Maximum Power Point Tracking Approach Research Articles

Implementation of a low-cost current perturbation-based improved PO MPPT approach using Arduino board for photovoltaic systems

In photovoltaic (PV) systems, the conversion of solar energy into electrical energy by the PV module is influenced by various factors, including sunlight intensity and temperature. To achieve optimal performance, it is crucial to accurately track the maximum power point (MPP) of the PV module. Among the numerous MPP tracking (MPPT) techniques that have been developed, the perturbation and observation (PO) method has gained significant attention due to its simplicity and reliability. However, the fixed perturbation step size used in the traditional PO algorithm can result in poor tracking capability, excessive ripple, and drift, leading to high power loss and low tracking efficiency. To address these limitations, this paper proposes an improved version of the PO strategy that introduces an adaptable step-magnitude mechanism, utilizing current perturbation instead of the voltage perturbation typically employed in the classical PO method. This enhanced indirect PO approach maintains low complexity and can be easily implemented on a cost-effective Arduino Uno board. A streamlined C++ code integrates the improved PO MPPT strategy with a Proportional Integral Derivative (PID) controller, eliminating the need for separate PID blocks and further reducing system complexity. To evaluate the effectiveness of the proposed technique, comparative analyses are conducted against the traditional PO algorithm, particle swarm optimization (PSO), fuzzy logic control (FLC), and a recently introduced approach, the zone voltage (ZV) method. Simulation results using Proteus software demonstrate that the improved PO approach outperforms the other techniques in various aspects, including achieving the highest static and dynamic tracking efficiencies of 99.38% and 99.88%, respectively, negligible power loss and fluctuations, the fastest convergence speed, and the shortest tracking time of 0.19 seconds.

A Methodology to Optimize PMSM Driven Solar Water Pumps Using a Hybrid MPPT Approach in Partially Shaded Conditions

Solar water pumps are crucial for farmers, significantly reducing energy costs and providing independence from conventional fuels. Their adoption is further incentivized by government subsidies, making them a practical choice that aligns with sustainable agricultural practices. However, the cost of the required solar panels for the chosen power makes it essential to optimize solar water pumping systems (SWPS) for economic viability. This study enhances the efficiency and reliability of permanent magnet synchronous motor (PMSM)-driven SWPS in rural areas using hybrid maximum power point tracking (MPPT) algorithms and voltage-to-frequency (V/f) control strategy. It investigates the sensorless scalar control method for PMSM-based water pumps and evaluates various MPPT algorithms, including grey wolf optimization (GWO), particle swarm optimization (PSO), perturb and observe (PO), and incremental conductance (INC), along with hybrid combinations. The study, conducted using MATLAB Simulink, assesses these algorithms on convergence time, MPPT accuracy, torque ripple, and system efficiency under different partial shading conditions. Findings reveal that INC-GWO excels, providing higher accuracy, faster convergence, and reduced steady-state oscillations, thus boosting system efficiency. The V/f control strategy simplifies control mechanisms and enhances performance. Considering system non-idealities and maximum duty cycle limitations, PMSM-based SWPS achieve superior efficiency and stability, making them viable for off-grid water pumping applications.

Enhanced MPPT approach for grid-integrated solar PV system: Simulation and experimental study

The global solar energy utilization has significantly risen, mostly due to the technological, economic, and environmental advantages it offers, particularly through the implementation of photovoltaic (PV) systems. The energy output of PV systems is contingent upon climate conditions. A new hybrid two-stage maximum power point tracking (MPPT) system developed to optimize power extraction and increase efficiency in PV systems. The system consists of two stages: a perturb and observe (P&O) MPPT for regulating the reference voltage in the first stage, and an enhanced model reference adaptive controller (EMRAC) for adjusting the DC-DC converter duty cycle in second stage. This system is designed primarily for 100.7 kW three-phase grid-integrated PV systems operating in environments with rapidly changing solar irradiation and temperature levels. In order to demonstrate efficacy of the suggested approach, a probabilistic evaluation is conducted across three levels of uncertain scenarios, namely ropp, step irradiance profile, and rapid changing irradiation and temperature profile. The proposed approach attained maximum power point (MPP) in 0.11 s and had an average tracking efficiency of 98.28 %. Quantitative, statistical, comparative and experimental analyses utilizing the dSPACE 1202 reveal the effectiveness of the proposed control strategy and demonstrate the superior performance over P&O, INC and ANN based on the power output, response time, power loss, efficiency, and error rates.

A modified particle swarm optimization-based adaptive maximum power point tracking approach for proton exchange membrane fuel cells

Fuel cells are one of the most promising renewable energy sources, offering advantages like reliability, eco-friendliness, and low pollutant emissions, which have spurred rapid advancements in power generation technologies. However, fuel cells face significant challenges, including high initial costs, limited fuel availability, and the difficulty of maintaining operation at the maximum power point, which hinders their use in stand-alone applications. In this paper, a Modified Particle Swarm Optimization (MPSO) method is proposed for maximum power point tracking (MPPT) to optimize the power output of Proton Exchange Membrane Fuel Cells (PEMFCs). The proposed method dynamically adjusts to key operational parameters such as cell temperature, hydrogen partial pressure, and membrane water content, areas that have not been comprehensively addressed in previous research. In this paper, an MPSO algorithm-based MPPT tracking approach without a PID controller is proposed to achieve the maximum power point (MPP) of a PEMFC. Under rapid temperature fluctuations in the fuel cell, the proposed MPSO MPPT method achieved a maximum power of 1223.5 W with an average of 5.66 iterations. In comparison, the meta-heuristic particle swarm optimization (PSO) method and the conventional perturb and observe (P&O) method achieved maximum power outputs of 1218.5 W and 1213.65 W, respectively, with PSO requiring 12.33 iterations. Additionally, the proposed approach showed improvements in power efficiency by 2.47 %, 2.87 %, and 13.58 % for the Jaya algorithm. demonstrating effective MPPT tracking under different operating conditions and perturbations. The MPSO method is implemented in the Simulink/MATLAB environment and is compared with the Perturb & Observe (P&O) and Conventional PSO (CPSO) methods. The results demonstrate that the proposed MPSO approach outperforms these traditional techniques in terms of tracking speed, efficiency, and stability under varying conditions. This successful implementation lays a strong foundation for future integration into real-world PEMFC systems.

The improvement of field-oriented control performance for double stator IPMSM based on NFC powered by dual photovoltaic solar panels

This paper presents a proposed method Neuro Fuzzy Control (NFC)-Field Oriente Control (FOC)-Space Vector Modulation (SVM) using IP controller for a five-level NPC inverter-Double Stator Interior Permanent Magnet Synchronous Motor (DSIPMSM) supplied by two photovoltaic solar panels Maximum Power Point Tracking (MPPT) Fuzzy Sliding Mode Control (FSMC). The torque generated and the magnetic flux of the DSIPMSM may be independently controlled thanks to FOC, while IP controllers are used to minimize dq axis current errors to zero. A boost converter is made up of an inductor, a capacitor, and a separate input-output connection. A MPPT approach based on the FSMC algorithm is used to extract the maximum amount of energy from the solar panel while controlling the inverter duty cycle. The NFC tool is an extremely potent instrument that bridges the gaps created by the fuzzy logic controller. The proposed method’s worth was demonstrated by the simulation results in terms of durability, decoupling between the d-q axis, and dynamic performance.

An efficient implementation of three-level boost converter with capacitor voltage balancing for an advanced MPPT approach in PV Systems

Ensuring optimal extraction of power from photovoltaic (PV) systems under diverse climatic circumstances is a significant challenge, which requires an efficient Maximum Power Point (MPP) Tracking (MPPT) algorithm and an adequate conversion stage. Therefore, this paper proposes an advanced MPPT strategy implemented by a Three-Level Boost converter (TLB), which serves as the interface between the PV generator and the DC load, offering advantages such as reduced output voltage distortion, minimized inductor current ripple, and decreased switching losses. To achieve more accurate tracking, a robust voltage balance control across the TLB converter capacitors is also suggested. The proposed system is meticulously implemented and evaluated using MATLAB/Simulink, demonstrating its robust control capabilities in MPP tracking and voltage balancing across the TLB converter capacitors under diverse climatic scenarios. Furthermore, comparative analysis under various test circumstances including static and dynamic insolation conditions and sudden load variations, shows that the suggested MPPT algorithm offers noteworthy improvements over the InC algorithm. Additionally, a thorough comparison with existing research methods proves its effectiveness over these methods. The advanced MPPT strategy achieves a steady-state error of 0 W, an average tracking efficiency between 99.13 % and 99.44%, and fast-tracking velocity. Further, a comprehensive real-time evaluation using the cutting-edge RT-LAB platform confirms the consistency of the results with the simulation outputs, emphasizing the robustness and low implementation complexity of the overall proposed control strategy.

Efficient maximum power point extraction using hybrid optimisation based parameter estimation model of solar double diode model

ABSTRACT PV systems operate most efficiently when they are operated at their Maximum Power Point (MPP). Despite the wide range of MPPT approaches available, there is potential to improve the efficiency of MPP Tracking (MPPT). One method to achieve this improvement is to use model-based MPPT techniques, which can speed up the MPP search process by estimating the MPP with a high degree of accuracy. The main objective of the research, which is to create an efficient double-diode model-based MPPT technique with a focus on hybrid Optimisation, is informed by this justification. The suggested method for predicting the MPP voltage makes use of an accurate double-diode model for PV modules that is dependent on temperature and irradiance. This research employs a parameter estimation system based on the proposed Optimisation algorithm known as Memory Integrated Sand Cat Swarm Optimisation Algorithm (MI-SCS) to overcome the limitations of standard parameter estimate methods, which are related to the availability of experimental p-V curves or datasheets with precise temperature and irradiance values. The method that is being given is based on the integration of the Crow Search Optimisation (CSO) Algorithm with the Sand Cat Swarm Optimisation (SCSO) algorithm.

Enhanced Adaptive Dynamic Surface Sliding Mode Control for Optimal Performance of Grid-Connected Photovoltaic Systems

This study presents an enhanced, adaptive, and dynamic surface sliding mode control (SMC), a cutting-edge method for improving grid-connected photovoltaic (PV) system performance. The suggested control approach uses dynamic SMC and adaptive approaches to enhance the robustness and efficiency of a system. Proportional–integral (PI) and SMC, two control systems for maximum power point tracking (MPPT) in PV systems, are compared in this paper. This study finds that the SMC system is a more effective and efficient MPPT approach for PV systems compared to the conventional PI control system. The SMC system’s unique feature is the capacity to stabilize grid voltage and attain a modulation index of less than one. An important component of power electronic system control is the index, which acts as a parameter representing the relationship between the output signal’s amplitude and the reference signal’s amplitude. The SMC method demonstrates improved robustness, efficiency, and stability, especially in dynamic operating settings with load and solar radiation changes. Compared to the PI control, the SMC exhibits a noteworthy 75% reduction in voltage fluctuations and an improvement in the power output of 5% to 10%. Regarding output power optimization, voltage stability, and accurate current tracking, the SMC system performs better than the PI control system. Furthermore, the SMC technique maintains a modulation index below one and guarantees grid voltage stability, both of which are essential for the efficiency and stability of power electrical systems.

Study, simulation and realization of a fuzzy logic-based MPPT controller in an isolated DC microgrid

This study presents a pioneering methodology for implementing the maximum power point tracking (MPPT) controller, based on fuzzy logic. Through a comprehensive performance analysis, we evaluate its effectiveness compared to the widely used perturb and observe (P&O) algorithm, which is a common MPPT technique. The main objective of our proposed MPPT approach is to improve the performance of a photovoltaic (PV) system. To evaluate the performance of the proposed MPPT controller and compare it with the P&O algorithm, we designed and simulated both controllers using MATLAB/Simulink. We also implemented a prototype of the controllers using an Arduino Mega board, and evaluated their performance under real operating conditions. The experimental results unequivocally confirm that the fuzzy logic-based MPPT controller outperforms the P&O algorithm in terms of performance, speed and accuracy. The fuzzy logic controller offers greater accuracy in tracking the maximum power point under various environmental circumstances, including variations in solar irradiation and connected load. Overall, this work contributes to the development of efficient and reliable MPPT controllers for PV systems, and provides a comparison of the performances of two popular MPPT techniques. Future research could explore other MPPT techniques and evaluate their performance using similar experimental setups.

Performance improvement of PV systems during dynamic partial shading conditions using optimization algorithms

PV power plants encounter varying levels of irradiance, temperature fluctuations, and partial shading because of the differences in sunlight conditions. Partial shading can cause an increase in the power loss, leading to a reduction in efficiency. Maximum Power Point Tracking (MPPT) is of utmost importance in the functioning of photovoltaic (PV) systems for electricity generation because it is indispensable for maximizing power extraction from PV modules, thereby increasing the overall power output. In situations where partial shading is present, the utilization of MPPT algorithms to achieve maximum power output becomes complex because of the existence of multiple distinct peak power points, each having a unique local optimum. To overcome this issue, a method is proposed that uses Darts Game Optimization (DGO), a game-based optimization process, to efficiently determine and extract the maximum power from various local optimal peaks. A population-based optimization method known as the Darts Game Optimization algorithm exists. In this approach, the optimization process begins by creating a population of random players. Then, the algorithm iteratively updates and improves the population to search for the best player or solution. In this study, the DGO algorithm was applied to the MPPT process for voltage optimization in the PV procedure. The DC-DC converter is utilized to capture the maximum available power, and the findings demonstrate that the DGO algorithm efficiently identifies the global maximum, resulting in accelerated convergence, reduced settling time, and minimized power oscillation. Through simulations, the feasibility and effectiveness of the DGO centered MPPT approach was confirmed and compared with MPPT algorithms relying on perturb and observe (P&O) and Particle Swarm Optimization (PSO). The simulation results offer compelling evidence that the DGO algorithm, as proposed in this study, proficiently traces the global maximum, thereby substantiating its practicality and efficiency.

Design and implementation of microcontroller-based solar charge controller using modified incremental conductance MPPT algorithm

This paper presents the modeling, design, and implementation of a rapid prototyping low-power solar charge controller with maximum power point tracking (MPPT). The implemented circuit consists of a 60 W photovoltaic (PV) module, a buck converter with an MPPT controller, and a 13.5V-48Ah battery. The performance of the solar charge controller is increased by operating the PV module at the maximum power point (MPP) using a modified incremental conductance (IC) MPPT algorithm. While the traditional IC MPPT approach requires a substantial amount of code and algorithmic steps to keep the PV module at the MPP, the proposed IC achieves the same process with a reduced number of lines of code, owing to its optimized algorithmic approach. By adjusting the duty cycle of the generated Pulse Width Modulation (PWM) signal, maximum power transfer from the PV module is attained during the operation of the battery charging process. The simulation model is configured and tested in Matlab/Simulink environment under different solar data (1000 W/m2, 500 W/m2, 800 W/m2) with constant temperature (25 °C). To validate the simulations, experimental studies are conducted using the developed rapid prototype with the 32-bit embedded microcontroller in the laboratory. According to the simulation and laboratory results, the maximum power tracking performance of the traditional method was found to be 95.51%, while the proposed method achieved a ratio of 96.59% with less steady-state oscillation. The average tracking efficiency has increased by 1.13%. The proposed IC tracks the MPP more accurately and provides maximum available power for battery charging at different solar radiations compared to the traditional IC approach. For low-powered electric devices, the proposed system can be used to provide a charging infrastructure solution.

Overview of Solar Photovoltaic MPPT Methods: A State of the Art on Conventional and Artificial Intelligence Control Techniques

Due to their inherent ability and environmentally friendly nature, renewable energy sources are the only real option for producing pollution-free energy in the modern era. Solar energy is one of the best possibilities in this family for supplying civilization with the power and energy it needs. Researchers can efficiently boost a PV panel’s efficiency by using the maximum power point tracking (MPPT) approach to extract the most power from the panel and send it to the load. The authors of this study examined and surveyed the sequential advancement of solar PV cell research from one decade to the next, and they elaborated on the upcoming trends and behaviours. Many maximum power point tracking algorithms (MPPTs) that are employed in photovoltaic systems (PVSs) that function under both uniform and partial shade situations are structurally summarized in this work. Well-written descriptions of the features of photovoltaic modules are followed by a variety of effective control strategies, including both AI-based and traditional controllers. In addition, appropriate knowledge of the various controllers is essential when the PV system is exposed to partial shade, keeping in mind the different control systems’ classifications in this situation. A thorough analysis of several soft computing-based techniques is also included, as well as many classical controller-based PV systems. First, well-developed traditional MPPT methods are used, followed by artificial intelligence-based MPPT approaches. Later, a thorough comparison of the various MPPT-controlling approaches is established. For PV systems operating under partial shade conditions (PSCs), the advantages and disadvantages of the various MPPT techniques are outlined, contrasted, and assessed. Future research directions for MPPT are also being investigated. A collection of several datasets pertaining to various control processes that were gleaned from various research articles has also been presented. Researchers working on PV-based MPPT and those working in the sectors of renewable energy production and environmentally sustainable development would be very interested in the findings of this review study.

A Three-Port DC-DC Converter Combined Configuration Method for PV-Battery Power Systems based on Prognostic Anticipating Controller Algorithm

The Photovoltaic (PV) system is renewable energy developed nowadays in the power generation system and it is connected to the grid. This grid-tie PV system is used to generate power and utilize consumer applications, in the grid-tie PV system the output is not efficient because of the saturation of the sunlight in the PV array and it's affected the power quality and stability of this system. In this proposed Prognostic Anticipating Controller Algorithm (PACA) system, a DC synchronization with PV and ESS is proposed to achieve improved power quality and the losses are less to compare the existing method. Due to the concern with energy emergencies, the energy obtained from sunlight is considered the most capable conventional resource. Hence, the maximum power point tracking approach is necessary for obtaining enhanced efficiency from solar panels. In the case of Direct Current (DC) application, the output obtained from the Photovoltaic (PV) array cannot be directly connected to electronic devices. For regulating the output from the PV array, the DC-DC converter is provided in between the load and the array. The converter design plays a significant role in tracking the maximum power point of the solar panel. The PACA logic controller is employed in the generation of optimal control pulse for the DC-DC converter. Moreover, in the solar photovoltaic system, the steady-state operation is performed and the various solar irradiance results are analyzed.

Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applications

Solar energy is the most promising among many renewable energy sources to meet the increasing demand. Photovoltaic (PV) based power generating solutions are expected to gain popularity as a power source for different applications, including independent and grid connected loads, due to their cleanliness, high performance, and high dependability. The efficacy of photovoltaic systems is impacted by several elements, including geographical location, positioning, shadowing effects, and local climate conditions. In order to fulfil the demands of loads, an interleaved boost converter is utilized, which has a reduced number of filters with less stress on the devices. Solar powered systems employ several maximum power point tracking (MPPT) methodologies. However, when there is partial shading, many power peaks arise, which complicates the identification of the overall peak. Although MPPT approaches are designed to measure and maintain the global maximum power point (GMPP), there are still significant oscillations observed around the GMPP with subpar settling time, tracking efficiency, and conversion efficiency. In this work, novel hybrid MPPT technique called artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) method and water cycle optimization based perturb and observe (WCO-PO) have been proposed. Artificial neural network (ANN) has been used to determine the best scaling factor in ANN-ASSPO MPPT. Performance is enhanced in ANN-ASSPO MPPT by using the optimum scaling factor, particularly in situations when the irradiance is rapidly changing/partial shading conditions. Similarly, in WCO-PO MPPT water cycle optimization is used to determine the peak power when the PV panel is subjected to partial shading conditions. The performances of proposed hybrid MPPT ANN-ASSPO and WCO-PO techniques have been compared in terms of power generated, output voltage, average settling time and conversion efficiency. The MATLAB/Simulink tool is employed to carry out the experiment for this study.

Improved solar photovoltaic performance in standalone low‐voltage direct current microgrids using sensor fault tolerant control

AbstractThe advancement of renewable energy technology has been significantly aided by solar photovoltaics (PV). Since solar PV is a weather‐dependent source, it cannot be dispatched. To ensure that the solar PV system can harvest the maximum amount of electricity for the available irradiance level, maximum power point tracking (MPPT) algorithms are used. For standalone low‐voltage DC (LVDC) microgrids to utilize the energy storage system as efficiently as possible, maximum power extraction is essential. The sensed PV voltage and current are essential for these MPPT algorithms to ensure that the maximum power point of the panel is captured. This work proposes an effective fault‐tolerant control (FTC) scheme for the solar PV subsystem in the LVDC microgrid that can seamlessly extract the maximum power despite the PV voltage sensor being faulty. The proposed FTC scheme uses a sliding mode observer (SMO)‐based method to detect and isolate PV voltage sensor faults in the standalone LVDC microgrid. The efficacy of the proposed FTC is assessed in a range of circumstances involving load disturbance, irradiance change, and various sensor fault scenarios. The performance of the proposed FTC is validated using experimental analysis on the LVDC microgrid testbed and MATLAB simulations. Given a faulty PV voltage sensor, at a given operating condition of the microgrid, the proposed FTC scheme is successful in reducing the additional power burden on the battery storage by at least two times. Consequently, the additional discharge in terms of SoC is also seen to be decreased by at least 9%. The proposed FTC technique outperforms the popular MPPT approaches for solar PV in terms of PV voltage sensor fault tolerance in the microgrid.

Maximizing Electric Power Recovery through Advanced Compensation with MPPT Algorithms

The present investigation introduces an advanced methodology for maximum power point tracking (MPPT) applied to a piezo harvester scheme. A comprehensive rectifier circuit, equipped with an embedded MPPT component, is established to optimize energy production by monitoring a DC-DC inverter connected to the rectifier. Furthermore, the system’s sensitivity error has been finely tuned to dynamically adjust its impedance unit in real time, thereby optimizing load acquisition. This innovative approach seamlessly integrates the MPPT algorithm into the piezo harvester circuit. Moreover, the vehicle’s road handling is significantly augmented through the incorporation of a robust steering front and an active differential control system. Leveraging the MPPT module, the rectifier consistently achieves a power recovery efficiency exceeding 85%, independent of varying load conditions. Additionally, a DC-DC converter circuit has been seamlessly integrated to finely adjust the output voltage to meet specified levels. Numerical simulations demonstrate the effectiveness of the harvesting scheme, extracting a substantial output power of 90 W with an overall efficiency of 70%. The improved MPPT approach, employing angles of arrival (AoA) DV-Hop control strategies, minimizes the system’s power consumption based on the Global Positioning System (GPS). The utilization of Harris Hawks optimization (HHO) and the generation of quadrants in the four-quadrant operation mode of DC motors in the wireless sensor network (RCSFs) have been significantly enhanced in this study. Simulations reveal that, at a velocity of 50 km/h, shock absorbers utilizing the received signal strength indication (RSSI) can harvest between 60 and 90 W on a class C road, based on the time of arrival (TOA). Striking a balance in ride comfort using the time difference of arrival (TDOA) as a trade-off constitutes approximately 30% of the piezoelectric harvester (PEH) system’s power consumption when operating in active suspension mode, optimized by particle swarm optimization (PSO).

MPPT efficiency enhancement of a grid connected solar PV system using Finite Control set model predictive controller

Maximum power point tracking (MPPT) is required to get the highest possible power generated from a photovoltaic (PV) cell. Numerous researchers have proposed different MPPT strategies to be able to collect maximum generated electricity from the photovoltaic cells.In this research paper, a MPPT model predictive control strategy for a grid-connected PV system is presented. Model predictive control (MPC) was used to develop and model the AC load energy tracking efficiency for the PV systems with a power rate of 20 kW at standard test conditions. For the purpose of obtaining the power tracking performance, a DC-DC boost converter, DC-AC two level three phase inverter, and control mechanism for a grid connected AC load system was examined and presented in this paper. To approximate the actual PV array properties, the PV model is used, and the MPPT approach is suggested as a way to regulate the DC-DC boost converter and get the most power possible from the PV array when compared to P&O and model predictive control system. A three-phase, two-level VSI is employed in this study that is controlled by a model predictive control system with SVPWM. The inverter's control structure is developed using a model predictive control system (inner loop current controller) with reference frame transformation (abc to dq) coordinates by utilizing PLL. The PLL is used to obtain critical information about the grid voltage. A RL filter is then used to lower the total harmonic distortion of the output and connect the inverter's output to the grid. The MATLAB R2019a environment is used to create the system model. The overall performance of the system for conventional perturb and observer is around 97.72%, while for Finite Control Set Model Predictive Controller is 99.80%, which is better than previous similar research with faster time response and less oscillation around maximum power point.

Maximum power point tracking improvement using type-2 fuzzy controller for wind system based on the double fed induction generator

Introduction. In this paper, to maximize energy transmission in wind power system, various Maximum Power Point Tracking (MPPT) approaches are available. Among these techniques, we have proposed the one based on typical fuzzy logic. Despite the somewhat reduced performance of fuzzy MPPT. For a number of reasons, fuzzy MPPT can replace conventional optimization techniques. In practice, the effectiveness of conventional MPPT methods depends mainly on the accuracy of the information given and the wind speed or knowledge of the aerodynamic properties of the wind system. Novelty. Our new MPPT for monitoring the maximum power point has been proposed. We developed an algorithm to improve control performance and govern the stator’s developed active and reactive power using the typical fuzzy logic 2 and enable robust control of a grid-connected, doubly fed induction generator. Purpose. MPPT which implies the wind turbine’s rotating speed should be modified in real time to capture the most wind energy, is necessary to achieve high efficiency for wind energy conversion, according to the aerodynamic characteristics of the wind turbine. Methods. Developing a mathematical model for a wind energy production system is complex, can be strongly affected by wind variation and is a non-linear problem. Thanks to these characteristics, thus, the Lyapunov technique is combined with a sliding mode control to ensure overall asymptotic stability and robustness with regard to parametric fluctuations in order to accomplish this goal. We contrasted our fuzzy type-2 algorithm’s performance with that of the fuzzy type-1 and Perturbation & Observation (P&O) suggested in the literature. Practical value. The simulation results demonstrate that the control performance is satisfactory when using the fuzzy logic technique. From these results, it can be said for the optimization of energy conversion in wind systems, the fuzzy type-2 technique may offer a workable option. Since it presents a great possibility to avoid problems either technical or economics linked to conventional strategies.

Performance optimization of photovoltaic system under real climatic conditions using a novel MPPT approach

ABSTRACT In order to mitigate the impact of temperature and irradiation variations on photovoltaic (PV) panels, it is crucial to employ the maximum power point tracking (MPPT) technique. This paper introduces a novel MPPT approach that provides a reference current, denoted as Iref, to extract the maximum power from the PV array. The difference between the input current Ipv, and Iref is then fed into a PID controller, which adjusts the duty cycle accordingly to control the Quadratic Boost Converter (QBC). The proposed MPPT approach is based on the analysis of measurement data obtained from PV panels. From this, a linear mathematical function between Iref and solar irradiation is achieved. To assess the performance of this approach, a comparative study is being carried out not only with conventional MPPT techniques such as P&O and INC-COND but also with modern techniques such as ANN, PSO, FLC, and improved P&O (IMP-P&O). The simulation results clearly demonstrate enhanced tracking performance under various test conditions, including the Step & Ropp test, as well as real profiles for both sunny and cloudy days. Certainly, the novel approach shows its ability to outperform both conventional and modern methods with a faster convergence speed (less than 2 milliseconds), the lowest steady-state oscillations (less than 0.25 Watts), and the highest tracking efficiency (greater than 99.79%).

Improved power quality based PVDG system using different optimised MPPT controllers

The characteristic curve of photovoltaic (PV) modules is nonlinear due to environmental influences. The maximum quantity of energy can only be taken from the nonlinear angle of the solar system at one exact moment. The primary function of an MPPT (maximum power point tracking) controller in a PV system is to extract maximum power point (MPP) from solar panels. In this paper, an enhanced MPPT approach using different optimization techniques is suggested for PV systems. The Fuzzy logic controller (FLC), Whale Optimization Algorithm(WOA), and Modified Grey Wolf Optimization algorithm (MGWO) are presented for the MPPT controller. Among all methods, the MGWO technique is one of the best at determining the MPP in solar systems because of its quick response and low fluctuations. Accurate training data is one of the biggest challenges in developing an effective MGWO. The irradiance and temperature are taken into consideration as input variables, and the optimal voltages are optimized as an output variable using the MGWO algorithm. Matlab/Simulink simulations are run to verify the suggested model’s suggested tracking effectiveness. To ensure accurate results, simulations are run under various climatic conditions. The simulation result shows that the proposed method operates correctly in a variety of climatic conditions efficiently.