Microgrids (MGs) are a solution to excessive load demand and power grid failure because they provide utility systems with stability and continuous power flow. A controller for a Fuzzy Logic System with neural network that is adaptable (Adaptive Fuzzy Neural Network Inference System) is suggested for a hybrid microgrid that is fueled by renewable energy sources. A modern high-gain Landsman converter is one of the numerous converters in use is employed to increase the solar output and achieve a steady DC-link voltage to provide outputs with high efficiency. The converter control is accomplished via the ANFIS method, a noteworthy substitute that combines two computational techniques: Neural networks and fuzzy set theory (ANN). Using the Crow Search Algorithm (CSA), the ANFIS constraints are reinforced to boost the convergence rate and dependability predictive accuracy rate. PWM-based rectification system controlled by a Proportional-integral control algorithm then links the wind system and microgrid configuration. When power from solar and wind sources is scarce, energy storage battery system (BESS) is used to hold energy for use in the DC connection. The MATLAB platform simulates evaluations of the control strategy. The proposed Landsman converter with high gain demonstrates superior energy efficiency compared to the Super Lift Luo converter, which in turn makes it a more effective solution for stabilizing DC-link voltage and boosting RES outputs in hybrid microgrid systems.

The expenses associated with conventional energy have increased dramatically in recent times due to factors such as climate change, environmental degradation, and the depletion of fossil resources. Thus, there is an urgent global need to use clean, innovative energy. Worldwide, there is widespread usage of electric cars (EVs) with smart grid technologies. More and more attention has been directed into the Vehicle-to-Grid (V2G) system. The Bi-directional grid-connected AC/DC converter, which enables bilateral power transfer while satisfying grid power quality criteria, is an essential component of the V2G hybrid system. A Bi-directional Power flow control for three phase grid tied converter is presented in this study. It can offer bidirectional power transfer between EVs and the grid in addition to correcting reactive power and lowering power grid volatility. Introducing the V2G system setup and creating the mathematical model for the AC/DC converter are the first tasks. The next step is to propose an analysis of the PI control method and build a controller. D-Q-0 theory finds use in efficiently synchronising the grid and controlling ripple current in the grid current. The basis for the system simulation model is MATLAB/Simulink.

The rise in popularity of electric cars (EVs) has resulted in a growing need for ecologically responsible and effective charging facilities. There is a need for efficient and reliable power management systems that can integrate multiplesources of power to charge EVs. This project aims to address the challenges of integrating multiple sources of power for charging EVs while ensuring high power quality with reduced harmonic distortion. This aims to develop a systemthat combines multiple power sources using a novel multisource inverter and feed theminto the grid with both linear and non-linear loads. A PSO (particle swarm optimization) adjusted PI (proportional integral) controller controls an enhanced PFC (power factor correction) converter that serves as an AC supply and a charging station for electric automobiles. Another input source battery converter is also fed into the system. The multiple power sources are then combined using a four-leg inverter that is controlled by a PI controller with DQ theory transformation. The system ensures that the power fed into the grid is of high quality with reduced harmonic distortion. This project has potential applications in industrial, commercial, and residential power systems where multiple power sources are required to beintegrated into the grid. The entire work for enhancing chargingcapacity of the EV through improved MS is carried out in MATLAB 2021 and simulation responses are obtained. This proposed work has a THD Value of 1.24% when the papers arecompared and the Settling time obtained using PSO-PIcontroller is 0.25s.

In order for the globe to meet its energy requirements in the future, renewable energy sources (RES) are essential. Many dispersed generations like RESs and non-linear loads are features of the power electronics-based current power system design, which has caused a number of power quality (PQ) issues to arise. Regarding a double fed induction generator (DFIG) driven wind energy conversion system (WECS), this research suggests an optimal control mechanism. In this paper, the benefits of a metaheuristic optimizer, WOA, are applied to determine the optimal tuning of the PI controllers in the study's control system. The sophisticated controllers system is used for the adaptive modification of the discontinuity control gain, reducing the phenomenon of chattering in the stimulation network while maintaining the resilience of the closed-loop system. The PWM rectifier's highest possible voltage is tracked by the PI Controller, which also provides increased dependability. Initially, the DFIG and turbine modeling will be introduced. The rotor dimensions are then changed to achieve vector control over both the reactive and active power when utilizing the suggested whale optimization tuned PI controller. The converter's objectives are to deliver input currents with a reasonable harmonic content in order to run at a power factor of one. Extraction of maximum real power occurs at the interface between the DFIG and the powers electronic converter. When compared to other controlling schemes, the WOA shows highest accuracy of 94.34% and less THD value of 0.98%. Lastly, it's going to use the MATLAB 2021a / Simulink program to do a number of computational simulations in order to verify the suggested controls.

For high voltage submissions, this architecture is recommended since all of the switches utilized have voltages lower than their maximum output voltage. One advantage of this proposed configuration is that it can increase the input power sources' magnitude by utilizing switched capacitor units. Switching angle-based control has been used to decrease the number of components needed to offer the greatest number of voltage levels at the output and improve power delivery quality. The input DC source is converted into a 29 level output voltage using three DC sources, twelve switches, and one switched capacitor. Based on utilizing the maximum voltage on the power switches and utilizing less hardware, the proposed construction performed better, according to the data. The effectiveness of the recommended inverter is validated by obtaining several test results for the 29-level inverter. Finally, the proposed 29-level inverter would be a preferable choice for usage in solar systems. This project is developed using the Mat lab 2021a environmental software. When the inverters are compared, the Twenty Nine level inverter has a THD Value of 3.66% and the paper's total efficiency is 92.6%.

The growing need for solar energy as a renewable resource has created new difficulties for the energy sector. Nonetheless, the ability of photovoltaic (PV) power generation technology to be associated to the grid power generation organization and satisfy the demand of rising energy consumption is one of its primary benefits. Partial shading has a negative impact on a PV system's performance ratio (PR). It is frequently seen in residential rooftop solar photovoltaic (PV) installations. This consequences in a reduction in the power output of the solar panel. In order to get around this, continuous duty cycle variation schemes for Maximum Power Point Tracking under partial shading conditions have been proposed. In this scheme, a high gain self-lift Luo converter is connected to PV module to enable the highest output voltage possible under any condition. An Improved Incremental Conductance (InC) based MPPT, has been implemented to track the MPP even when there are other local maxima present. The proposed method increases the system's compactness by tracking Maximum Power Point (MPP) through continuous duty cycle variation of the converter without the need of expensive parts like microprocessors and signal converters. In a grid interface, the converter's objectives are to deliver input currents with a manageable harmonic content and run at unity power factor. Using MATLAB 2021a/Simulink, a number of numerical simulation is achieved to validate the proposed scheme.

Global warming and other negative environmental effects have been caused by the mining of fossil fuel resources as a result of the sharp rise in the demand for power around the world. By using electric vehicles (EVs), which have a number of socioeconomic and environmental advantages, several governments are attempting to transform conventional forms of transportation into green transportation systems. These EVs don't directly use fossil fuels, but they do use more of them because of the electricity they receive from the fossil fuel-based power distribution infrastructure. Because of this, scientists are trying to create more affordable and ecologically friendly renewable sources of energy in an effort to reduce the difference between the availability and demand of electricity. Here, we employ a grid-tied method based on hybrid renewable energy sources (HRES) to charge EVs. Uneven voltage, frequency, and supply-demand are all consequences of EVs and the grid, which affect the overall power system. To solve these problems, this project presents workable optimization strategies. Utilizing a Probabilistic Neural Network (PNN), the lithium-ion battery's state of charge (SOC) is calculated when it is first charged utilizing hybrid renewable energy sources. In this study, voltage is increased or decreased by a bidirectional BIFRED converter that is Perturb and Observe (P&O) MPPT(maximum power point tracking) regulated. Power is removed from the grid if the demand for EV charging is greater than the amount of power produced by HRES; if the demand for EV charging is less than the amount of power produced by HRES, excess power is fed back into the system. PI controllers are also used to control active and reactive electricity and synchronise the grid. MATLAB simulation is used to validate the proposed approach.

Power systems worldwide are looking into new ways to generate electricity as a result of the depletion of fossil fuel-based energy supplies worldwide. The major emphasis of attention is on clean and renewable energy resources (RES), of which wind and solar are two of the best. Although it is sporadic, solar energy is abundant in tropical regions. Therefore, connecting it to the grid is the best way to use it as a dependable power source. If it utilized as a stand-alone power source, measures to address its dispatch ability problems (resulting from its sporadic availability) must be taken to ensure that it continuously provides enough power to its linked loads. The suggested system is said to be interactive since it can function in both islanded and grid-connected modes. Additionally, load management is provided when operating in island mode under low illumination conditions. The proposal outlines a two-stage solar PV system, the first of which uses a KY converter (shows voltage gain 1:12) to maximize power extraction and function as a voltage booster. Cascaded ANFIS controller is employed to tune the parameters of the converter. A two-leg inverter that feeds the available electricity into the grid operates in the second stage. This work is developed using Matlab 2021a simulation.

This project is about developing an effective way for ensuring optimal flow of electricity in a renewable energy-powered water pumping system. This work intends to find a worldwide monitoring method with an ideal modification of the DC bus power augmentation in this setting. A Neuro fuzzy logic algorithm is used as the primary optimizer of the supervision power transfer in the proposed study to enhance the diverse power flows shared between the network's components. The proposed configuration also eliminates the undesired capacitive feedback cycle, as well as the undesired conductivity through the body transistor of the inactive switches in the earlier established Bridgeless Isolated Cuk converter. This enhances the charger's performance greatly. A wind turbine (WT) is obligatory as the primary renewable energy basis, and it is linked to a battery energy storage system (BSS) to maintain the electrical supply stability of an induction motor pump unit. The proposed converter's improved effectiveness and power indexes are studied to verify its excellent charging performance under all operational situations. In this paper the comparison graph between the MPPT tracking efficiency of the proposed controller obtained value is (93.2%) and the efficiency of the proposed converter obtained value is (91%).

Recently, the use of renewable energy sources (RES) has grown in prominence as a way to guarantee sustainable growth. RES include geothermal, wind, biomass, and solar power. However, because of their minimal maintenance requirements and simplicity of supply, wind and solar power are the most often used RES. In order to enhance power quality for grid-connected networks, this work suggests and develops a dependable controller for a Boost converter that comprises a 13-level inverter. Several voltage sources are required for MLI, and because of inverter architecture and switching techniques, most MLIs have different loads that utilize the power sources. Employing a boost converter managed by a PI controller enhances PV output since it is more straightforward, useful, and provides superior power tracking. An asymmetric cascaded 13-level multilevel inverter (MLI) which is around 1.54% less THD and shows highest efficiency of 94.5% is presented in this work as a step across improving grid power quality (PQ) and converters. The sinusoidal PWM approach is used to regulate the inverter. The LC filter is used to feed the MLI output to the grid or load. MATLAB simulation is used to verify the suggested approach.

The increasing need for power and the depletion of fossil fuels, the electric vehicle (EV) and renewable energy production have advanced significantly. In addition, when EVs are grouped together at a charging station, it consumes a tremendous quantity of power, which might have an adverse effect on the grid's ability to function. Consequently, it is thought that implementing renewable energy on the charging facility's end offers an intriguing a mutually beneficial outcome. Charge stations may significantly lower the amount of energy they need from the electrical grid and hence lower the amount of grid capacity that is needed by incorporating renewable energy. Therefore, this study proposes a Novel Integrated Improved GWO (IGWO) (shows less settling time of 0.15 secs) optimized Zeta-Boost Converter (ZBC) for Grid Tied PV Charging Station Uses. While photovoltaic panels offer many benefits, they also have several drawbacks, such as their reliance on certain environmental factors. A PI controller built around optimization techniques was implemented to address this. At first, solar power and the smart grid are used to charge lithium-ion batteries. Utilizing an inbuilt DC/DC ZBC with a high voltage gain of 1:12, surplus power is sent to the grid via a single-phase inverter. Simulink and MATLAB 2021a are used to simulate and evaluate the suggested approach.

Class Topper Optimized Proportional Integral (CTO-PI) and Synchronous Reference Frame (SRF) based Power Quality (PQ) Improvement has been proposed in this novel. The proposed system enables Distribution Synchronous Static Compensator (D-STATCOM) to provide a variety of additional services, such as reduction of voltage flickers, elimination of harmonics, load balancing, voltage sag, and impulsive and oscillatory transients. Additionally, it actively feeds electricity into the utility grid. D-STATCOM captures the fundamental inphase and quadrature components of current exactly because it uses CTO-PI to function. By introducing high-gain at the third order harmonic, it reduces stationary errors in the 3 ϕ inverter's current control loop. In order to effectively mitigate harmonics in the source current, the proposed LCLComb filter reduces the current harmonics Using SRF, it ensures quick power balance between the inverter unit and the electric grid. For a variety of load types, DSTATCOM's performance is determined to be satisfactory when using the proposed control algorithm. The proposed approach is validated through simulation using MATLAB Simulink software.

Power and energy are two key requirements in today's contemporary environment. As the need for energy grows by the day, the final answer to this type of issue is to implement renewable energy sources. Since the various renewable energy sources have varied energy levels at different time durations, it is impossible to connect them all to a single DC bus in nature. Furthermore, this single device is inadequate and inadequate for energy delivery. This sparks the concept of Regional Energy Sources. This gives rise to the idea of Distributed Energy Sources. Thus, dispersed electrical power is described as the substitution of one UPS unit with numerous smaller UPS units operating in addition, increasing the supply method's dependability as well as energy capabilities. A Second Order boost converter helps to increase the PV system's output voltage. The Second Order boost converter raises the PV organization's output voltage. An Adaptive Neuro Fuzzy (ANFIS) based Maximum Power Point Tracker (MPPT) technology is used to track the maximum power from PV system. A PWM generator raises the duty sequences on time when the productivity reference signal is given to the dc-dc converter. The PV system's output voltage is increased using the Interleaved Boost Converter. Droop techniques are used in parallel-connected DC to DC converters to distribute power fairly. The droop factor is adjusted to balance the charge levels of energy storage battery devices. Through the use of a bidirectional battery converter, the excess energy from the PV system is stored in a battery and promotes equitable current sharing. An approach known as proportional integral (PI) controller is used to maintain an infinite dc link voltage. The proposed strategy is validated through simulation using MATLAB 2021a /Simulink software. In this paper the comparison between the accuracy of the converter has 92.4%, voltage gain has 10V and the ANFIS tracking efficiency has 93.5% is predicted.

Hybrid concentrated solar power/photovoltaic systems (CSP/PV) combine the advantages of the two separate systems while reducing their drawbacks. The design of such a hybrid system is challenging due to the various trade-offs between the thermal and electrical performance, and the overall system complexity. A reliable simulation model that includes all relevant optical, thermal, and photovoltaic aspects, can therefore be extremely useful to analyse the combined system performance and fine-tune the various design parameters. While multi-physics modelling of photovoltaic systems is well established, this is not the case for hybrid CSP/PV. In this paper, a novel multi-physics framework is presented for a hybrid system consisting of a parabolic trough with integrated PV cells covered by a dichroic coating, focusing incident sunlight towards a thermal receiver. Instead of monofacial PV cells, bifacial cells are considered for harvesting also the diffuse and ground reflected light at the back of the trough. The presented framework relies on an existing simulation tool for PV modules, that is combined with a ray-tracer that includes the spectral beam splitting functionality of the coating, and a novel 1.5D thermal model for the receiver tube. Long term outdoor monitoring results are used to predict the averaged, time-resolved annular thermal and electric energy yield of the system. This energy production is compared for three different multilayer coating designs with an increasing amount of (TiO 2 , SiO 2 ) layers. These results show that the amount of reflected sunlight towards the thermal receiver can be enhanced at the expense of the transmitted sunlight towards the PV cells, when a higher number of layers are used. The reduction of the incident power on the PV cells is however almost fully compensated by the enhanced spectral match of the transmitted light with the spectral response of the considered bifacial cells, in addition to the enhanced cell efficiency due to the lower thermalization losses. This results in superior system efficiency, for the application scenario where the generated thermal energy is also converted in electrical energy, and geographical locations with sufficient direct sunlight; a conclusion that is drawn from comparing the total electrical energy yield in Spain and Belgium, as a function of the thermal to electrical conversion fraction.

Peak clipping, load sharing, and an artificial neural network (ANN) based demand forecast algorithm are the methods used in this research to optimize energy usage and increase grid stability. Photovoltaic (PV) solar array linked to a boost converter under proportional-integral (PI) controller control makes up the system. The Self-Lift Luo converter is mounted between a three-phase grid-connected inverter and a bidirectional battery arrangement. To achieve energy management for the proposed system using Super Capacitor and Bidirectional Battery converter along with battery system. The WECS with DFIG, AC-DC conversion takes place with the aid of PWM rectifier, and the control of rectifier is carried out with an ANN controller. The ANN approach is used in demand-side handling to mechanically switch loads to an off status and estimate the greatest demand in the load. First, by taking advantage of cost-saving options, consumers may lower their power costs by modifying the schedule and quantity of electricity consumption. Second, the energy system gains overall efficiency when energy use is shifted from peak to non-peak hours. Techniques for peak cropping and distribution of load guarantee effective use of energy resources and support grid stability. The demand side management system also enhances consumer awareness and engagement by providing real-time information and incentives for load shifting. By actively involving consumers in energy management practices, the project fosters a more sustainable energy consumption culture. Finally this project is implemented and results are carried out in MATLAB simulation 2021. This proposed work has a THD Value of 0.97 percent when the controllers are compared and the Efficiency obtained using Self-lift Luo converter is 93 percent.

In recent times, hybrid energy storage systems (HESS) have emerged as a global alternative for supplying ongoing, dependable, and sustainable energy for electronic gadgets. For effective power management, the ideal Artificial Neural Network Based Droop Management for HESS based direct current microgrid is created in this study. The voltage control loop and current control looping of the battery and SC are controlled by three Artificial Neural Network controllers, sequentially. The voltage control loop adjusts grid voltage, whereas the current control mechanisms mitigate for power imbalances between the SC and the battery when the source and load vary. Bidirectional Buck Boost Converters (BBC) are modeled, analyzed, and designed. The best Droop-ANN controller is built for a BBC with a battery and a SC for retention. With modifications in source and load fluctuations, the efficiency of the proposed optimum Droop ANN-controller-based HESS is examined. With modifications to the supply and load, the controller successfully adjusts the DC grid voltage. The recommended optimum ANN controller's efficacy is compared to the traditional PI controller technique for HESS. Mat lab 2021a software is used to validate the proposed work. The comparative table shows that the recommended converter has a settling time of 0.2 seconds.

The current breakthroughs in energy storage technologies, power electronic converters, motor drives, and appropriate control theories have the globe paying special attention to electric vehicles, or EVs. One of the home transportation technologies that will soon be greatly favored over traditional automobiles is EVs. Since the power produced by DC sources fluctuates simultaneously and is not stable, converters are necessary to alter the power as needed. The potential of interleaved Single-Ended Primary Inductor Converter (SEPIC) technology to increase DC source voltage for EV applications is the main focus of this study. Given the inherent instability of DC power from sources, converters play a crucial role in regulating power output. A Proportional-Integral (PI) controller is employed to manage converter voltage, and optimization of PI controller parameters is achieved through Firefly optimization. The stabilization of the DC link voltage contributes to enhanced battery performance, making it a promising solution for the EV industry. The proposed work is simulated in MATLAB to validate the anticipated outcomes of the process.