Bio: Ramazan Akkaya is an academic researcher from Selçuk University. The author has contributed to research in topics: Voltage source & Induction motor. The author has an hindex of 12, co-authored 25 publications receiving 531 citations.
TL;DR: A genetic algorithm (GA) optimized ANN-based MPPT algorithm implemented in a stand-alone PV system with direct-coupled induction motor drive to eliminate dc–dc converter and its accompanying losses is proposed.
Abstract: Artificial neural network (ANN) based maximum power point tracking (MPPT) algorithm makes use of the advantages of ANNs such as noise rejection capability and not requiring any prior knowledge of the physical parameters relating to PV system. This paper proposes a genetic algorithm (GA) optimized ANN-based MPPT algorithm implemented in a stand-alone PV system with direct-coupled induction motor drive. The major objective of this design is to eliminate dc–dc converter and its accompanying losses. Implementing off-line ANN in DSP needs optimization of ANN structure to obtain an ideal size. GA optimization was used in this study to determine neuron numbers in multi-layer perceptron neural network. Another objective of this work is to prevent the necessity of the trade-off between the tracking speed and the oscillations around the maximum power point. Hence, varying step size is used in MPPT algorithm and PI-controller is adopted for simple implementation. Simulation and experimental results have been used to demonstrate effectiveness of the proposed method.
TL;DR: In this article, a brushless dc motor drive for heating, ventilating and air conditioning fans, which is utilized as the load of a photovoltaic system with a maximum power point tracking (MPPT) controller, is presented.
Abstract: This paper presents a brushless dc motor drive for heating, ventilating and air conditioning fans, which is utilized as the load of a photovoltaic system with a maximum power point tracking (MPPT) controller. The MPPT controller is based on a genetic assisted, multi-layer perceptron neural network (GA-MLP-NN) structure and includes a DC–DC boost converter. Genetic assistance in the neural network is used to optimize the size of the hidden layer. Also, for training the network, a genetic assisted, Levenberg–Marquardt (GA-LM) algorithm is utilized. The off line GA-MLP-NN, trained by this hybrid algorithm, is utilized for online estimation of the voltage and current values in the maximum power point. A brushless dc (BLDC) motor drive system that incorporates a motor controller with proportional integral (PI) speed control loop is successfully implemented to operate the fans. The digital signal processor (DSP) based unit provides rapid achievement of the MPPT and current control of the BLDC motor drive. The performance results of the system are given, and experimental results are presented for a laboratory prototype of 120 W.
TL;DR: An optimal design method to optimize three-phase induction motor in manufacturing process is presented and the optimally designed motor is compared with an existing motor having the same ratings.
Abstract: This paper presents an optimal design method to optimize three-phase induction motor in manufacturing process. The optimally designed motor is compared with an existing motor having the same ratings. The Genetic Algorithm is used for optimization and three objective functions namely torque, efficiency, and cost are considered. The motor design procedure consists of a system of non-linear equations, which imposes induction motor characteristics, motor performance, magnetic stresses and thermal limits. Computer simulation results are given to show the effectiveness of the proposed design process.
TL;DR: In this article, a stand-alone photovoltaic power system was designed and implemented to operate residential ac-powered appliances such as fluorescent lambs, fans etc. The charge method is realized with closed-loop current control of buck-boost dc-dc converter.
Abstract: In this study, a stand-alone photovoltaic power system was designed and implemented to operate residential ac-powered appliances such as fluorescent lambs, fans etc. Sun-tracker is implemented for improved efficiency of the system by keeping the solar module perpendicular to the sun's incoming rays. The charge method is realized with closed-loop current control of buck-boost dc–dc converter. The proposed system also uses a voltage source type PWM inverter to convert DC voltage from battery storage to supply AC loads. In the PWM method used, selected harmonics are eliminated with the smallest number of switching and an improvement in the system efficiency by reducing switching losses and providing ease of filtering on the inverter output is obtained. Charge controller and PWM inverter systems have been realized by using PIC16F873 microcontrollers. An experimental system was implemented to demonstrate the effectiveness of the proposed system. Simulation and experimental results are given to verify the system's efficiency.
TL;DR: In this article, a genetic algorithm is used to improve the MPPT efficiency of a PV system with induction motor drive by optimizing the input dataset for an ANN model of PV modules.
Abstract: Maximum power point tracking (MPPT) algorithms are used to force photovoltaic (PV) modules to operate at their maximum power points for all environmental conditions. In artificial neural network (ANN)-based algorithms, the maximum power points are acquired by designing ANN models for PV modules. However, the parameters of PV modules are not always provided by the manufacturer and cannot be obtained readily by the user. Experimental measurements implemented in the overall PV system may be used to obtain the ANN dataset. One drawback of this method is that the generalization ability of the neural network usually degrades and some data reducing the effectiveness of the network may exist. A genetic algorithm can be used to automatically select the important data among all the inputs, resulting in a smaller and more effective dataset. In our study, a genetic algorithm is used to improve the MPPT efficiency of a PV system with induction motor drive by optimizing the input dataset for an ANN model of PV modules. A variable frequency volts-per-Hertz (V/f ) control method is applied for speed control of the induction motor, and a space-vector pulse-width modulation (SV-PWM) method is used to operate a 3-phase inverter. Both simulation and experimental results are presented to demonstrate the validation of the method.
TL;DR: The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in photovoltaic systems application, mainly because of their symbolic reasoning, flexibility and explanation capabilities.
Abstract: Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more popular nowadays. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems and once trained can perform prediction and generalization at high speed. AI-based systems are being developed and deployed worldwide in a wide variety of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. AI has been used in different sectors, such as engineering, economics, medicine, military, marine, etc. They have also been applied for modeling, identification, optimization, prediction, forecasting and control of complex systems. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in photovoltaic systems application. Problems presented include three areas: forecasting and modeling of meteorological data, sizing of photovoltaic systems and modeling, simulation and control of photovoltaic systems. Published literature presented in this paper show the potential of AI as design tool in photovoltaic systems.
TL;DR: It is concluded that the control strategy has a significant impact on the resonance of the MC input filter.
Abstract: This paper presents a review of the most popular control and modulation strategies studied for matrix converters (MCs) in the last decade. The purpose of most of these methods is to generate sinusoidal current on the input and output sides. These methods are compared considering theoretical complexity and performance. This paper concludes that the control strategy has a significant impact on the resonance of the MC input filter.
TL;DR: In this article, a state of the art review on various maximum power point techniques for solar PV systems covering timeworn conventional methods and latest soft computing algorithms is presented to date critical analysis on each of the method in terms of tracking speed, algorithm complexity, dynamic tracking under partial shading and hardware implementation is not been carried out.
Abstract: In recent years solar energy has received worldwide attention in the field of renewable energy systems Among the various research thrusts in solar PV, the most proverbial area is extracting maximum power from solar PV system Application dof Maximum Power Point Tracking (MPPT) for extracting maximum power is very much appreciated and holds the key in developing efficient solar PV system In this paper, a state of the art review on various maximum power point techniques for solar PV systems covering timeworn conventional methods and latest soft computing algorithms is presented To date critical analysis on each of the method in terms of (1) tracking speed, (2) algorithm complexity, (3) Dynamic tracking under partial shading and (4) hardware implementation is not been carried out In this regard the authors have attempted to compile a comprehensive review on various solar PV MPPT techniques based on the above criteria Further, it is envisaged that the information presented in this review paper will be a valuable gathering of information for practicing engineers as well as for new researchers
TL;DR: In this paper, the state-of-the-art in research works on non-isolated DC-DC buck, boost, buck-boost, Cuk and SEPIC converters and their characteristics, to find a solution best suiting an application with maximum power point tracking.
Abstract: Photovoltaic (PV) is a fast growing segment among renewable energy (RE) systems, whose development is owed to depleting fossil fuel and climate-changing environmental pollution. PV power output capacity, however, is still low and the associated costs still high, so efforts continue to develop PV converter and its controller, aiming for higher power-extracting efficiency and cost effectiveness. Different algorithms have been proposed for Maximum Power Point Tracking (MPPT). Since the choice of right converter for different application has an important influence in the optimum performance of the photovoltaic system, this paper reviews the state-of-the-art in research works on non-isolated DC–DC buck, boost, buck–boost, Cuk and SEPIC converters and their characteristics, to find a solution best suiting an application with Maximum Power Point Tracking. Review shows that there is a limitation in the system's performance according to the type of converter used. In can be concluded that the best selection of DC–DC converter which is really suitable and applicable in the PV system is the buck–boost DC–DC converter since it is capable of achieving optimal operation regardless of the load value with negotiable performance efficiency and price issue.
TL;DR: In this paper, the authors present a review of the performance and reliability of various methods for maximum power point tracking (MPPT) in PV-based power systems, including their limitations and advantages.
Abstract: Given the considerable recent attention to distributed power generation and interest in sustainable energy, the integration of photovoltaic (PV) systems to grid-connected or isolated microgrids has become widespread. In order to maximize power output of PV system extensive research into control strategies for maximum power point tracking (MPPT) methods has been conducted. According to the robust, reliable, and fast performance of artificial intelligence-based MPPT methods, these approaches have been applied recently to various systems under different conditions. Given the diversity of recent advances to MPPT approaches a review focusing on the performance and reliability of these methods under diverse conditions is required. This paper reviews AI-based techniques proven to be effective and feasible to implement and very common in literature for MPPT, including their limitations and advantages. In order to support researchers in application of the reviewed techniques this study is not limited to reviewing the performance of recently adopted methods, rather discusses the background theory, application to MPPT systems, and important references relating to each method. It is envisioned that this review can be a valuable resource for researchers and engineers working with PV-based power systems to be able to access the basic theory behind each method, select the appropriate method according to project requirements, and implement MPPT systems to fulfill project objectives.