Maximum Power Point Tracking Approach Research Articles

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.

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.

Long Short Term Memory Tracker‐Based Modified DC‐DC Converter for Power Quality Improvement in Grid‐PV Systems under Uniform and Partial Shading Environments

In previous era, coal and oil like non‐renewable resources were mostly used for power generation, thus causing a severe environmental problem as well as high economic. An intelligent maximum power point tracking (MPPT) system was proposed to overwhelm the above‐mentioned impacts, which tracks the maximum generation of photovoltaic (PV) under various atmospheric conditions. Moreover, a modified DC‐DC converter was introduced to low inrush current and switching oscillation. The proposed model working process was split into three steps: first, design the normal PV with a grid‐integrated power system. Second, create a dataset with the aid of a designed PV model, and finally, design an intelligent MPPT controller based on that dataset. The difference among the MPPT and PV voltage was fed to an optimal controller that generates an appropriate pulse signal of the modified DC‐DC converter. The proposed intelligent tracking system was analyzed under various circumstances like static, dynamic and combined shading conditions. The proposed model offers 99.5% efficiency and very low harmonics values like 0.15% current total harmonic distortion (THD) and 0.46% voltage THD. The outcome verifies that the intelligent MPPT approach performs well than that of the currently used controllers in solar power generation and conditioning systems.

A New Design and Embedded Implementation of a Low‐Cost Maximum Power Point Tracking Charge Controller for Stand‐Alone Photovoltaic Systems

This article presents a low‐cost maximum power point tracking (MPPT) charge controller designed to efficiently charge solar batteries using a simple and robust MPPT method, while providing protection against over‐currents and over‐voltages. The proposed MPPT approach utilizes a modified Incremental Conductance (INC) algorithm adapted to low‐cost embedded controllers, as it avoids all the mathematical division calculations typically used in the conventional INC algorithm. It also introduces a modified step size based only on photovoltaic (PV) power change to enhance tracking accuracy and convergence speed. Furthermore, to reduce the stress and switching losses of DC–DC converter, a feedback control with two proportional and integral (PI) compensators is also used to control both the PV voltage and to protect the battery from over‐currents using PV power limitation technique. Simulation results, carried out under MATLAB/Simulink, demonstrate the good performance of the proposed control scheme regarding any variations in atmospheric conditions, both for the MPPT control and for the battery charge control. Finally, a processor‐in‐the‐loop (PIL) test using a low‐cost Arduino MKR ZERO board is also performed to verify the hardware implementation of the proposed method. PIL results are in totally accordance with MATLAB/Simulink simulation results, which further proves the effectiveness of the proposed method.

Improving Photovoltaic System Performance with Artificial Neural Network Control

Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels in response to environmental changes. The performance of the ANN controller was compared with traditional MPPT approaches using simulation in Simulink/Matlab. The results of the simulation showed that the ANN controller performed better than the traditional MPPT techniques, highlighting the effectiveness of this method for dynamically changing solar panel performance. The ANN particularly demonstrates higher precision and adaptability when environmental conditions vary. The strategy consistently achieves and maintains the maximum power point, enhancing overall energy harvesting efficiency. The integration of an ANN controller marks a significant advance in solar energy control. The study highlights the superiority of the ANN controller through rigorous simulations, demonstrating increased accuracy and adaptability. This approach not only proves effective, but also has the potential to outperform other MPPT strategies in terms of stability and responsiveness.

Iot and AI-Based MPPT Techniques for Hybrid Solar and Fuel Cell

Rising electricity demands necessitate advancements in energy generation efficiency and reliability, prompting increased interest in hybrid systems integrating solar and fuel cell renewable sources. This study proposes the MPPTs (maximum power point tracking) approach utilizing Internet of Things (IoT) for actual-time data collections from diverse sensors in a hybrid system. Sensors measuring solar irradiance, temperature, fuel cell performance, and energy storage levels feed data into AI algorithms, dynamically optimizing operational settings for enhanced energy extraction. The AI algorithms adapt to environmental changes, component deterioration, and energy consumption fluctuations, resulting in a continuous efficiency improvement. The proposed technique demonstrates superior tracking accuracy, boosting energy conversion efficiency from 88% to 94%, with a reduced system response time from 4.5 to 2.25 min. Performance metrics validate the effectiveness, suggesting potential for advanced control strategies in future renewable energy systems. Further research avenues include scalability, robustness, and cost-effectiveness for broader real-world applications.

Real-time implementation of a novel MPPT control based on the improved PSO algorithm using an adaptive factor selection strategy for photovoltaic systems

Particle Swarm Optimization (PSO) is considered as one of Maximum Power Point Tracking (MPPT) controller algorithm developed for PhotoVoltaic system (PV) to guarantee a maximum power extraction under different climatic conditions of temperature and irradiation. Classical MPPT algorithms like Perturbe and Observe (P&O), PSO, Adaptive Neuro-Fuzzy Inference System (ANFIS) are an effective method for tracking the maximum power point (MPP) for the PV systems, but the problems with these approaches that they are less stable, high oscillation around steady state and slower convergence to the MPP. Based on recent research, the purpose of this paper is to introduces a novel MPPT controller based on a modified version of heterogeneous multi swarm PSO algorithm using an adaptive factor selection strategy (FMSPSO), to overcome the previous shortcomings and compared with conventional PSO, ANFIS and classical P&O controllers. Simulation and experimental results revealed that the new FMSPSO algorithm can overcome the previous shortcomings providing the superior performance to track the MPP efficiently with a shorter convergence time and small oscillations compared to other algorithms. The experimental confirmation of the FMSPSO algorithm has been carried out using NI-myRIO-1900 card and shows that with the proposed MPPT approach efficiency can reach a value greater than 99% even in climatic variation of irradiation and temperature.

Multiple voltage source converters based microgrid with solar photovoltaic array and battery storage

In this paper, a standalone photovoltaic (PV)-battery storage (BS) based microgrid (MG) is presented with a 415V-AC bus. The PV array is linked to the main voltage source converter (VSC1) DC-link to maximize power production in an efficient and cost-effective single-stage structure. This is achieved through an incremental conductance (INC) maximum power point (MPP) tracking approach. Moreover, a secondary converter (VSC2) is connected to BS for power flow management. MG performance is evaluated under various operating conditions. Steady-state and dynamic performances of MG are studied for variations such as changes in solar PV irradiance and load unbalances.

Analysis of Open Circuit Voltage MPPT Method with Analytical Analysis with Perturb and Observe (P&O) MPPT Method in PV Systems

This study conducts a comprehensive comparison between two prominent Maximum Power Point Tracking (MPPT) techniques employed in solar energy systems: the Perturb and Observe (P&O) method and the Analytical Solution Fractional Open Circuit Voltage (ASFOCV) method. To assess the effectiveness of these MPPT approaches, a simulation study was conducted using four SHARP NDQ295 model photovoltaic panels, configured as two panels in series and two in parallel. Both the P&O and ASFOCV MPPT methods were evaluated under various scenarios of radiation levels and temperature changes. The results unequivocally demonstrate the superior performance of the ASFOCV MPPT method over the P&O MPPT method. The ASFOCV method notably enhanced converter output power by up to 5% when compared to the P&O method, leading to more efficient energy production. Furthermore, the ASFOCV method exhibited rapid stabilization of output voltage during abrupt weather changes, outperforming the P&O method in this regard. This study underscores the potential of the ASFOCV MPPT method to enhance the efficiency of solar energy systems and its adaptability to fluctuating environmental conditions. Future research endeavors could focus on mitigating the ASFOCV method’s sensitivity to temperature variations and conducting real-world applications to further investigate its performance under practical circumstances.

Artificial Intelligent Maximum Power Point Controller based Hybrid Photovoltaic/Battery System

Photovoltaic (PV) cells have non-linear properties influenced by environmental factors, including irradiation and temperature. As a result, a method known as maximum power point tracking (MPPT) was implemented to boost the PV cells' efficiency and make the most of the energy they could provide. The traditional perturb and observe (P&O) approach for determining the maximum power point tracking (MPPT) has various drawbacks, including poor steady-state performance, increased oscillation around the MPP point, and delayed reaction. As a result, this work aims to present a hybrid fuzzy logic (FL) and P&O MPPT approach to improve the PV system's performance coupled to the lithium battery storage system. Matlab/Simulink is used to bring the suggested technique to life, after which its efficacy is evaluated in the context of rapid changes in the irradiance level. According to the findings of the simulations, the suggested strategy has the potential to enhance the steady-state performance of PV systems in terms of oscillation and time response. Finally, the proposed results are compared with that obtained by the conventional P&O technique, and the stress of PV power is limited to ∆P=1kW and the overshoot power is limited to 5%.

A New Dragonfly Optimized Fuzzy Logic-based MPPT Technique for Photovoltaic Systems

Photovoltaic (PV) power systems should be operated at the maximum power point (MPP) for best solar energy utilization, which can be achieved using maximum power point tracking (MPPT) techniques. Perturb & Observe (P&O) and Fuzzy logic MPPT approaches were two of the various strategies that were suggested as effective ways to achieve Maximum Power under Continuous Irradiation. When exposed to changes in environmental conditions, these approaches perform poorly dynamically and exhibit substantial steady-state oscillations around the MPP. To overcome this problem, this paper proposes the Dragonfly optimization-based fuzzy logic MPPT approach for maximum power extraction of photovoltaic (PV) systems. The approaches for implementing FL-based MPPTs that are currently available are not adaptable to the operating point, which varies widely in real-world PV systems with operational irradiance and ambient temperature. The proposed MPPT (DAFLC-MPPT) is straightforward, accurate, and offers quicker convergence to the optimal operating point. With consideration of various operating situations at slow and fast changes in solar radiation, the efficacy and viability verifications of the proposed AFL-MPPT approach are validated. The proposed strategy outperforms the standard P&O and fuzzy logic methods.

Machine learning based cascaded ANN MPPT controller for erratic PV shading circumstances

Power generation is challenged to meet energy demand during peak hours. As a result of limited non-renewable energy resources, power utilities heavily rely on fossil fuels. Therefore, scientists and researchers are looking for some distributed generators to provide additional power during peak hours. During such period, load demand is solved using solar power. As a consequence, grid-connected solar Photovoltaic (PV) systems are catching the attention owing to their ability to significantly reduce the use of fossil fuels. Under Partial Shading Condition (PSC), this paper utilizes Luo Converter along with Cascaded Artificial Neural Network (ANN), which is a Machine Learning Based Maximum Power Point Tracking (ML-MPPT) approach for tracking optimal power from PV system. The gained DC supply is converted into AC voltage using  VSI attached to the system. In addition, PI controller engaged controls the voltage at grid side and results in effective grid synchronization. Furthermore, MATLAB Simulink analysis is carried out and the outcomes reveal the effectiveness of proposed system with 98% efficiency under different PV circumstances.

Innovative maximum power point tracking technique for wind energy conversion system

In this work, an optimal control scheme based on the Maximum Power Point tracking for the Wind Energy Conversion System using Permanent Magnet Synchronous Generator (PMSG) is proposed and also modeled. The system studied in this paper consists of wind energy system powering a battery using a buck-boost converter as an interface. By modifying the buck-boost duty cycle, we vary the reflected voltage at PMSG, and accordingly its speed. It is controlled with Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) approach. The overall system is simulated with MATLAB/SIMULINK.