Hybrid MPPT Control: P&O and Neural Network for Wind Energy Conversion System

In the field of wind turbine performance optimization, many techniques are employed to track the maximum power point (MPPT), one of the most commonly used MPPT algorithms is the perturb and observe technique (PO) because of its ease of implementation. However, the main disadvantage of this method is the lack of accuracy due to fluctuations around the maximum power point. In contrast, MPPT control employing neural networks proved to be an effective solution, in terms of accuracy. The contribution of this work is to propose a hybrid maximum power point tracking control using two types of MPPT control: neural network control (NNC) and the perturbation and observe method (PO), thus the PO method can offer better performance. Furthermore, this study aims to provide a comparison of the hybrid method with each algorithm and NNC. At the resulting duty cycle of the 2 methods, we applied the combination operation. A DC-DC boost converter is subjected to the hybrid MPPT control.  This converter is part of a wind energy conversion system employing a permanent magnet synchronous generator (PMSG). The chain is modeled using MATLAB/Simulink software. The effectiveness of the controller is tested at varying wind speeds. In terms of the Integral time absolute error (ITAE), using the PO technique, the ITAE is 9.72. But, if we apply the suggested technique, it is smaller at 4.55. The corresponding simulation results show that the proposed hybrid method performs best compared to the PO method. Simulation results ensure the performance of the proposed hybrid MPPT control.

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

In this paper, the behavior of a grid-connected hybrid ac/dc microgrid has been investigated. Different renewable energy sources - photovoltaics modules and a wind turbine generator - have been considered together with a solid oxide fuel cell and a battery energy storage system. The main contribution of this paper is the design and the validation of an innovative online-trained artificial neural network-based control system for a hybrid microgrid. Adaptive neural networks are used to track the maximum power point of renewable energy generators and to control the power exchanged between the front-end converter and the electrical grid. Moreover, a fuzzy logic-based power management system is proposed in order to minimize the energy purchased from the electrical grid. The operation of the hybrid microgrid has been tested in the MATLAB/Simulink environment under different operating conditions. The obtained results demonstrate the effectiveness, the high robustness and the self-adaptation ability of the proposed control system

A Review of Control Techniques for Wind Energy Conversion System

Wind energy is the most efficient and advanced form of renewable energy (RE) in recent decades, and an effective controller is required to regulate the power generated by wind energy. This study provides an overview of state-of-the-art control strategies for wind energy conversion systems (WECS). Studies on the pitch angle controller, the maximum power point tracking (MPPT) controller, the machine side controller (MSC), and the grid side controller (GSC) are reviewed and discussed. Related works are analyzed, including evolution, software used, input and output parameters, specifications, merits, and limitations of different control techniques. The analysis shows that better performance can be obtained by the adaptive and soft-computing based pitch angle controller and MPPT controller, the field-oriented control for MSC, and the voltage-oriented control for GSC. This study provides an appropriate benchmark for further wind energy research

Roach Infestation Optimization MPPT Algorithm of PV Systems for Adaptive to Fast-Changing Irradiation and Partial Shading Conditions

Of all the renewable energy sources, solar photovoltaic (PV) power is considered to be a popular source owing to several advantages such as its free availability, absence of rotating parts, integration to building such as roof tops and less maintenance cost. The nonlinear current–voltage (I–V) characteristics and power generated from a PV array primarily depends on solar insolation/irradiation and panel temperature. The power output depends on the accuracy with which the nonlinear power–voltage (P–V) characteristics curve is traced by the maximum power point tracking (MPPT) controller. A DC-DC converter is commonly used in PV systems as an interface between the PV panel and the load, allowing the follow-up of the maximum power point (MPP). The objective of an efficient MPPT controller is to meet the following characteristics such as accuracy, robustness and faster tracking speed under partial shading conditions (PSCs) and climatic variations. To realize these objectives, numerous traditional techniques to artificial intelligence and bio-inspired techniques/algorithms have been recommended. Each technique has its own advantage and disadvantage. In view of that, in this thesis, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). In addition, the mathematical formulations and operation of the boost converter is investigated. To validate the effectiveness of the proposed RIO MPPT algorithm, MATLAB/Simulink simulations are carried out under varying environmental conditions, for example step changes in solar irradiance, and partial shading of the PV array. The obtained results are examined and compared with the particle swam optimization (PSO). The results demonstrated that the RIO MPPT performs remarkably in tracking with high accuracy as PSO based MPPT. Last but not the least, I am very grateful to the Arctic Centre for Sustainable Energy (ARC), UiT The Arctic University of Norway, Norway for providing an environment to d

Control of Proton Exchange Membrane Fuel Cell System

265 p.In the era of sustainable development, proton exchange membrane (PEM) fuel cell technology has shown significant potential as a renewable energy source. This thesis focuses on improving the performance of the PEM fuel cell system through the use of appropriate algorithms for controlling the power interface. The main objective is to find an effective and optimal algorithm or control law for keeping the stack operating at an adequate power point. Add to this, it is intended to apply the artificial intelligence approach for studying the effect of temperature and humidity on the stack performance. The main points addressed in this study are : modeling of a PEM fuel cell system, studying the effect of temperature and humidity on the PEM fuel cell stack, studying the most common used power converters in renewable energy systems, studying the most common algorithms applied on fuel cell systems, design and implementation of a new MPPT control method for the PEM fuel cell system

A novel mine blast optimization algorithm (MBOA) based MPPT controlling for grid-PV systems

One of the most important areas in today's world is meeting the energy needs of various resources provided by nature. The advantages of renewable energy sources for many application sectors have attracted a lot of attention. The majority of grid-based enterprises use solar photovoltaic (PV) systems to collect sunlight as a reliable energy source. Due to solar PV's simple accessibility and efficient panel design, it is widely used in a variety of application scenarios. By employing the Maximum Power Point Tracking (MPPT) technique, the PV modules can typically operate at their best rate and draw the most power possible from the solar system. Some hybrid control mechanisms are utilized in solar PV systems in traditional works, which has limitations on the problems of increased time consumption, decreased efficiency, and increased THD. Thus, a new Mine Blast Optimization Algorithm (MBOA) based MPPT controlling model is developed to maximize the electrical energy produced by the PV panels under a different climatic situations. Also, an interleaved Luo DC-DC converter is used to significantly improve the output voltage of a PV system with a lower switching frequency. A sophisticated converter and regulating models are being created to effectively meet the energy demand of grid systems. The voltage source inverter is used to lower the level of harmonics and ensure the grid systems' power quality. Various performance indicators are applied to assess the simulation and comparative results of the proposed MBOA-MPPT controlling technique integrated with an interleaved Luo converter

Contribution à l’amélioration des performances d’un système de dessalement d’eau alimenté par une source photovoltaïque

Cette thèse présente une étude visant à améliorer l'efficacité d'un système de dessalement d’eau en utilisant une source photovoltaïque. Cette amélioration a été réalisée en appliquant l’ESC côté PV, la DTC 12 sectors côté motopompe et l'optimisation côté membrane dans des conditions climatiques saines et ombragées. L'étude a impliqué la conception et la mise en oeuvre d’un prototype de système de dessalement d'eau qui utilise une source d'énergie photovoltaïque. Les performances du système ont été évaluées en mesurant son taux de rejet de sel et sa consommation d'énergie spécifique. Les résultats ont montré que l'augmentation du débit d'eau saumâtre et l'amélioration des contrôles sur le côté PV et motopompe a considérablement amélioré les performances du système. L'étude a conclu que l'utilisation d'une source d'énergie photovoltaïque pour un système de dessalement de l'eau est une approche prometteuse pour répondre aux défis de la pénurie d'eau, en particulier dans les zones où l'électricité n'est pas facilement disponible. Cette approche peut réduire l'empreinte carbone associée aux méthodes traditionnelles de dessalement de l'eau et avoir un impact positif sur l'environnement. La recherche fournit des indications précieuses pour la conception et l'optimisation des systèmes de dessalement d'eau alimentés par l'énergie photovoltaïque, contribuant à la gestion durable des ressources en eau

Power Converter of Electric Machines, Renewable Energy Systems, and Transportation

Power converters and electric machines represent essential components in all fields of electrical engineering. In fact, we are heading towards a future where energy will be more and more electrical: electrical vehicles, electrical motors, renewables, storage systems are now widespread. The ongoing energy transition poses new challenges for interfacing and integrating different power systems. The constraints of space, weight, reliability, performance, and autonomy for the electric system have increased the attention of scientific research in order to find more and more appropriate technological solutions. In this context, power converters and electric machines assume a key role in enabling higher performance of electrical power conversion. Consequently, the design and control of power converters and electric machines shall be developed accordingly to the requirements of the specific application, thus leading to more specialized solutions, with the aim of enhancing the reliability, fault tolerance, and flexibility of the next generation power systems

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above