Artificial Intelligence in Renewable Energetic Systems

We have contributed to explain how the project managers and promoters proceed to realize smart buildings, how to optimize their energy efficiency by allowing an effective management of the resources with a minimum of costs. This by analysing some examples of smart residential buildings in Algeria. Then, we tried to find the satisfaction degree that these buildings offer to their occupants, but for this part, it concerns a tertiary building, by doing a diagnosis at the cyber park site of Sidi Abdallah and using also a satisfaction survey.This study demonstrate that for succeeding such as building and its good energy management, also its techniques related to home automation; integration of smart systems, a less energy consuming. It is necessary to ensure that these facilities meet the needs of the users, ensure the simplicity of use to suit the equipment whether in terms of operation or programming, the reliability and durability of equipment too that does not slow down consumers in their acceptance.

The ability to recognize human activities from sensed information become more attractive to computer science researchers due to a demand on a high quality and low cost of health care services at anytime and anywhere. In this work, we proposed a new hybrid classification model to perform automatic recognition of activities in a smart home using KNN-WSVM combined the K-Nearest Neighbors (KNN) with Weighted Support Vector Machines (WSVM) learning algorithm, allowing to better discrimination between the classes of activity. We also added the temporal features (TF) in the used method KNN-WSVM. Experiments show our proposed approach outperforms the KNN, WSVM used alone in terms of recognition performance, highlighting the advantages of this method.

Today’s cities must respond to multiple challenges related to the evolutions of the contemporary world. The safety of people and goods has become a strategic issue for countries and companies. An intelligent system for Smart Cities is a desired technology in the 21st century. The main attraction of any automated system using the Internet of Things (IoT) is to reduce human labour, effort, time and errors due to human negligence. Managing the security of people in a public space is very important in the smart cities. The challenge is to find a system that offers many security solutions at the same time? An affordable and effective tool. In this paper a global system that manages different solutions in a single application using the IoT is proposed. Various sensors based on facial recognition, fire detection, vehicle number plate recognition can be added at this prototype to improve the intelligence and ability to make more accurate decisions.

The main benefit of smart home system is controlling and monitoring of household appliances remotely and flexibly. Control systems for smart home have been improved with the integration of technologies of communication, digital information and electronics in the home environment. In this paper, a new and flexible smart home control system is presented. The proposed system is based on the Raspberry Pi B+ board, which is programmed as the embedded home server and the standard ZigBee is used to ensure communications within the home network. To accomplish this experiment, XBee ZigBee devices, smart plugs, and Raspberry pi board are used.

This paper studies an Indoor Positioning System (IPS) based on Visible Light Communication (VLC) using the solar cell as an optical receiver unlike conventional receivers. Due to the advantage of the solar cell such as: low cost, flexibility, and high light sensitivity, the proposed system is capable of simultaneous communication and energy gathering. The studied system is considered like an environmentally friendly and a promising technology in the next years.

Smart Home has attracted significant attention from the research community in the recent years. Considerable progress has been made with the miniaturization of electronic devices for home automation and with the development of the technology of embedded system. The present work designs a smart and secure system based on various sensors and Raspberry Pi via the Internet of Things (IoT). This paper focuses on two aspects of smart home i.e. home security and home automation. This system is also designed to assist and support persons at home. It is intended to control appliances and the effective interactions to the persons. The user can visualise and control devices using internet, a webpage interface has been developed to help users to save the electrical energy by regular monitoring of home devices or the proper switching ON/OFF scheduling of them for more energy efficiency.

The impact of new information and communication technologies on our societies is universal. They have the advantage of being transversal and affecting all sectors, notably health. E-health, a multidisciplinary field of research, is growing significantly and is nowadays necessary for systems of healthcare. The determinants of this breakthrough in healthcare remain the age demographic of the population coupled with the inadequacy of the health workforce, without ignoring the rising costs of care. This is reflected in the continuing rise in the number of people with addictions, as chronic diseases become more acute, creating a huge need for appropriate technologies. The circumstances are therefore conducive to reflection leading to the modeling of more innovative solutions for effective patient care.The NICT’s incursion in the medical field is most often seen as the introduction of tools designed to guarantee or even improve the quality of healthcare services, so our research combines crosses and puts into dialogue the relationship between space and the psychiatric patient. To do this, we approach space as a determining factor in helping people with psychiatric illnesses, and as an interactive, real-time approach to the needs of the sick.

Energy consumption in city is increasing because the number of population is increasing. Also, this energy consumption differs according to the weather data, inhabitants of buildings and type of building; commercial, residential or administrative. Whereas, the citizen needs to have a compromise between the energy consumption, the economic cost, the comfort and the environmental impact of the building. In this paper, we will propose a smart model which permits to manage, control and regulate the consumption of energy according to some criteria. Thus, this model allows figuring, regulating, optimizing energy consumption and satisfying the occupants’ comfort in real time. Thus the citizen does not need to read electricity metrics or wait the billing period to know its energy consumption. Also, this approach allows saving the energy resources and increasing the system productivity even in peak demand hour.

In this paper, we are developed a novel strategy for a hybrid energy management system consisting of a photovoltaic (PV) array, a polymer electrolyte membrane fuel cell (PEM-FC) as energy sources, the purpose of this system is to balance the power supply with load demand fluctuation and to ensure its long-term sustainability. Therefore, we used multiple forms of power supply such as a Fuel cell energy storage system, PV generator, and an intelligent energy management system to control puissance’s of system. DC-DC converters are used to interface PV-FC combination. Energy management between the two sources is done by operating them in three different modes based on power requirement of load. In the case of additional energy directed to the electrolyzer, to be converted to hydrogen and stored for the time of need.

Metamaterials are artificial pseudo-homogeneous structures with electromagnetic properties not found in nature. This paper presents a microwave band-stop filter making use of complementary split-ring resonator (CSRR). This filter combines a conventional bandstop filter characteristics and negative permittivity metamaterial to establish a metamaterial filter. This structure is designed in the X-band [8.2–12.4] GHz, using relatively dielectric constant substrate material (RO4003 εr = 3.38 and tangential losses (tg(δ) = 0.0027)), Numerical calculations using the Finite Element Method MEF based the High Frequency Structure Simulator (HFSS) software was used to design this filter.

The main objective of this paper is to control the flux and torque of an induction motor by using three-level DTC with 24 sectors. Direct torque control has been widely used due to its advantages of less parameter dependences and faster torque response. However, in conventional DTC, there are obvious torque and flux ripples. In this present paper, we propose to reduce the electromagnetic torque ripple, flux ripple, and THD of stator current of the induction machine controlled by three-level DTC with 24 sectors, by using artificial intelligence techniques, fuzzy logic and neural networks. In the other hand, we propose to replace conventional hysteresis controller of torque by fuzzy logic, and hysteresis controller of flux by neural networks. Simulation results are presented and show the effectiveness of the proposed hysteresis.

The paper deals with design and sizing of hybrid PV/diesel/battery energy system using two energy management strategies: load following (LF) and cycle charging strategy (CC). The hybrid system is designed to electrify 25 households, primary school, and small dispensary in the Saharan and rural village of Moulay Lahcen, in the province of Tamanrassat, Algeria. A comparative analysis has been elaborated using HOMER in terms of cost of energy (COE), net present cost (NPC), renewable fraction (RF), fuel consumption, battery autonomy and GHG emissions (mainly carbon emissions) between the two control strategies. The results showed that cycle charging strategy is more cost effective compared to load following in both households and school, in the case of dispensary the load following is more cost effective than the cycle charging strategy. The LF strategy has less GHG emissions and more PV penetration than CC strategy in all cases.

The aim of this work is the evaluation of wind power presented by DFIG and FACTS device which is the UPFC on the critical clearing fault time in multi-machine power system. The performance of wind power integrated into power system is investigated by searching the optimal location can be the DFIG generators installed for enhancing the critical fault-clearing time (TCid). This performance can be also improvement using one of powerful FACTS device on transient stability improvement. The simulation of our system models has been prepared by using MATLAB/SIMULINK software where the IEEE 3 machines 9 buses (Western System Coordinating Council) is taken as a test system and the obtained results showed the performance of wind power and UPFC to increase the fault critical clearing time of power system.

Since solar thermal stations, domestic hot water and heat-process systems all are being to use solar energy by converting the sun’s rays into heat. In order for these processes to be effective, they must absorb as much solar radiation as possible while limiting the loss radiative heat from the absorbent surface which has to have selectivity in the wavelengths of the solar spectrum. This selectivity allows it to have a maximum of absorbance with a minimum of thermal radiation emitted. This article deals with the optimization based on artificial neural networks of the solar absorption and infrared emission of the absorbing surface, in terms of absorbance and emissivity, study the possibility of integrating multilayer films deposited on aluminum and cover substrates to describe the properties of a surface. More than 300 different coatings and surface treatments for selective absorption of solar energy have been reported in the scientific and technical literature. Only a few of these have been subjected to detailed theoretical analyses [1]. In our multilayer, we used two materials having, respectively, a high and a low refractive index. We studied two cases: silicone black-pigmented coating/Polyurethane black-pigmented coating and Polyurethane black-pigmented coating/SiO₂. The thin films were deposited by Dip-coating technique. Spectroscopic ellipsometry was used to determine the optical constants and the thicknesses of every individual multilayer.

In this article which presents a experimental study of parabolic trough collector concentrator. In this work, we propose the practical realization of any concentrator having a 2.88 m2 opening and it is equipped by a solar semi-electronic tracking system with a closed circuit of the heat-transfer fluid. A study on the extensive comparison of three outdoor test methods for determining the thermal performance of parabolic trough solar collectors is presented. These test methods of vertical movement and two other case of bi-axel movement with and without the absorber tube glass envelope, in other review the principles of the work of power plants with a review of the possibility of solar energy in Algeria. There are also important conditions for power generation with economic concentrating solar power provides all the electrical grid requirements.The experimental side has studied the influence of the prototype’s orientation on the performances of training. Several trials have been carried out in order to have the possibility of attending the temperatures that can ensure the vaporization of water. These trials have been preceded in different conditions of climatic operation. The results are provided and have been discussed. They are discussed in detail and are explained in view of the on-site parabolic trough solar collectors in the real service.

In this paper, we propose a set of times series forecasting techniques based on the combination of Support Vector Regression methods to predict global horizontal solar radiation in Algeria. The models were constructed and tested using different architectures of Support Vector Machine (SVM), namely, (RBF kernel, Polinomial kernel and Linear kernel). We use individual time series models and linear combination techniques to predict global solar radiation indifferent sites in Algeria. For this aim, the recorded data of 4 stations spread over Algeria were used to build different combination schemes for the different times series algorithms. The efficiency of the different models was calculated using a number of statistical indicators: the Mean Absolute Percentage Error (MAPE), the Mean Squared Error (RMSE), Mean Bias Error (MABE) and the Coefficient of Determination (R2). The results obtained from these models were compared with the measured data.

The present paper suggests a simulation study for a grid connected photovoltaic system, associated to a shunt active power filter (SAPF). On the one hand, to extract the maximum power from the PV generator a fuzzy logic Maximum power taking point (MPPT) is proposed in order to track permanently the optimum point. On the other hand, direct power control (DPC) is used to control the shunt active power filter in order to compensate the undesirable harmonic. The simulation of the system under the Matlab/SimulinkTM environment demonstrates the robustness of the proposed controls that simultaneously guarantee harmonic current compensation, unit power factor operation and injection of solar power to the power grid.

This paper proposes the modeling and control of Wind Energy Conversion Systems (WECS) based on Doubly Fed Induction Generator (DFIG). The fuzzy logic control is used to improve the extracted wind power at given wind velocity; the mechanical power available from a wind turbine is a function of its shafts speed. Then, the rotor side converter (RSC) is controlled in the aim to follow the optimal torque for given maximal wind power. The effectiveness of the proposed control strategy is validated by theoretical analysis and simulation carried out in Matlab/Simulink environment.

This paper present an intelligent technique extraction of Maximum Power Point Tracking (MPPT) by using fuzzy logic from a standalone hybrid generation power system comprising a permanent magnet synchronous generator branch (PMSG) based wind turbine and photovoltaic generator branch compared to the conventional hill climb search (HCS) and Perturb and Observe (P&O) algorithms respectively. The fuzzy controller for solar wind MPPT scheme shows a high precision in current transition and keeps the voltage without any changes, in variable-load case, represented in small steady state error and small overshoot. The proposed scheme ensures optimal use of the photovoltaic (PV) array and PMSG wind proves its efficacy in variable load conditions, unity and lagging power factor at the inverter output (load) side.

Global warming and environmental pollution in one hand and the depletion of fossil fuel resources in the coming decades in the other hand are now the two main concerns worldwide. Renewable energies are a solution to replace conventional sources of energy that are polluting and tend to disappear. Photovoltaic energy constitutes among renewable energies that it has the broadest potential of development.This paper proposes a direct torque controlled IM supplied with photovoltaic panel to replace flux oriented control; and to track the maximum power point, this technique (MPPT) is based on fuzzy logic technique to get better performance specially on variation of load and weather condition. Finally, simulation results are given to show the effectiveness and feasibility of the approach.

This paper describes a PV system supplied a large scale interconnected grid. A PV array is connected to AC grid via a DC-DC boost converter and a three-phase three-level Voltage Source Converter (VSC). Maximum Power Point Tracking (MPPT) is implemented in the boost converter by means of a Simulink model using fuzzy logic controller by switching the duty cycle of the boost converter.A three phase VSC converts the 500 V DC link voltage to 260 V AC and keeps unity power factor. The VSC control system uses two control loops: an external control loop which regulates DC link voltage to 260 V AC and an internal control loop which regulates Id and Iq grid currents (active and reactive current components). Id current reference is the output of the DC voltage external controller. Iq current reference is set to zero in order to maintain unity power factor. Vd and Vq voltage outputs of the current controller are converted to three modulating signals used by the PWM Generator.

This paper presents an application of a new control based on Type 2 Fuzzy Logic Controller (T2FLC) for controlling the both powers (active, reactive) of the Doubly Fed Induction Generator (DFIG) in Wind Energy Conversion System (WECS). In addition, a comparison applied between T2FLC and a classical Type 1 Fuzzy Logic Controller (T1FLC) to the purpose of showing the performance of the T2FLC controller as compared to T1FLC controller. The proposed controllers implemented and tested using MATLAB/Simulink. The simulation results show that the controller based on T2FLC characterized by good performance with better convergence and it is fast than those obtained with the T1FLC method.

This paper presents the simulation study of three phase shunt active filter connected to photovoltaic array that based on DPC approach. The proposed system has the ability to inject active power into the grid, however it improves the quality of energy by correcting the power factor and reducing harmonic currents in distribution network. This work interests in improving the control of DC side where it is proposed to use fuzzy logic controller in DC bus voltage regulation loop, this later have been developed to provide better performance compared to conventional PI controller. Otherwise, to extract the maximum power of PV system it is proposed to associate the PV panels by a DC–DC boost converter. Therefore, the control of the boost is based on sliding mode that has good accuracy on following the maximum power point. The results of simulation obviously demonstrate that the designed fuzzy logic controller can be used for improving DC bus voltage under external parameter variation.

In this paper, a new MPPT controller based on variable step size fuzzy controller have been proposed and investigated. The performance of the proposed algorithm are analyzed under different atmospheric conditions using Matlab/Simulink environment using boost DC-DC converter connected to Solarex MSX-60 panel. The results have demonstrated the high performances of the proposed technique showing good improvements of the proposed algorithm to track effectively the maximum power point with low oscillation, low ripple, low overshoot and good rapidity in slow or fast changing atmospheric conditions.

Fault diagnosis in photovoltaic (PV) installations is a fundamental task to protect the components of PV systems (modules, strings and inverters), from damage and to eliminate possible fire risks. In this paper, an intelligent fault detection and diagnosis method has been presented and implemented into a reconfigurable Field Programmed Gate Array (FPGA). Only faults that can be appeared in PV arrays are examined in this work. The designed method consists of two parts: the first one is based on signal threshold approach, and the second one is based on an artificial neural network (ANN). The whole parts of the system have been implemented into FPGA board (named ZYNQ XC7Z010-1CLG400C). Xilinx System Generator (XSG) and VIVADO tools have been used to simulate and implement the algorithm. Results demonstrate with success the possibility implementation of the designed diagnosis method.

Global solar radiation is very important variable in agricultural meteorology and many applied sciences, is measured at very limited number of meteorological Algeria stations. The aim of our study is the determination of global solar radiation for short time scale by some geographical and meteorological parameters, for this purpose we developed tree semi empirical models: Capderou, Atwater&Ball and Lieu&Jordan models. MATLAB was the simulation tool where the program was developed to calculate the hourly global radiation. The results obtained were confirmed by comparing them with the measured results by two statistical indicators: coefficient of determination R2 and the percentage root mean square error RMSE% for two Algerian sites: Tamanrasset and Algiers.

The “e-maintenance” is the modern definition of the maintenance, to face the in-creasing complexity of the industrial troubleshooting by adding a stronger dimension of cooperation at information’s level. Besides, it permits to remote control the maintenance tasks which will be a gain in time and costs. In this paper, we present “e-MIED for e-Maintenance Intelligent & Diagnostics” as an e-maintenance platform presenting a clear guideline for researcher in this field; we centered the conception of our platform on a completely modular framework in order to allow easily adjusting the global environment behavior in accordance with the requirements of client resources. Moreover, the software platform will be applied on a novel hardware photovoltaic solar platform made by the research team. Fault diagnosis module using Support Vector Machine is applied to distinguish the different health states of a machine or component and it’s considered as the main part of e-MIED platform.

As one of artificial intelligence technologies, Artificial Neural Networks (ANNs) are widely used as an alternative solution for ill-defined problems. Training using examples make it possible to deal with noisy and replace missed data. Nonlinear problems can be solved using artificial neural networks and, once trained, forecasting.This paper investigates the application of Genetic Algorithm (GA) based ANN in weather forecasting and compares the whole performance with the one of an ordinary ANN where both of them have been used for the prediction of air temperature, atmospheric pressure, relative humidity and mean wind speed. The proposed system uses ANN with GA based generated weights.The obtained results using this model are good enough to prove that the proposed system can be used for modelling in other fields of renewable energy problems.

Renewable Energy Sources (RES) are one of the key solutions to handle the world’s future energy needs, while decreasing carbon emissions. To produce electricity, large concentrating solar power plants depend on Direct Normal Irradiance (DNI), which is rapidly variable under broken clouds conditions. To work at optimum capacity while maintaining stable grid conditions, such plants require accurate DNI forecasts for various time horizons. The main goal of this study is the forecasting of DNI over two short-term horizons: 15-min and 1-h. The proposed system is purely based on historical local data and Artificial Neural Networks (ANN). For this aim, 1-min solar irradiance measurements have been obtained from two sites in different climates. According to the forecast results, the coefficient of determination (R2) ranges between 0.500 and 0.851, the Mean Absolute Percentage Error (MAPE) between 0.500 and 0.851, the Normalized Mean Squared Error (NMSE) between 0.500 and 0.851, and the Root Mean Square Error (RMSE) between 0.065 kWh/m2 and 0.105 kWh/m2. The proposed forecasting models show a reasonably good forecasting capability, which is decisive for a good management of solar energy systems.

In this paper we investigated the reliability of a GRNN algorithm for the power prediction of a PV panel in order to minimize the effect of fast changing of the meteorological conditions. An experimental database of meteorological data (irradiance and module temperature) as input and electrical measure (power delivered by PV Panel) as output has been used. A database composed of two sets 97 values each one is used for training and validating the proposed GRNN model. The data used to develop the proposed algorithm are attained during two separated days from a PV panel within the MIS-Lab of UPJV, France. According to the gained results the algorithm can help to predict real instantaneous power even during temporary change in meteorological data.

The main challenge in replacing legacy systems with the newer alternatives is to capture maximum energy and deliver maximum power at minimum cost for the given load. Solar energy which is free and abundant in most parts of the world has proven to be an economical source of energy in many applications. Among the renewable energy technologies, the solar energy coupling with fuel cell technology will be the promising possibilities for the future green energy solutions. The new efficient photovoltaic array (PVA) has emerged as an alternative measure of renewable green power, energy conservation and demand-side management. However in photovoltaic power generation system the control problems arise due to large variances of output under different insulation. This problem can be overcome by hybrid photovoltaic generation system i.e. use of photovoltaic arrays with fuel cells and power storage such as battery bank. The stand-alone hybrid system aims to provide power efficiency supply to the consumers with a constant voltage and frequency along with proper power management using simple control techniques. The modeling and control strategies of the hybrid system are realized in MATLAB/Simulink.

In this paper, we propose a new method for Bulk waves detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO₃). We have used the classification by probabilistic neural network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials.By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Direct torque control (DTC) based on artificial neural network (ANN) of a five-phase permanent magnet synchronous motor drive (PMSM) is presented in this paper. Using the mathematical model of the five-phase motor, DTC control strategy is developed, and the corresponding controllers are properly designed in order to provide independent torque and flux control. In order to improve the performance of the DTC, a neural network based DTC scheme is adopted instead of the DTC based on the look-up table. The employed neural network uses the Levenberg-Marquardt back propagation algorithm for the adjustment of weights to increase the learning process accuracy. The efficacy of the proposed method is verified by simulation for various dynamic operating conditions, and the system’s performance is compared with conventional DTC.

The power characteristic of a wind turbine is naturally nonlinear, because the position of the maximum power varies with the wind speed, for each wind speed, it is necessary that the system finds the maximum power. To approach this goal, a specific command must be used. In this paper, a variable speed wind generator maximum power point tracking (MPPT) based on artificial neural network (ANN) is presented.Which at first allows prediction of the maximum rotation speed by using the ANN, which is then determined the optimal speed rotation by using the new maximum rotation speed method (MRS).The generator used in this study is a synchronous permanent magnet (PMSG), controlled by an electronic converter with pulse width modulation (PWM); this last uses of a vector and an MPPT (Maximum power point tracking) controller to check the electromechanical magnitudes such as the torque or the rotational speed of the generator in order to extract the maximum wind energy. The simulation results show the effectiveness and robustness of the proposed control.

This paper proposes the use of an artificial neural network (ANN) to model an improved liquid desiccant solar system. This system consists of six major components: an air dehumidifier or absorber, a solution regenerator or desorber, a heat and mass exchanger, and an air-to-air heat exchanger. The experimental data were collected from literature during four successive typical days (5–8 September 2010) from a large volume of data. By developing a MatLab program an ANN was used with the main goal of predicting the coefficient of performance (COP) based on the lowest input variables. The optimal topology of the static neural network is (8-8-1): {8 neurons in the input layer; 8 neurons in the hidden layer and 1 neuron in output (COP)}. The optimized neural network reproduces the experimental data from the literature with a great accuracy: an average mean square error of 1.93% and determination coefficient R2 > 0.90 which can be considered very satisfactory.

The aim of this work is to use multi-layered perceptron artificial neural networks and multiple linear regressions models to predict the efficiency of the magnetic refrigeration cycle device operating near room temperature. For this purpose, the experimental data collection was used in order to predict coefficient of performance and temperature span for active magnetic refrigeration device. In addition, the operating parameters of active magnetic refrigerator cycle are used for solid magnetocaloric material under application 1.5 T magnetic fields. The obtained results including temperature span and coefficient of performance are presented and discussed.

The important feature of wind energy is the fluctuation of the power produced over time. The stability of the network is based on the balance between production and consumption. For this, the idea of storage has been exploited. Owing of this, in first stage, an ANFIS controller is proposed for speed control in order to ensure the real-time tracking of the optimum operating point and MPPT giving online a maximum production of electric power for different wind speeds. In second stage, we present a new solution for the wind energy storage based on short term storage. This solution is based on the use of the intelligent flywheel based on fuzzy logic. The new Fuzzy FESS can be used not only to minimize wind power fluctuations, but also to adjust the frequency and the voltage of the grid during operating conditions. Simulation testes on a 1.5 MW DSIG system are given to illustrate the feature of control method and the large interest of energy storage in such WECS.

This paper presents Proton exchange membrane fuel cells (PEM-FC) modeling, ultracapacitor modeling, analysis of the parameter influences for performance evaluation and optimal control of energy storage elements. Theoretical results are verified by simulation models for a PEM-FC and Ultracapacitor (UC). This paper introduces the electrochemical model of fuel cell (FC). The Simulink model based on it is suitable. The results of the model are used to control the output of energy storage elements.

Harmonic pollution is a very common issue in the field of power electronics, Harmonics can cause multiple problems for power converters and electrical loads alike, this paper introduces a modulation method called selective harmonic elimination pulse width modulation (SHEPWM), this method allows the elimination of a specific order of harmonics and also control the amplitude of the fundamental component of the output voltage. In this work SHEPWM strategy is applied to a five level cascade inverter. The objective of this study is to demonstrate the total control provided by the SHEPWM strategy over any rank of harmonics using the simulated annealing optimization algorithm and also control the amplitude of the fundamental component at any desired value. Simulation and experimental results are presented in this work.

Heating, ventilation and air conditioning (HVAC) systems in buildings aim to control the indoor climate in order to keep occupants comfortable by control the temperature and air flow specially in arid zone like south west Algeria when we have hot summer and cold winter. To achieve the comfort, it is necessary to have adaptable control systems that could deal with the parameters required to control the indoor climatic conditions. This paper, we have developed a nonlinear physical model of order seven by using lumped capacitance method. The experimental data measured in the laboratory we used for the validation phase of the models. The identification of the thermal parameters was did manually under the Matlab/Simulink environment. The control phase aims to provide user comfort. The choice was made on conventional controllers adapted to this kind of model. The PID and On/Off controllers are given good results.

In this paper a Genetic Algorithm (GA) method based on graphs theory is proposed to determine the distribution network reconfiguration in presence of wind turbine based DG considering all technical and topological constraints. The objective function considered in this study is the minimization of real power loss. A detailed performance analysis is applied on (33 bus, 69 bus and 84 bus networks) to illustrate the effectiveness of the proposed method. Then this method was validated on Algerian distribution network (116 bus).

Renewable energy is an alternative solution for water pumping and irrigation for isolated and arid regions. This paper analyzes the irrigation requirements of greenhouse model representative and provides solution by photovoltaic system for irrigation.According to the results of calculations, monthly water consumption varies between 1.02 m3 and 9.70 m3 for three products (Tomato, Muskmelon and Watermelon). The proposed technology can fully satisfy these water requirements.Furthermore, this paper discusses the economic analyses for this technology, in comparison with the diesel system. The obtained results indicates that the Levelized cost of energy (LCOE) of PV system is acceptable compared to the diesel generator, where LCOE_PV = 0.060 $/kWh, LCOE_Diesel = 0.260 $/kWh, and the water price for PV system is 6.09 $ and for diesel system is 10.43 $, for daily consumption of 0.33 m3. This proves the PV water pumping system is more economical than the diesel system.

The aim of our work is to be able to make significant improvements to the performance of silicon-based solar cells by the inclusion of silicon nanowires on the one hand, and on the other hand, by incorporating germanium into the substrate based on silicon. Our strategy consisted in particular of ordering the nanowires of silicon perpendicularly to the electrodes in order to improve significantly the collection of the photogenerated charges. The work we have presented in this paper is the modeling, simulation and optoelectronic characterization of photovoltaic cells based on silicon nanowires using a silicon-germanium substrate. The main objective is to improve the conversion efficiency of this type of solar cells by orientation of the silicon nanowires.

Multilevel inverters are most familiar with power converter applications. In this current paper, we discussed another possibility for DC/AC energy conversion. Converters based on current source inverters (CSI) configuration, take an important role in active power filtering in term of harmonics compensate and increasing system efficiency. To illustrate the design feasibility of the proposed structure of three and five level neutral point clamped (NPC) inverter, variable band hysteresis current regulation is used to determine the semiconductors switching signals. Association of shunt active power filter (APF) and multilevel inverter using type-2 fuzzy logic controller can seen in this analysis paper. All series of simulation results in MATLAB/Simulink environment are demonstrated and compared to illustrate the effectiveness of this scientific research.

This study aims to investigate the optimal sizing and the reliability of the (PV/wind/battery) hybrid energy system to supply a stand-alone site during their 20 years of lifetime. In this case, an Equivalent Loss Factor (ELF) as reliability index is used and applied in this work. A metaheuristic method such as particle swarm optimization (PSO) is used to solve the optimization problem and compared with other new and improved PSO Algorithms as Fast Convergence and Time Varying Acceleration. A comparative analysis is performed between this study and previous works for a standalone system with a hybrid energy system considering the economic cost using PSO method. Simulation results using Matlab software prove the reliability of the hybrid PV/wind/battery system during its lifetime and the improved PSO method by FC-TVAC algorithm has shown its performance in the optimisation study.

Solar cell/module modeling involves the formulation of the non-linear current versus voltage (I-V) curve. Determination of parameters plays an important role in solar cell/module modeling. This paper presents an application of the improved PSO search method and Particle Swarm Optimization technique for identifying the unknown parameters of solar cell and photovoltaic module models, namely, the series resistance, shunt resistance, generated photocurrent, saturation current, and ideality factor that govern the current-voltage relationship of a solar cell/module. For the confirmation of accuracy of the proposed method, a measurement data of 57 mm diameter commercial (R.T.C. France) silicon solar cell and a module consisting of 36 polycrystalline silicon cells (Photowatt-PWP 201) has been selected and the best optimal value of each parameter has been obtained using Improved PSO. Comparative study among different parameter estimation techniques is presented to demonstrate to verify the accurateness and the effectiveness of the proposed approach.

This paper presents a new contribution for the control of Wind-turbine energy driven wound field synchronous generator (WFSG) connected to the grid via a back to back converter. The goal is to track the maximum power point via a dedicated control. For this purpose, a robust backstepping controller with integral actions is applied to the WFSG. This control is based on both feedback laws and Lyapunov technique. In order to verify the validity of the proposed method, this control is applied to an accurate dynamic model of the whole generator. The simulation results show the performances.

Algeria’s energy needs are actually satisfied mainly through the use of hydrocarbons. Demand for energy is growing exponentially because the population is growing massively and the building sector is in full expansion in order to address and solve the problems of housing, health and education. In 2011, Algeria initiated a dynamic green energy program by launching an ambitious development project aims at developing a renewable energy power source of 22000 MW and to save more than 60 Million TOE in energy efficiency by the year 2030. Our research topic is linked to the energy efficiency program initiated in Algeria. The objective of this article in to present a study carried out on the thermal performance of Phase Change Materials (PCMs) and their integration in buildings in order to achieve thermal comfort and to reduce the energy consumption. Then, an investigation on the capacity of using (PCMs) in Algeria is presented for a sustainable development and rational energy consumption.

In this paper, we study the effect of the technological parameters of on chip inductor, the characteristics of on chip inductor is simulated using the MATLAB simulation. All of inductor is fabricated in the standard silicon process with metal layers. The simulated results are accurate only when technological parameters used in ensemble, such as the metal conductivity, metal thickness, oxide thickness between the top metal and the substrate, and substrate conductivity on inductance, quality factor and self-resonant frequency.

We performed modeling and simulation of copper indium gallium diselenide (CIGS) thin film solar cell. CIGS absorbers today have a typical thickness of about 1–2 µm. However, on the way toward mass production, it will be necessary to reduce the thickness even further. We investigated the influence of the alloy compositions $$ {\text{x = }}\left[ {\text{Ga}} \right]/\left\{ {\left[ {\text{In}} \right] + \left[ {\text{Ga}} \right]} \right\} $$x =Ga/In+Ga, the CIGS absorbers thickness and the temperature. Optimal results are obtained with a thickness of about 2 µm and a temperature of 318 K. It was also shown that, the short-circuit current density (J_sc) decreases when the x composition increases. Very high J_sc of 40.72 mA/cm2 was obtained, when x = 0.2. In contrary, the open-circuit voltage (Voc), the fill factor (FF) and the efficiency $$ (\eta ) $$(η) of the solar cell are increasing with the increase of the x composition. An optimal efficiency of about 30.34% was obtained with x = 0.9. Moreover, a comparison with published data for the Cu(In,Ga)Se₂ cells have shown an excellent agreement.

This paper develops characterization of graphene and ionic liquids in order to develop material, which allows several applications like sensors and energy storage. Several steps were applied in order to synthesise Nano-sensor. First, we synthesised Oxide Graphene. We mix ionic liquids with (GO) in order to maximise conductivity. We add new elements like the toxic substance tetrahydrofuran, which that it was adsorbed by GO-IL. This new material constitutes needs to product the Sensor based on ionic liquid functionalized graphene modified electrode for sensitive detection of Tetrahydrofuran.

In this paper, we are interested in modeling and simulations of InGaN/GaN Quantum Wells Solar Cell, like all other semiconductor devices solar cells are sensitive to temperature, the most parameter affected by the rise in temperature is the open circuit voltage V_oc. In this paper, the thermal effects on InGaN/GaN multiple quantum well solar cells (MQWSCs) with an Indium concentration of 0.28 are studied. A temperature of 280 K gives better results of open circuit voltage, maximum power output and conversion efficiency their values are 1.84 V, 24.86 mW/cm2 and 24.87% respectively.

The photovoltaic module is typically represented by an equivalent circuit whose parameters are calculated using the experimental current voltage characteristic I–V. The description of photovoltaic cells current–voltage mathematical is usually defined by a coupled nonlinear equation, difficult to solve using analytical methods. This difficulty has led to the development of several algorithms for solving this equation using numerical techniques. This paper compares three different algorithms of a 5-parameter single-diode and algorithm of two diodes solar PV model using manufacturer’s data sheet. This comparison is based on calculated the error relative between the obtained results and experimental values in the important points of the I–V curves, the peak power (Imax, Vmax), short-circuit current Isc and open circuit voltage Voc. We have compared via Matlab developments for multi-crystalline, mono-crystalline and thin-film modules.

The role of interactions is certainly one of the most difficult and important phenomena to be solved in the physics of condensed matter. Thus, we use the Full Potential Linearized Augmented Plane Wave (FP-LAPW) method, which is based on the resolution of the Kohn-Sham equations in two arbitrarily defined regions in the elementary mesh. The Generalized Gradient Approximation (GGA) is used to process binary alloys which are ZnO and ZnTe. We will compare these predictions with the experimental results and with the theoretical work devoted to this deviation. The Wien2k code based on density functional theory (DFT) is explored to determine the various structural and electronic properties of ternary ZnTe_1-xO_x alloys for x = 0.25, 0.50, 0.75 as well as the curvature parameters by the ab-initio calculation means.

This article aims to modeling and to simulate new solar cells for a photovoltaic application with semiconductor materials III-V, more exactly based of $$ InAs_{x} Sb_{1 - x} /GaAs $$InAsxSb1-x/GaAs. We initially studied the influence of the indium concentration on the various alloy parameters GaInAsSb epitaxed on GaInSb substratum. Indeed the increase of the density of indium decreases the band gap of the structure, in order to absorb the maximum of solar spectrum. The study includes results giving the variations of the various factors influencing the efficiency on conversion according to the indium concentration in the structure.