|Commenced in January 2007||Frequency: Monthly||Edition: International||Paper Count: 18|
The use of renewable energies is growing significantly worldwide. Faced with the increasing demand for electrical energy, mainly for the needs of remote, deserted and mountainous regions, numerous applications use photovoltaic energy. In this sense, the proposed study concerns a mathematical modeling and an experimental validation for the recovery of essential oil by a steam distillation system using photovoltaic energy. In this paper, we proceed to a modeling of the solar system that includes a photovoltaic (PV) generator with an electronic power converter allowing a continuation of the optimum operating point. The results obtained are promising and are validated practically.
The inherent nature of normal boost converter has more voltage stress across the power electronics switch and ripple. The presented formation of the front end rectifier stage for a photovoltaic (PV) organization is mainly used to give the supply. Further increasing of the solar efficiency is achieved by connecting the zero voltage soft switching boost converter. The zero voltage boost converter is used to convert the low level DC voltage to high level DC voltage. The inherent nature of zero voltage switching boost converter is used to shrink the voltage tension across the power electronics switch and ripple. The input stage allows the determined power point tracking to be used to extract supreme power from the sun when it is available. The hardware setup was implemented by using PIC Micro controller (16F877A).
In this paper, type-2 fuzzy logic control (T2FLC) and neuro-fuzzy control (NFC) for a doubly fed induction generator (DFIG) based on direct power control (DPC) with a fixed switching frequency is proposed for wind generation application. First, a mathematical model of the doubly-fed induction generator implemented in d-q reference frame is achieved. Then, a DPC algorithm approach for controlling active and reactive power of DFIG via fixed switching frequency is incorporated using PID. The performance of T2FLC and NFC, which is based on the DPC algorithm, are investigated and compared to those obtained from the PID controller. Finally, simulation results demonstrate that the NFC is more robust, superior dynamic performance for wind power generation system applications.
The article proposes maximum power point tracking without mechanical sensor using Multilayer Perceptron Neural Network (MLPNN). The aim of article is to reduce the cost and complexity but still retain efficiency. The experimental is that duty cycle is generated maximum power, if it has suitable qualification. The measured data from DC generator, voltage (V), current (I), power (P), turnover rate of power (dP), and turnover rate of voltage (dV) are used as input for MLPNN model. The output of this model is duty cycle for driving the converter. The experiment implemented using Arduino Uno board. This diagram is compared to MPPT using MLPNN and P&O control (Perturbation and Observation control). The experimental results show that the proposed MLPNN based approach is more efficiency than P&O algorithm for this application.
Maximum Power Point Tracking (MPPT) has played a vital role to enhance the efficiency of solar photovoltaic (PV) power generation under varying atmospheric temperature and solar irradiation. However, it is hard to track the maximum power point using conventional linear controllers due to the natural inheritance of nonlinear I-V and P-V characteristics of solar PV systems. Fuzzy Logic Controller (FLC) is suitable for nonlinear system control applications and eliminating oscillations, circuit complexities present in the conventional perturb and observation and incremental conductance methods respectively. Hence, in this paper, FLC is proposed for tracking exact MPPT of solar PV power generation system under varying solar irradiation conditions. The effectiveness of the proposed FLC-based MPPT controller is validated through simulation and analysis using MATLAB/Simulink.
The pursuit of the MPPT has led to the development of many kinds of controllers, one of which is the Fuzzy Logic controller, which has proven its worth. To further tune this controller this paper will discuss and analyze the use of Genetic Algorithms to tune the Fuzzy Logic Controller. It will provide an introduction to both systems, and test their compatibility and performance.
It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now.
This paper presents modeling and simulation of Grid Connected Photovoltaic (PV) system by using improved mathematical model. The model is used to study different parameter variations and effects on the PV array including operating temperature and solar irradiation level. In this paper stepped P&O algorithm is proposed for MPPT control. This algorithm will identify the suitable duty ratio in which the DC-DC converter should be operated to maximize the power output. Photo voltaic array with proposed stepped P&O-MPPT controller can operate in the maximum power point for the whole range of solar data (irradiance and temperature).
Photovoltaic (PV) energy is one of the most important renewable energy sources. Maximum Power Point Tracking (MPPT) techniques should be used in photovoltaic systems to maximize the PV panel output power by tracking continuously the maximum power point which depends on panel’s temperature and on irradiance conditions. Incremental conductance control method has been used as MPPT algorithm. The methodology is based on connecting a pulse width modulated dc/dc SEPIC converter, which is controlled by a microprocessor based unit. The SEPIC converter is one of the buck-boost converters which maintain the output voltage as constant irrespective of the solar isolation level. By adjusting the switching frequency of the converter the maximum power point has been achieved. The main difference between the method used in the proposed MPPT systems and other technique used in the past is that PV array output power is used to directly control the dc/dc converter thus reducing the complexity of the system. The resulting system has high efficiency, low cost and can be easily modified. The tracking capability has been verified experimentally with a 10 W solar panel under a controlled experimental setup. The SEPIC converter and their control strategies has been analyzed and simulated using Simulink/Matlab software.
Nowadays the use of Hybrid Electric Vehicles (HEV) is increasing dramatically. The HEV is mainly dependent on electricity and there is always a need for storage of charge. Fuel Cell (FC), Batteries and Ultra Capacitor are being used for the proposed HEV as an electric power source or as an energy storage unit. The aim of developing an energy management technique is to utilize the sources according to the requirement of the vehicle with help of controller. This increases the efficiency of hybrid electric vehicle to reduce the fuel consumption and unwanted emission. The Maximum Power Point Tracking (MPPT) in FC is done using (Perturb & Observe) algorithm. In this paper, the control of automobiles at variable speed is achieved effectively.
Alternative energy is any energy source that is an alternative to fossil fuel. These alternatives are intended to address concerns about such fossil fuels. Today, because of the variety of energy choices and differing goals of their advocates, defining some energy types as "alternative" is highly controversial. Most of the recent and existing alternative sources of energy are discussed below
Photovoltaic (PV) energy is one of the most important energy resources since it is clean, pollution free, and endless. Maximum Power Point Tracking (MPPT) is used in photovoltaic (PV) systems to maximize the photovoltaic output power, irrespective the variations of temperature and radiation conditions. This paper presents a comparison between Ćuk and SEPIC converter in maximum power point tracking (MPPT) of photovoltaic (PV) system. In the paper, advantages and disadvantages of both converters are described. Incremental conductance control method has been used as maximum power point tracking (MPPT) algorithm. The two converters and MPPT algorithm were modelled using MATLAB/Simulink software for simulation. Simulation results show that both Ćuk and SEPIC converters can track the maximum power point with some minor variations.
In this paper we propose the study of a centrifugal pump control system driven by a three-phase induction motor, which is supplied by a PhotoVoltaic PV generator. The system includes solar panel, a DC / DC converter equipped with its MPPT control, a voltage inverter to three-phase Pulse Width Modulation - PWM and a centrifugal pump driven by a three phase induction motor. In order to control the flow of the centrifugal pump, a Direct Torque Control - DTC of the induction machine is used. To illustrate the performances of the control, simulation results are carried out using Matlab/Simulink.
This paper proposes the method combining artificial neural network with particle swarm optimization (PSO) to implement the maximum power point tracking (MPPT) by controlling the rotor speed of the wind generator. With the measurements of wind speed, rotor speed of wind generator and output power, the artificial neural network can be trained and the wind speed can be estimated. The proposed control system in this paper provides a manner for searching the maximum output power of wind generator even under the conditions of varying wind speed and load impedance.