A Constant Grid Interface Current Controller for DC Microgrid

With the increased percentage of distributed renewable energy sources (RES) connected to the power network, it is challenging to maintain the balance between the power generation and consumptions against the unpredictable renewable energy generation and load variations. Considering this, this study proposed a new DC microgrid control strategy to reduce the disturbance to the main power grid from the distributed generation and load variations within the DC microgrid. The DC microgrid model used in this study includes an energy storage unit (battery), a distributed generation unit (PV) and loads. A fuzzy logic controller (FLC) is used to actively regulate the battery charging/discharging current to absorb the power variation caused by PV generation and load changes. The proposed control strategy is validated by simulation in MATLAB/Simulink

Plan and Reproduction of Multi Input DC-DC Buck Converter for Coordinated Inexhaustible Vitality Produced System Using Fluffy (Fuzzy) Controller

The target of this paper is to propose a Multi-input control converter for the cross breed framework so as to disentangle the power framework and lessen the expense. Sustainable power source advancements offers perfect, rich vitality accumulated from self re-establishing assets, for example, the sun, wind and so forth. As the power request expands, control disappointment additionally increments. Along these lines, sustainable power sources can be utilized to give steady loads. Another converter topology for half breed wind/photovoltaic vitality framework is proposed. Hybridizing sun oriented and wind control sources give a practical type of intensity age. The topology utilizes a combination of Buck converters. This design enables the two sources to supply the heap independently or at the same time contingent upon the accessibility of the vitality sources. Reproduction is done in MATLAB/SIMULINK programming and the consequences of the Buck converter and the hybridized converter are introduced

Design and Implementation of Full Bridge Non-isolated DC-DC Bidirectional Converter Using Fuzzy logic

With ever increasing benefits of non-renewable energy sources, the importance of energy storage system is increasing consistently. Power electronics converters are usually used to convert the power from renewable sources to match the load demand and grid requirements to improve dynamic and steady state characteristics of these green generation systems. Therefore, there is growing importance in bidirectional DC to DC converters for interface battery with energy sources. As DC to DC bidirectional converters can transfer the power between two DC sources in either direction, these converters are widely used in renewable energy hybrid power systems. Efficiency, economy and high conversion ratio are the some challenges in the development of DC to DC converters. For the low voltage range, non-isolated DC to DC converters are suitable also; they are fit for DC micro grid voltage levels

DC Microgrid based on Battery, Photovoltaic, and fuel Cells; Design and Control

Microgrids offer flexibility in power generation in a way of using multiple renewable energy sources. In the past few years, microgrids become a very active research area in terms of design and control strategies. Most of the microgrids use DC/DC converters to connect renewable energy sources to the load. In this paper, the simulation model of a DC microgrid with three different energy sources (Lithium-ion battery (LIB), photovoltaic (PV) array, and fuel cell) and external variant power load is built with MATLAB/Simulink and the simulative results show that the stability of DC microgrid can be guaranteed by the proposed maximum power point controller MPPT. The three energy sources are connected to the load through DC/DC converters, one for each. This type of topology ensures protection for each energy source as well as optimum stability at the load

Integration of distributed generation along with energy storage system to reduce the high penetration impacts of renewable energy sources into the power grid.

Compte tenu du comportement aléatoire et fluctuant des sources d'énergie renouvelable (SER), l'équilibre entre la génération et la demande ne sont pas faciles à contrôler. Par conséquent, la stabilité dynamique du flux d'énergie et le contrôle de la fréquence deviennent de plus en plus difficiles en raison des impacts de la pénétration élevée des SER dans les micro-réseau électrique. Des stratégies de contrôle des convertisseurs/onduleurs avec filtre sont nécessaires pour maintenir l'alimentation électrique appropriée dans l'ensemble du micro-réseau. L'objectif de notre travail est d'explorer les aspects critiques de la génération distribuée (GD), de l'intégration des énergies renouvelables et des systèmes de stockage de l'énergie, en mettant l'accent sur l'amélioration de l'efficacité du réseau électrique tout en minimisant la pollution atmosphérique. Cette thèse reconnaît les avantages environnementaux et économiques de la GD tout en soulignant les défis inhérents à la gestion des sources d'énergie renouvelable fluctuantes. Un algorithme de contrôle pour un système de stockage d'énergie hybride diesel-éolien à forte pénétration est conçu pour maintenir la stabilité dynamique du flux d'énergie et le contrôle de la fréquence du réseau. Les principaux résultats comprennent la réduction efficace du temps de transition dans le flux d'énergie éolienne et des fluctuations de fréquence. D'autre part, cette étude répond aux défis posés par la nature intermittente des SER et leur impact sur la stabilité dynamique et le contrôle de la fréquence. Nous avons introduit un algorithme de contrôle utilisant la logique floue pour un système de stockage d'énergie éolienne en utilisant la méthode de partage de puissance. En comparant cette approche au contrôleur conventionnel, l'algorithme proposé a démontré des améliorations substantielles dans la réduction du temps de transition dans le flux d'énergie éolienne et des fluctuations de fréquence. Dans le cadre de cette thèse, une étude complète de divers convertisseurs statiques est réalisée afin de déterminer le dispositif de stockage d'énergie le plus approprié pour les applications de réseaux intelligents. Ce système de stockage joue un rôle essentiel dans le maintien de la stabilité du réseau tout en minimisant les pertes d'énergie. L'objectif est d'identifier le dispositif de stockage d'énergie le plus adapté à cette application. Les avantages de cette technologie sont d'une grande efficacité et fiabilité, qui peuvent connecter diverses sources d'énergie et réduire les pertes de conduction dans les convertisseurs de puissance. On a analysé l'efficacité et la fiabilité de différents convertisseurs et évalué leur performance dans des conditions de charge et de décharge du système de stockage. Les plages de fonctionnement des convertisseurs élévateur-abaisseur, abaisseur-élévateur et abaisseur-élévateur (-Vout) ont été analysées pour optimiser le système de stockage d'énergie. Cette thèse présente également une analyse complète d'un schéma de simulation qui exploite un système solaire composé de panneaux photovoltaïques intégrés au réseau électrique, à diverses charges, et à un dispositif de stockage d'énergie. Après la modélisation des panneaux photovoltaïques et de leurs caractéristiques opérationnelles, un filtre adaptatif est développé pour atténuer les fluctuations du courant d'entrée. On a exploré en outre l'efficacité et les mécanismes de contrôle des convertisseurs de puissance et des onduleurs, facilitant ainsi l'intégration du système de stockage d'énergie avec le réseau électrique. Plusieurs techniques de contrôle non linéaires sont utilisées pour évaluer les performances du système avec différentes configurations, y compris un onduleur simple, un filtre multi-variable, un filtre passe bande et une configuration sans filtre. Cette recherche nous a permis de proposer une régulation efficace du bus DC au sein du réseau électrique. L'avantage clé de ces régulateurs non linéaires est leur capacité à compenser la puissance réactive et les courants harmoniques, ce qui se traduit par un réseau électrique sans perturbations et une réduction du taux de distorsion harmonique totale (DHT) des onduleurs, améliorant finalement l'efficacité globale du réseau électrique. Cette thèse apporte des connaissances précieuses pour optimiser les performances des systèmes éoliens et solaires ainsi que du dispositif de stockage d'énergie, et leur intégration au réseau grâce à des techniques de contrôle et de filtrage avancées, avec des implications significatives pour l'amélioration de la stabilité et de la fiabilité des sources d'énergie renouvelable dans le réseau électrique. Abstract Being the fluctuation behavior of Renewable Energy Sources (RESs), generation, balance, and demand are not easy tasks to control because it is not desirable to have constant power generation from RESs due to natural prospects. As a result, the dynamic stability of power flow and control of frequency is becoming more challenging due to the high penetration impacts of RESs. Control strategies of converter/inverter with filter are also required to maintain the proper power supply in the entire microgrid where energy storage device plays crucial roles. The objective of this study is to explore critical aspects of distributed generation (DG), renewable energy integration, and energy storage systems, focusing on enhancing power network efficiency while minimizing power losses and environmental air pollution. This doctoral thesis acknowledges the environmental and economic benefits of distributed generation (DG) while highlighting the inherent challenges in managing fluctuating renewable energy sources (RESs). A control algorithm for a high-penetration hybrid diesel-wind-based energy storage system is designed to maintain dynamic stability in power flow and control network frequency. The key findings include the effective reduction of transient time in wind power flow and frequency fluctuations through the use of an integral-derivative (I-D) controller. On the other hand, it recognizes the challenges posed by the intermittent nature of renewable energy sources (RESs) and their impact on dynamic stability and frequency control. This thesis introduced a control algorithm employed with a Fuzzy Logic (FL) controller for a wind-based energy storage system using the power-sharing method. By comparing this approach to the traditional Proportional Integral Derivative (PID) controller, the study demonstrated substantial improvements in reducing transient time in wind power flow and frequency fluctuations. A storage system (battery) plays a crucial role in maintaining network stability while minimizing energy losses. As a part of this thesis, a comprehensive survey of various DC-DC converters is done to determine the most suitable energy storage device for smart grid applications. The main objective is to identify this application's most appropriate energy storage device. The advantages of this technology are high efficiency and reliability, which can connect various energy sources and reduce conduction losses in the power converters. The study analyzed the efficiency and reliability of different converters and evaluated their performance in charging and discharging conditions of a battery. The operating ranges of boost-buck, buck-boost, and buck-boost (-Vout) converters are analyzed to optimize the energy storage system. This doctoral thesis also presents a comprehensive analysis of a simulation scheme that leverages a solar system composed of photovoltaic (PV) panels integrated with the electrical grid, various loads, and an energy storage device. The research begins by investigating the modeling of PV panel cells and their operational characteristics. Subsequently, an adaptive notch filter synthesis is developed to mitigate input current fluctuations. The research further explores the efficiency and control mechanisms of power converters and inverters, facilitating the seamless integration of the energy storage system with the electrical grid. Multiple simulations are conducted, employing nonlinear control techniques to evaluate the performance of the system with different configurations, including a simple inverter, a multi-variable filter, a notch filter, and a filter-less setup. The research aims to achieve effective regulation of the DC bus within the proposed grid. The key advantage of these nonlinear controllers is their ability to compensate for reactive power and harmonic currents, resulting in a disturbance-free power network and a reduction in the Total Harmonic Distortion (THD) rate of the inverters, ultimately enhancing the overall efficiency of the power grid. This thesis contributes valuable insights into optimizing the performance of wind and solar systems along with energy storage device and their integration with the grid through advanced control and filtering techniques, with significant implications for improving the stability and reliability of renewable energy sources in the power grid

Modeling and Analysis of a 12kW Solar-Wind Hybrid Renewable Energy System

The increase in rate of depletion of natural resources in the last decade as well as the increased global focus on climate change has made the transition to renewable resources of energy a priority for various countries and organizations across the globe. The sporadic nature of energy generated by photovoltaic systems and wind energy conversion systems has led to an increased utilization of more reliable hybrid renewable energy systems. A combination of both solar and wind energy-based power generations systems reduces the impact of seasonal variation on the amount of power generated and therefore, can be used under varying weather conditions. This research aimed to design a 12kW hybrid photovoltaic-wind renewable energy system for utility scale implementation. The study provides a detailed description of various components required to create a grid-connected hybrid system. The proposed system constituted a 12.8kW PV array and a 12kW wind turbine, and the input solar and wind data were utilized for the region of Valentine in Nebraska. The selection and/or design procedure of various sub-components such as boost converter, permanent magnet synchronous generator, maximum power point tracking system, converters, etc. were also studied and elucidated in order to provide a detailed understanding of a small power hybrid generation system. The output voltage and power characteristics from the hybrid systems as well as wind and solar systems separately were generated and analyzed. Finally, a cost analysis of the hybrid system was conducted in order to calculate the payback period. Advisor: Jerry Hudgin

Survey on Photo-Voltaic Powered Interleaved Converter System

Renewable energy is the best solution to meet the growing demand for energy in the country. The solar energy is considered as the most promising energy by the researchers due to its abundant availability, eco-friendly nature, long lasting nature, wide range of application and above all it is a maintenance free system. The energy absorbed by the earth can satisfy 15000 times of today’s total energy demand and its hundred times more than that our conventional energy like coal and other fossil fuels. Though, there are overwhelming advantages in solar energy, It has few drawbacks as well such as its low conversion ratio, inconsistent supply of energy due to variation in the sun light, less efficiency due to ripples in the converter, time dependent and, above all, high capitation cost. These aforementioned flaws have been addressed by the researchers in order to extract maximum energy and attain hundred percentage benefits of this heavenly resource. So, this chapter presents a comprehensive investigation based on photo voltaic (PV) system requirements with the following constraints such as system efficiency, system gain, dynamic response, switching losses are investigated. The overview exhibits and identifies the requirements of a best PV power generation system

A short predictive Model Predictive Control (MPC) approach for hybrid characteristics analysis in DC-DC converter

Historically, the MPC has been successfully applied in drives system for over a decade. Furthermore, the DC-DC converter naturally deals with high switching phenomenon that contributes to the challenging in control approach. Its operation conventionally associated with PI/PID controller in order to meet the desired output. However, the PI/PID controller lacking in getting a good transient response since this controller highly depends on the controller gains. Recently, an advanced controller has been proposed in the literature for the purpose to enhance the DC-DC converter performance. Hence, in this thesis, the short prediction horizon of MPC using search tree optimization that generates low switching states phenomenon is proposed. The MPC algorithm is developed based on the hybrid characteristic signals from the DC-DC converter. The load changes due to the increasing or decreasing the loads (could be happened of heating effect) will affect the tracking of the output voltage. The Kalman Filter (KF) is used for load estimation for smoothing and tracking the output voltage. The performance of short prediction horizons is being compared to PI controller in terms of transient response during the start-up scenario. The results show that the proposed controller has a better response than PI controller, which is the overshoot has been reduced to more than 50% and the settling time more faster about 25% than PI controller during start-up scenario. Therefore, this control approach for DC-DC buck converter has produced the promising output transient performance when compared with the conventional PI controller while also minimizing the switching sequence phenomenon

An Accurate Battery Charger SEPIC-Coupled Inductor Using Fuzzy Type 2

Recently, the needs of electrical energy have increased in line with the increasing population in Indonesia. Electrical in order to save the use of fossil energy, renewable is used, namely solar energy. Solar energy depends on the conditions of sunlight and the temperature of the solar panel. So, if the solar panel is directly connected to the battery, it will cause the battery be damaged. To overcome this, a controlled DC-DC converter is needed to stabilize the solar panel output before connecting to the battery. The DC-DC converter that used is a SEPIC coupled inductor converter, this converter has the ability to increase efficiency, the output polarity is not reversed, and avoid input current ripple. The control used to adjust the output of the SEPIC converter is a type 2 fuzzy logic controller because it has ability to find a set point value faster than other control logics and can handle uncertainty better than a type 1 fuzzy logic controller. The output of the SEPIC converter is used for charging lithium ion battery with a capacity 12V 21Ah. The output value of the SEPIC converter is 12.6V for charging voltage and 7A for charging current. The method used for battery charging is the constant current constant voltage method (cc-cv)

Performance Analysis Of Hybrid Ai-Based Technique For Maximum Power Point Tracking In Solar Energy System Applications

Demand is increasing for a system based on renewable energy sources that can be employed to both fulfill rising electricity needs and mitigate climate change. Solar energy is the most prominent renewable energy option. However, only 30%-40% of the solar irradiance or sunlight intensity is converted into electrical energy by the solar panel system, which is low compared to other sources. This is because the solar power system\u27s output curve for power versus voltage has just one Global Maximum Power Point (GMPP) and several local Maximum Power Points (MPPs). For a long time, substantial research in Artificial Intelligence (AI) has been undertaken to build algorithms that can track the MPP more efficiently to acquire the most output from a Photovoltaic (PV) panel system because traditional Maximum Power Point Tracking (MPPT) techniques such as Incremental Conductance (INC) and Perturb and Observe (P&Q) are unable to track the GMPP under varying weather conditions. Literature (K. Y. Yap et al., 2020) has shown that most AIbased MPPT algorithms have a faster convergence time, reduced steady-state oscillation, and higher efficiency but need a lot of processing and are expensive to implement. However, hybrid MPPT has been shown to have a good performance-to-complexity ratio. It incorporates the benefits of traditional and AI-based MPPT methodologies but choosing the appropriate hybrid MPPT techniques is still a challenge since each has advantages and disadvantages. In this research work, we proposed a suitable hybrid AI-based MPPT technique that exhibited the right balance between performance and complexity when utilizing AI in MPPT for solar power system optimization. To achieve this, we looked at the basic concept of maximum power point tracking and compared some AI-based MPPT algorithms for GMPP estimation. After evaluating and comparing these approaches, the most practical and effective ones were chosen, modeled, and simulated in MATLAB Simulink to demonstrate the method\u27s correctness and dependability in estimating GMPP under various solar irradiation and PV cell temperature values. The AI-based MPPT techniques evaluated include Particle Swarm Optimization (PSO) trained Adaptive Neural Fuzzy Inference System (ANFIS) and PSO trained Neural Network (NN) MPPT. We compared these methods with Genetic Algorithm (GA)-trained ANFIS method. Simulation results demonstrated that the investigated technique could track the GMPP of the PV system and has a faster convergence time and more excellent stability. Lastly, we investigated the suitability of Buck, Boost, and Buck-Boost converter topologies for hybrid AI-based MPPT in solar energy systems under varying solar irradiance and temperature conditions. The simulation results provided valuable insights into the efficiency and performance of the different converter topologies in solar energy systems employing hybrid AI-based MPPT techniques. The Boost converter was identified as the optimal topology based on the results, surpassing the Buck and Buck-Boost converters in terms of efficiency and performance. Keywords—Maximum Power Point Tracking (MPPT), Genetic Algorithm, Adaptive Neural-Fuzzy Interference System (ANFIS), Particle Swarm Optimization (PSO