MPPT Control Research Articles

Study and Analysis of RBFN based MPPT controller for wind energy integrated with Traction Power Supply System

The need of energy for railway is rising due to the increased speed at which trains must operate to maintain safety and dependability. This situation implies more electricity consumption and more challenges to railway operation stability. Therefore, this research suggests configuring wind energy integration with the current railway traction substations built on the current traction substation. The suggested setup consists of a converter that converts DC to DC with RBFN-based MPPT regulator wind energy source linked to a permanent magnet synchronous generator (PMSG), a rectifier, and a conventional traction system. The proposed system is modelled and analyzed by utilizing a radial basis function network (RBFN) in this study. Three traction substations: Aysha, Adegla and Dewanle:-are the subject of the case study, which integrates wind energy with the railway electric power system working at 25 kV AC. For the Aysha wind farm, a MATLAB/Simulink simulation is run in order to confirm the suggested technology. The setup with or without MPPT was used in order to evaluate the results. The proposed system produces an average output of 260.3kW without MPPT and 289.3kW with MPPT controller at wind speed of 20m/sec. The simulation findings suggest that the RBFN-based control method performs better under diverse wind conditions and is more suited for wind energy integration with traction system.

Enhancing Wind Turbine Efficiency: A Comparative Study of Two Innovative MPPT Control Algorithms

Enhancing Wind Turbine Efficiency: A Comparative Study of Two Innovative MPPT Control Algorithms

A novel design and analysis of hybrid fuzzy logic MPPT controller for solar PV system under partial shading conditions

Renewable energy resources are more useful when associated with the thermal power generation network because of their high accessibility in the environment, good system response, easy manufacturing, plus high scalable. So, the present research is going on solar power to reduce consumer grid dependency. The running of the PV network is quite easier, plus less human sources are involved. However, the solar modules’ power generation is nonlinear fashion. So, the collection of peak power from the sunlight-dependent systems is a highly challenging task. In this article, a Modified Differential Step Grey Wolf Optimization with Adaptive Fuzzy Logic Controller (MDSGWO with FLC) is developed for collecting the maximum power from renewable energy resources under diverse Partial Shading Conditions (PSCs). The introduced method comprehensive analysis has been done along with the other recently existing MPPT methods in terms of convergence speed, MPP tracking accuracy, operating efficiency of the introduced method, functioning duty value of the DC–DC boost power converter, dependence of MPPT on sunlight system, total number of sensing devices are needed, plus peak power extraction from the proposed system. Here, the sunlight power generation cost is more to limit this issue, a power converter is selected in the second objective to develop the voltage source capability of the PV network. The overall PV-interfaced power converter network is examined by utilizing the MATLAB environment.

Improving Power Quality in a Grid-Connected Hybrid Power System through the Implementation of a Fuzzy Logic Controller

The examination of modeling and designing grid-connected hybrid power systems with efficient control strategies is the focus of this study. Integration of wind and photovoltaic systems into the ac-bus bar system is a common practice in enhancing operational performance. Utilization of Matlab Simulink software is employed for the design of this hybrid system, incorporating efficient MPPT control algorithms to optimize power generation from solar and wind sources under varying climatic conditions. Analysis of the maximum power point tracking mechanism and control methods of the hybrid power system is conducted, particularly in response to changes in weather conditions such as wind speed and solar radiation. Maintaining grid voltages and Unity Power Factor (UPF) while feeding currents into the grid is essential for the hybrid system. The employment of Proportional Integral (PI) control instead of Fuzzy Logic Controller (FLC) in the voltage-oriented control approach is explored to enhance power quality.

Performance analysis of drone sqadron optimisation based MPPT controller for grid implemented PV battery system under partially shaded conditions

In recent years, growing consumer demand for sustainable energy is driving down costs and paving the way for more clean energy in the future. Since these energies are sporadic, and the load pattern does not correlate to their generation pattern, a battery storage system is required. Operating dispersed alternative energy sources, connected to the grid in this situation makes energy control an unavoidable task. The difficulty is brought on by things like intermittent sources, daytime costs, solar panel and battery size restrictions, and limitations in battery charging and discharging rates. Moreover, integrating energy storage devices into the power system network is one of the recommendations made in this work to increase the performance and dependability of these systems. Compared to the existing works reported in the literature, this study offers enhanced control techniques for hybrid Photovoltaic and Battery Energy Storage System under fluctuating atmospheric scenario. Here, an innovative approach called Drone Squadron Optimisation, which tracks the Global Maximum Power Point, is used get maximum power. Further, suggested approach is tested with various partial shading instances and the uniform irradiance condition to validate it. This research also investigates photovoltaic models and the status of the battery storage device for improved energy management in the system. Finally, the proposed algorithm assists in quickly identifying the potential benefits of a grid-connected, battery-powered rooftop solar system.

Thermoelectric power generation system intelligent Runge Kutta control: A performance analysis using processor in loop testing

As an emerging source of clean electrical power, Thermoelectric generation (TEG) finds its applications in heat removal and electricity generation as an efficient improvement tool for thermal devices utilizing fossil fuels. TEG exhibits low power density and its temperature distribution is usually non-uniform over thermal conductive surfaces. Due to the non-uniformity of heat flow and loose surface contact, non-uniform temperature distribution (NTD) appears on TEG surfaces and the TEG control problem becomes complex, multi-solution, nonlinear, and highly sensitive to operating conditions. The bypass diode activation of TEG systems streamlines the power flow in a string of series-connected modules, generating multiple peak points. Classical techniques fail to address these issues of multiple maxima and lose efficiency. To solve this problem, a novel SI-based optimization algorithm, Runge Kutta Method (RUN), is applied as MPPT control. To gauge the performance of the proposed controller several distinct case studies are used, including varying temperatures gradient, NTD, 24-hour thermal profile stochastic temperature, and experimental verification. Additionally, MPPT rating analysis, economic assessment, and statistical studies are done for comparison with other state-of-the-art control techniques. The minimum tracking and settling times have been improved by RUN to 180 ms. For the additional hardware implementation, the maximum levelized cost of energy (LCOE) is about 0.16 $kWh1.07¥kWh. The power tracking efficiency of RUN can be above 99 % with energy harvest improvements of 6.3 %.

Modified Perturb and Observe-based MPPT control of MHPCS for single-phase power distribution positioned in remote locations

Modified Perturb and Observe-based MPPT control of MHPCS for single-phase power distribution positioned in remote locations

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Control and mathematical design of PV-based renewable energy systems for LCL-SR filter-based power quality improvement

Power quality challenges arise when photovoltaic (PV) systems are integrated into power networks. This research work examines the control and mathematical design of an LCL-SR (series resistance) filter for power quality improvement in PV-based sustainable energy systems to meet this difficulty. Power quality may be improved via an LCL filter in grid-connected systems. A non-linear load-connected PV system incorporates a variety of power electronics controllers, including an MPPT controller, battery controller, and inverter current controller, which introduce harmonics into the grid. A passive filter, LCL-SR, is suggested for PV applications that interact with voltage source inverters and the utility grid or electric load to reduce harmonics. A detailed mathematical modeling is presented to design an LCL-SR filter, and its performance is evaluated under different load conditions in terms of THD (Total Harmonics Distortion) and efficiency. LCL-SR filter has 4.32% THD but in the case of LCL filter with parallel R is less THD, which is 3.61%. In terms of efficiency and THD LCL-SR is best among all compared filters which have THD is 4.32% along with an efficiency is 75.95%. The results are compared without a filter, and different configurations of LCL-SR are simulated to show the effectiveness of the proposed filter for PV & residential applications.

Ziegler-Nichols Based Controller Optimization for DFIG Wind Turbines

Wind power conversion systems are becoming the most preferable alternate renewable energy source. Doubly Fed Induction Generator’s utilization rotating parts in wind turbines is increasing with the wind power penetration to the electric grid. The observation becomes more noticeable when there are abnormal conditions within the electrical power system. In this paper, MPPT Controller is incorporated with DFIG through the MATLAB Simulink program is simulated. A conventional PI controller is tuned with Ziegler-Nichol’s (Z-N) method and was implemented to the DFIG Control Block. The rotor-side converter (RSC) is utilized to regulate the power generated by the doubly-fed induction generator (DFIG), while the direct current (DC) bus voltage is controlled by the grid-side converter (GSC). The DFIG's angular and comparative angular speeds are used to generate the reference torque value in the MPPT controller. This improved the time response, output power, electromotive torque, and other current and voltage characteristics. A comparison was made between the output evaluation of the inbuilt controller and the output evaluation of the proposed controller. The MPPT with tuned PI Controller's results show that the suggested system, which uses DFIG coupled to a rotor, is accurate, dependable, and feasible. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6966

Artificial Neural Network based MPPT Controller with Boost Converter for Varying Solar Insolation in Arba Minch

Solar energy is an elixir of energy that can be used to generate electricity with the help of technology. Ethiopia has an abundance of natural geometric to generate electrical energy from solar irradiation. Renewable energy is a promising word for making the country free of fossil fuels. However, due to low efficiency, it necessitates a variety of methods to maximise output efficiency. In this effort, a MATLAB-SIMULINK PV module model was created to stuff Maximum Power Point (MPP) from solar energy. To test the performance of the proposed model, a closed loop system was created using a conventional controller and an artificial neural network (ANN). The developed model was put through its paces with various isolations. In response to uncertain solar irradiation conditions, the Artificial Neural Network ANN associated with the PV module delivered fast response and minimal oscillations.

Intelligent Energy Management System: Harnessing Fuzzy Logic for Charge Control

Aim: Charge controller that leverages fuzzy logic was developed to enhance the efficiency of traditional charge controllers. A model was constructed and assessed for its performance through MATLAB/Simulink. It allows for flexibility in controlling the variability of renewable energy sources. It also improves the efficiency and lifespan of energy storage systems while minimizing the impact on the grid and environment. 
 Study of the design: The PV system consists of a PV module, PWM inverter, MPPT controller and DC-DC converter which are connected using MATLAB/Simulink environment.
 Methodology: we conducted validation tests to substantiate the advantages of our fuzzy charge controller. The creation of fuzzy rules was based on the system's performance and subsequently translated into precise values with the assistance of a fuzzy inference system. This Research Project was Completed in Two Months.
 Results: Our findings clearly demonstrate that the implementation of fuzzy logic control results in superior charge controller performance. This, in turn, safeguards against battery discharging and overcharging during unpredictable weather conditions.
 Conclusion: These protective measures are made possible through the decision-making capabilities of the DC-DC buck-boost converter, which effectively regulates both the voltage and current output of the PV module.

A new approach to MPPT hybrid incremental conductance-sliding mode control for PV grid-connected

As photovoltaic energy is clean, renewable, and less noisy, it is increasingly integrated into the grid. This integration aims to overcome energy deficits and get rid of pollution from conventional sources. In this paper, a two-stage configuration of PV energy conversion to a three-phase grid has been studied. The control of this configuration can be divided into two parts, such as DC bus control and AC bus control. The DC bus is controlled by MPPT control. The AC bus is controlled by DC link voltage control, phase-looked loop control, and voltage source inverter control. The contribution in this study aims to improve the quality of energy injected via a new hybrid approach to MPPT control. The proposed control is a combination of robust sliding-mode control and incremental inductance. Unlike conventional hybridization, the proposed control estimates correct the error upon entry of the conductance incremental control. This correction provides an adaptive incremental step for increment conductance control. The proposed control is compared with three other controls under MATLAB/Simulink. Such as incremental conductance, variable step size incremental conductance, and conventional hybrid MPPT incremental inductance-sliding mode control. The simulation results show a remarkable minimization of the ripple phenomenon and the chatter phenomenon. Thus, the quality of the energy injected into the network is improved, such as by reducing the total harmonic distortion of the current and increasing efficiency.

A hybrid approach with MPPT controller for weed cutting based on solar powered lawnmower with minimal intervention of human involvement adopting IoT technology

A hybrid approach with MPPT controller for weed cutting based on solar powered lawnmower with minimal intervention of human involvement adopting IoT technology

Variable Power Tracking Control Strategy of Doubly Fed Induction Generators for Fast Frequency Responses

Frequency regulation and droop control of doubly fed induction generators (DFIGs) can quickly respond to frequency changes and reduce the maximum rate of frequency (MROFF) in power systems. However, due to real-time dynamic changes in the MPPT control loop, the ability to improve the lowest frequency point is limited. Therefore, this article first describes an in-depth analysis of the dynamic characteristics of the incremental power of frequency regulation with droop control using an equivalent linear model. The limitations of improving the lowest frequency point under the influence of dynamic changes in the MPPT control loop are revealed. Secondly, to address the impact of these dynamics, an improved decoupling frequency regulation (IDFR) strategy based on power tracking is proposed, aiming to increase the maximum frequency deviation (MFD) and MROCOF. Then, in order to overcome the difficulty of adjusting control coefficients in the IDFR strategy, an adaptive control coefficient tuning fuzzy control method based on frequency deviation and ROCOF was proposed to flexibly adjust control requirements under various working conditions, thereby improving the control stability and performance of the system and effectively solving the problem of control coefficient allocation. Finally, to verify the frequency regulation performance of the proposed IDFR strategy under various operating conditions, simulations were conducted based on different disturbances and wind conditions. The results show that the proposed IDFR strategy significantly improves the system MFD and MROCOF improvement ability under various conditions.

Analyzing grid connected shaded photovoltaic systems with steady state stability and crow search MPPT control

Analyzing grid connected shaded photovoltaic systems with steady state stability and crow search MPPT control

Experimental investigation of ANFIS-PSO MPPT control with enriched voltage gain DC–DC converter for grid-tied PV applications

Experimental investigation of ANFIS-PSO MPPT control with enriched voltage gain DC–DC converter for grid-tied PV applications

Control System of PV Source for DC Distribution System With Seamless Operation Transition Between I-V Droop Control and Current Control for MPPT

Control System of PV Source for DC Distribution System With Seamless Operation Transition Between I-V Droop Control and Current Control for MPPT

An LCS-HB Algorithm Optimized MPPT Controller and DC–DC Boost Converter for PV System at Partial Shading Conditions

In this study, a hybrid localized crow search asserted honey badger (LCS-HB) algorithm is used to optimize the MPPT controller for PV systems operating under PSC with the goal of achieving MPP. The developed approach’s main objective is to track the solar PV system’s highest power point while operating under PSC. A PID controller’s duty cycle can be changed to regulate the output voltage of a DC–DC converter. The proposed LCS-HB algorithm is used to tune the PID controller variables for improved performance. The proposed work is modeled in MATLAB/Simulink to conduct the experimental analysis. By contrasting the results with those of the conventional methods, the effectiveness of the proposed model is confirmed. Based on these discoveries, the experimental outcomes supported the algorithm’s high level of precision in tracking the MPP of the solar array working under PSC. Future studies will address hardware validation of the proposed MPPT technique using the LCS-HB method.

SMGSA algorithm-based MPPT control strategy

SMGSA algorithm-based MPPT control strategy