity in the explored solutions and the crossover operator follows an adaptive search strategy, which consists of searching the entire space at the beginning while maintaining a focused search when the population tends to converge in a certain region of the search space. The performance of the method is comprehensively evaluated on different solar cell models, including single and double diode, and single diode PV modules, of a R.T.C France silicon solar cell, ESP-160 PPW PV, STP6-120/36 and Photowatt-PWP201 module. The results obtained from single and double diode models for R.T.C France are respectively 9.8618 × 10 - 4 and 9.8683 × 10 - 4 , and for Photowatt-PWP201, STP6-120/36 and ESP-160 PPW are 2.3824 × 10 - 3 2.382420230900 × 10 - 3 , 1.6735 × 10 - 2 1.6735786505085 × 10 - 2 and 8.2942 × 10 - 2 8.2942 × 10 - 2 . The statistical obtained results show that the proposed method has very competitive performance in terms of accuracy and reliability when compared to other advanced algorithms. Therefore, the proposed algorithm is highly useful to extract the parameters of solar PV models.

Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation

Particle swarm optimization approach to determine all parameters of the photovoltaic cell

This paper is concerned with the assessment of the the performance of the Amougdoul wind farm. We have determined the Weibull parameters; namely the scale parameter, c (m/s) and shape parameter, k . After that, we have estimated energy output by a wind turbine using two techniques: the useful power calculation method and the method based on the modeling of the power curve, which is respectively 134.5 kW and 194.19 KW corresponding to 27% and 39% of the available wind energy, which confirm that the conversion efficiency does not exceed 40%.

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Mustapha Elyaqouti

Papers

Implementation in Arduino of MPPT Using Variable Step Size P&O Algorithm in PV Installations

Modelling and Predicting of the Characteristics of a Photovoltaic Generator on a Horizontal and Tilted Surface

Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation

Particle swarm optimization approach to determine all parameters of the photovoltaic cell

Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco