Maximum Power Tracking Research Articles

Global maximum power tracking of PV system under partial shading

The increased usage of electrical energy in the recent times leads to a greater demand. It invites large development in the production of electrical energy from renewable energy sources. It involves more evolving technologies. Out of all energy extraction from solar would be abundant. Photovoltaic (PV) are one such components helps in deriving large amounts of energy, this has become more easiest method due to its economic liabilities and the world has aimed its interest in developing the PV technology, which gives clean energy. This paper objective is to implement various Maximum Power Point Tracking (MPPT) algorithms, mainly Cuckoo Search Algorithm, fuzzy logic control (FLC) and conventional perturb and observe (P&O), incremental conductance (INC) on solar PV systems. These controlled MPPT algorithms helps in driving DC-DC boost converter, which helps to obtain maximum output from the PV Panels/cells/modules/Arrays. The obtained results are compared with each other under several operating conditions. The operating conditions include change in irradiance, change in temperature dynamically, and partial shading on PV panels. The implemented MPPT algorithms require only the PV array voltage and current to control DC-DC converter, which makes them economically feasible and attractive. From the results, it can be observed that Cuckoo search algorithm gives better results under partial shading situations.

Modelling and Installation of Grid Connected Rooftop Solar PV System

The 100 kW Grid Connected Roof-Top (GCRT) Solar PV System erected at the SSGMCE, Shegaon Campus is modelled and simulated in this research. One of the renewable energies that is widely accessible is solar energy. In order to get the most out of solar energy, it is vital to use it properly. To meet the rising need for energy, more renewable sources must be used. A PV array's conversion efficiency decreases as a result of environmental parameter changes. Therefore, maximum power tracking, or MPPT, is employed. In order to boost energy production, MPPT detects peak power. Developing a PV array model, synchronising it with the grid, and implementing it in MATLAB Simulink are all covered in this work. This system comprises of a solar PV array, an IGBT inverter, and a grid connection. The power plant's performance is also described.

Metaheuristic Algorithm Based Maximum Power Point Tracking Technique Combined With One Cycle Control For Solar Photovoltaic Water Pumping Systems

Solar photovoltaic technology has become eminent in the world because of its clean and abundant nature and can be effectively used for water pumping applications. A maximum power point tracking method is indispensable to achieve the best benefit from photovoltaic systems. Conventional maximum power tracking methods become successful only under uniform irradiance conditions and fail to track the maximum power under partial shading conditions (PSC). Hence, nature-inspired metaheuristic algorithms were proposed to track the optimum power under varying environmental conditions. This study proposes a method for improving the performance of the nature-inspired maximum power point tracking algorithms by using a nonlinear control technique called one cycle control. Based on the duty cycle obtained from the tracking algorithm the one cycle control technique generates pulses for the DC–DC converter, which is connected to a brushless DC motor pump system through a voltage source inverter. The performance of the proposed system under various PSCs is validated using the Cuckoo search, particle swarm optimization, and grey wolf algorithms. Simulation results for various shading patterns prove the supremacy of the system with respect to convergence speed, tracking efficiency, robustness, steady-state oscillations at maximum power point, and initial exploration oscillations in comparison with systems without one cycle control. In addition, the introduction of a KY converter as a DC–DC converter reduces the output voltage ripple in the system. Thus, the proposed system with one cycle control overcomes the disadvantages of the existing methods and can be effectively utilized for water pumping applications.

Enhanced energy output from a PV system under partial shaded conditions through grey wolf optimizer

Due to its several powerful characteristics, the Grey Wolf Optimization (GWO) algorithm has recently become widely used in numerous optimization problems. In photovoltaic (PV) systems, power-voltage (P–V) curves present multiple maxima in case of partial shadow or modules mismatch which can be viewed as an optimization problem. In this paper, a GWO-based maximum power tracking (MPPT) algorithm for PV systems operating under partial shading conditions (PSCs) is proposed. With a direct control method, the proposed GWO-based MPPT allows successfully catching the global maximum power point (GMPP) of the PV array in case of uniform and non-uniform solar irradiation. To evaluate the effectiveness of the proposed MPPT scheme, a co-simulation combining Matlab/Simulink™ and Cadence/Pspice™ software environments is carried out and tested under various static and dynamic shading conditions. Furthermore, the tracking performance is compared with that of the particle swarm optimization (PSO) and classical perturb and observe (P&O) based MPPT methods under transient PSCs and also under inhomogeneous real conditions of Algerian sky. The simulation results have demonstrated the satisfactory performances of the proposed approach in terms of tracking efficiency and tracking speed over the other MPPT methods.

Improvement and Analysis of Phase-Phase Failure of Double Feed Induction Generator Using Maximum Power Monitoring Method

This study presents an analysis and control method for the doubly fed induction generator (DFIG) based wind energy production under the conditions of sudden grid voltage dips. The control aims are as follows: limiting the rotor currents, suppressing the fluctuations in the torque and suppressing the dc-link voltage fluctuation via the converter controls and ensuring maximum power point tracking. Rotor side converter (RSC) and grid side converter (GSC) structures have been given in the article and their limitations have been specified. Maximum power tracking control block has also been included in the system and its structure has been given. In this study; while RSC is controlled in a way that it will suppress the fluctuations in the torque and rotor currents, GSC is controlled in a way that it will suppress the fluctuations in dc-link voltage while taking the rotor power impact into consideration. Crowbar unit has been used in the rotor side to minimize these impacts. This unit is activated at the moment when a grid dip occurs and deactivates the RSC side. In this way, RSC side will not be affected by the high current fluctuations. The detailed results of the study were carried out in the MATLAB/Simulink environment,and the effectiveness of the proposed method was supported by the graphic results.

Enhance Photothermal Stability of Hybrid Perovskite Materials by Inhibiting Intrinsic Ion Migration

The degradation of the perovskite solar cells (PSCs) is closely related to phase decomposition, phase separation, and structural collapse, which mainly originates from the intrinsic ion migration under light soaking and thermal stress. Herein, the composition of perovskite materials is regulated based on its ion migration characteristics. By systematically studying the effect of A‐site cations on the ion migration and photothermal stability of organic–inorganic hybrid perovskite materials, it is found that the codoping of appropriate methylammonium (MA) and Cs cation in the formamidinium (FA)‐rich perovskite is favorable for the highly efficient and stable PSCs. The optimized triple‐cation FA0.90Cs0.04MA0.06PbI3 exhibits significantly improved photothermal stability in comparison with the double‐cation and Br‐containing perovskite materials. Moreover, the optimized triple‐cation device demonstrates excellent operation stability at maximum power tracking, and maintains 87% of initial efficiency after 1800 h.

Online Control of Smart Inverter for Photovoltaic Power Generation Systems in a Smart Grid

The main purpose of this study is to engage in research on a grid-connected photovoltaic (PV) power generation system smart inverter. The research content includes a smart maximum power point tracking (MPPT) controller and an inverter with power regulation. First, use the PSIM software package to establish the simulation environment of the grid-connected photovoltaic power generation system and use the Sanyo HIP-186BA19 photovoltaic module to form a 744 W system for simulation. In order to enable the photovoltaic module array (PVMA) to output the maximum power under different solar insolation and ambient temperature, the architecture is based on the extension theory-based smart MPPT method to improve the dynamic response and steady-state performance of photovoltaic power generation systems compared to perturb and observed (P&O) MPPT. When the sunshine is 1,000 W/m2, the photovoltaic power generation system adopts the extension theory-based maximum power tracking method. The time required to track the maximum power point is only 0.32 s, and the steady-state ripple is only 4.127 W. However, using the traditional P&O method requires 0.741 s to track the maximum power point, and the steady-state ripple reaches 18.131 W. Thus, the dynamic response speed of the maximum power tracking method proposed is 50% faster than that of the P&O method. The steady-state performance is also better compared to the P&O method. At the same time, a simple proportional-integral (PI) controller is used to regulate the DC-link voltage, output voltage, and current of the inverter to make the voltage of the grid-connected point stable at an effective value of 220 V. Then, the voltage-power control technology is added to the photovoltaic grid-connected inverter, and a simple proportional-integral controller is used to regulate the output of the smart inverter reactive power to improve the power quality of grid voltage. Finally, simulation and experimental results are used to verify the effectiveness of the regulation performance of the developed smart inverter.

Research and Application of MPPT Control Strategy Based on Improved Slime Mold Algorithm in Shaded Conditions

A PV maximum power tracking strategy for shaded conditions, based on an improved slime mold algorithm, is proposed in this research. To verify the superiority of the proposed algorithm, four bionomics algorithms—particle swarm optimization (PSO), tuna swarm optimization (TSO), squirrel search algorithm (SSA), and black widow spider algorithm (BWO)—were compared. The output parameter of the five control algorithms was summarized and analyzed. The adaptability of the algorithms was proven by setting different shading conditions. The simulation results demonstrated that the proposed algorithm possessed short response time, good tracking effect and fewer fluctuations. Eventually, the different algorithms were verified in the HIL + RCP physical platform. The experimental outcomes showed that the improved slime mold algorithm possessed the best tracking effect, with fewer power fluctuations.

Research on Maximum Power Tracking of Grey Wolf Optimization Algorithm Introducing Levy Flight

In the case of partial shadow, the power-voltage characteristic curve of photovoltaic array output will show multi-peak phenomenon, the traditional maximum power point tracking method is easy to fall into the local maximum power point, resulting in the loss of output power. Aiming at the common problems of traditional swarm intelligence optimization algorithms, such as slow convergence speed, large oscillation amplitude, and easy to fall into local optimum, a control method of gray wolf optimization algorithm with Levy flight was proposed. The algorithm uses GWO’s excellent ability of fast convergence speed and high solution accuracy to quickly converge to the vicinity of the maximum power point, and then uses CSA to achieve a stable state near the maximum power point. The power fluctuation is smaller. The simulation statistics show that compared with the traditional The improved gray wolf optimization algorithm has higher solution accuracy, shorter tracking time, and improves the output efficiency of the photovoltaic array.

Hybrid algorithm for MPPT tracking using a single current sensor for partially shaded PV systems

The power voltage characteristics of a photovoltaic array under partially shaded conditions is highly non linear and it exhibits multiple local peaks and a global peak. The maximum power is harvested from the photovoltaic panel only when the global peak is tracked. Many control algorithms were proposed in the past decades to track maximum power especially during partially shaded conditions. However, the control algorithms are still facing problems like slow convergence, more oscillations during tracking, and complexity in implementation. etc. In this work, a single sensor hybrid MPPT algorithm for battery charging application is proposed. The hybrid algorithm is a combination of salp swarm algorithm and conventional perturb and observe technique and it uses only battery charging current for tracking the maximum power. The performance of the algorithm has been tested under uniform and partially shaded conditions using MATLAB simulation and hardware prototype. The obtained results are compared against single sensor based grey wolf algorithm and sine cosine algorithm. The results show fast and accurate tracking of maximum power under uniform and dynamic shaded conditions.

Hybrid Maximum Power Tracking Technique for Partially Shaded PV Arrays

Photovoltaic panels are the most important green energy source because of their inexhaustible moreover its clean. It is important to connect the PV panels to the maximum power point tracking (MPPT) controller to optimize its output power. The PV panels output power efficiency is related to the rapidly variation of the incident irradiance moreover the partial shading pattern. The variation of the incident irradiance and the partial shading pattern makes the tracking of the global maximum peak (GMP) through the local ones too difficult, which extremely decrease the efficiency of the PV panels. The problem is the GMP value varies as the sun irradiance varies so; the detection of the GMP needs an efficient and fast algorithm which cannot be done by the traditional MPPT. In order to solve the problem, proposed system a hybrid new algorithm can combine a traditional MPPT algorithm, such as perturb and observe, or incremental conductance, with the ANN (artificial neural network) This new algorithm can combine a traditional MPPT algorithm, The proposed hybrid MPPT algorithm is based on the ANN and used to predict the global MPP region by estimating its voltage boundaries. Consequently, the conventional MPPT algorithm searches for the MPP in the predicted region. The proposed technique is modeled and simulated using MATLAB/Simulink.

Design and Analysis of Optimal Current Vector for HTS-Based Multi-Input Wireless Power Transfer Systems

This paper presents a newly-designed optimal current algorithm for high-temperature superconductor (HTS)-based multi-input wireless power transfer (WPT) systems. In this way, both high controllability and lower AC losses can be achieved in the proposed systems, and they are especially superior for long-range and long-time operations. Simplified AC loss modeling for HTS windings is developed for the designed transmitter coils. The accordant optimal current vector is derived and analyzed in order to achieve the highest output power and the lowest primary AC losses. With the proper current control of multiple transmitters and the use of a designed HTS coupler, the system controllability can be greatly improved compared with conventional WPT systems. Based on the information on the impedance characteristics on the primary side, the magnetic field generated by different transmitters can be maximized at the target position. Thus, the maximum output power tracking can be realized with a relatively long transmission distance and a low coupling coefficient. Both active and passive solutions are designed and presented to deal with the cross-coupling issue in multi-input WPT systems. For numerical validation, a practical prototype of the HTS couplers is fabricated. An experimental platform is established with a liquid nitrogen cooling system. The test results further validate the feasibility and the high controllability of the proposed system.

An Integrated Power Control Module for Photovoltaic Sources in DC Microgrid System

Photovoltaic (PV) arrays are highly compatible with DC microgrids as they are DC sources. However, the uncontrolled penetration of PV power in a DC grid will lead to overvoltage problems. This paper proposes an Integrated Power Control (IPC) Module for multiple PV sources in a DC grid which allows PV sources to deliver maximum power in MPPT mode and also works as a voltage controller to maintain DC bus voltage in droop mode. Thus, the IPC module integrates MPPT and DC bus voltage regulation in a single controller, reducing the complexity of the system and avoiding the need for the controller to switch between MPPT and droop mode as in conventional methods. Further, during unequal shading conditions between the multiple PV sources, the proposed IPC module effectively shares the power and operating the PV sources to deliver the available power to the DC bus. Thereby, the total efficiency of the system is increased. The IPC module is implemented in simulation for multiple PV sources on a DC grid, and maximum power tracking and power-flow management are verified. The prototype hardware setup is developed, and the results are presented to demonstrate the simple and cost benefited IPC module.

MPPT FOR HYBRID WIND, SOLAR AND THERMOELECTRIC POWER GENERATION SYSTEMS FOR OFF-GRID APPLICATIONS

Renewable energy resources for electrical generation are normally free but are not continuously available due to their sporadic availability. Thus, hybrid connections of various kinds of energy are made to increase utilization of renew-able energy. This paper involves a design of a hybrid renewable energy system employing maximum power point tracking (MPPT) techniques. The hybrid system consists of solar PV panels, a small-scale wind turbine, and a thermoelec-tric generator (TEG) module. Four MPPT techniques are examined in this research. They are the incremental conductance (IC) algorithm, fuzzy logic controllers (FLC) using 25 and 35 rules, and an interval type 2 fuzzy logic con-troller (IT2FLC). Each MPPT technique is tested in the system to determine which has the best maximum power tracking, stable operation, and efficiency. All of the studied energy resources are connected to a DC linked bus. The voltage of this bus is supplied to a three-phase inverter. Inverter output voltage is regulated for balanced and unba¬lanced loads. The generating capacity of the designed hybrid system is 5 kW and the system is simulated using MATLAB Simulink R2017a.

A Reactive Power Compensated Control Scheme for Solar-Assisted EV Fast-Charging Applications

The main objective of this work is to develop an efficient reactive power compensated control technique for a fast-charging scheme for electric vehicle(s) (i.e., level-3 charging). The developed charging technique has the four-quadrant power flow operation with the simultaneous assurance of the compensated reactive power control. The developed charging infrastructure scheme involves a solar panel and 3-phase grid to charge the E-mobility. A DC-DC boost converter is used to achieve maximum power tracking (MPPT) of a solar PV array, and a 3-phase grid-tied bidirectional voltage source converter (VSC) is utilized to provide the bulk of power to charge the EV. The 3-phase VSC has multiple functionalities including grid side power quality (PQ) improvement with reactive power compensation, seamless flow of power from the grid to EV, and solar to grid or battery to grid (V2G) operation. This scheme also facilitates tariffs earned by discharging solar energy to the grid with additional benefits of reactive power compensation. An arrangement is also made to tackle grid failure conditions during battery discharging mode by connecting a load for ancillary purposes. The effectiveness of this charging scheme is first examined in MATLAB/Simulink environment and then validated on developed hardware.

Fuzzy model based multivariable predictive control design for rapid and efficient speed-sensorless maximum power extraction of renewable wind generators

Introduction. A wind energy conversion system needs a maximum power point tracking algorithm. In the literature, several works have interested in the search for a maximum power point wind energy conversion system. Generally, their goals are to optimize the mechanical rotation or the generator torque and the direct current or the duty cycle switchers. The power output of a wind energy conversion system depends on the accuracy of the maximum power tracking controller, as wind speed changes constantly throughout the day. Maximum power point tracking systems that do not require mechanical sensors to measure the wind speed offer several advantages over systems using mechanical sensors. The novelty. The proposed work introduces an intelligent maximum power point tracking technique based on a fuzzy model and multivariable predictive controller to extract the maximum energy for a small-scale wind energy conversion system coupled to the electrical network. The suggested algorithm does not need the measurement of the wind velocity or the knowledge of turbine parameters. Purpose. Building an intelligent maximum power point tracking algorithm that does not use mechanical sensors to measure the wind speed and extracts the maximum possible power from the wind generator, and is simple and easy to implement. Methods. In this control approach, a fuzzy system is mainly utilized to generate the reference DC-current corresponding to the maximum power point based on the changes in the DC-power and the rectified DC-voltage. In contrast, the fuzzy model-based multivariable predictive regulator follows the resultant reference current with minimum steady-state error. The significant issues of the suggested maximum power point tracking method, such as the detailed design process and implementation of the two controllers, have been thoroughly investigated and presented. The considered maximum power point tracking approach has been applied to a wind system driving a 5 kW permanent magnet synchronous generator in variable speed mode through the simulation tests. Practical value. A practical implementation has been executed on a 5 kW test bench consisting of a dSPACEds1104 controller board, permanent magnet synchronous generator, and DC-motor drives to confirm the simulation results. Comparative experimental results under varying wind speed have confirmed the achievable significant performance enhancements on the maximum wind energy generation and overall system response by using the suggested control method compared with a traditional proportional integral maximum power point tracking controller.

Robust H2-Optimal TS Fuzzy Controller Design for a Wind Energy Conversion System

In this paper, robust optimal control of an uncertain wind energy conversion system (WECS), described by a Takagi–Sugeno fuzzy model, is proposed to guarantee the maximum power trajectory tracking (MPTT). The design of the fuzzy optimal control is based on a quadratic criterion related to the maximum power tracking error. The proposed fuzzy controller allows to regulate the rectified direct current (DC) voltage of the variable-speed wind turbine by adjusting the duty cycle of a boost converter. Linear matrix inequality (LMI) conditions, ensuring the optimal H2 tracking error, are proposed for the controller gain design. Simulation as well as experimental tests are performed for efficiency evaluation of the established MPPT fuzzy control scheme under variable wind speed conditions.

Improving the Maximum Power Extraction from Wind Turbines Using a Second-Generation CRONE Controller

Developing precise and robust algorithms that can help in obtaining maximum power yield in a variable speed wind turbine is an important area of research in wind engineering. The present manuscript proposes a technique that utilizes a second-generation CRONE controller for the maximum power tracking technique (MPPT) to maximize power generation in a wind energy conversion system (WECS) based on a double-fed induction generator (DFIG). The authors propose this novel method because the classical controllers cannot provide adequate performance in terms of extracting the maximum energy from variable speed wind turbines when applying a real wind profile and they cannot guarantee the high stability of the WECS. Moreover, this novel controller sufficiently handles problems related to the control effort level. The performance of the second-generation CRONE method was mathematically modeled using MATLAB/Simulink and compared with four other types of MPPT control techniques, which include a proportional-integral linear controller (PI), nonlinear sliding mode controller (SMC), backstepping controller (BS), and fuzzy logic controller (FLC). Two different wind profiles, a step wind profile and a real wind profile, were considered for the comparative study. The response time, dynamic error percentage, and static error percentage were the quantitative parameters compared, and the qualitative parameters included set-point tracking and precision. This test demonstrated the superiority of the second-generation CRONE controller in terms of all of the compared parameters.

Nonlinear Control Method of Photovoltaic Power Generation LVRT Based on Adaptive Maximum Power Tracking

With the continuous increase in the scale of photovoltaic grid connection, the impact of the safe and stable operation of photovoltaic power generation systems on the grid cannot be ignored. When a short-term failure of the power grid occurs, the large-scale disconnection of the unit will seriously affect the stability of the grid voltage and frequency. Therefore, this paper proposes a non-linear control method for photovoltaic power generation low voltage ride through (LVRT) based on adaptive maximum power tracking. This method adaptively adjusts the power tracking trajectory through the feedforward control of the voltage drop amplitude. Cooperating with the nonlinear control of the grid-connected inverter, this method can quickly and effectively control the power output of photovoltaic cells on the basis of providing appropriate reactive power support, so as to realize the rapid response of grid system. Through the simulation in PSCAD, it is verified that the control method described in this article can well realize the LVRT in different amplitudes of the photovoltaic system, especially when zero voltage drops occur.

HOSMD and neural network based adaptive super-twisting sliding mode control for permanent magnet synchronous generators

To solve the maximum power tracking (MPT) control problem of the direct-driven wind power system, a super-twisting integral sliding mode controller with adaptive parameter estimation is designed. A high-order sliding mode differentiator is introduced as the virtual control variate filter, which solves the difficulty of obtaining the derivative of the control variate and the controller saturation in the nonlinear system with disturbances. Since the speed loop of permanent magnet synchronous generator (PMSG) is susceptible to disturbances, a radial basis function neural network (RBFNN) approximator and its adaptive algorithm are proposed to observe the unmodeled part of the system and external disturbances. In addition, the super-twisting algorithm is introduced to improve the robust performance. An improved adaptive parameter estimation algorithm is used to obtain real-time estimated values in the circumstances of uncertain stator parameters and parameters perturbation during operation, which enhances control accuracy and reduces undesirable chatting. The errors of RBFNN approximation and parameter estimation are taken into Lyapunov functions to guarantee the stability of the whole system. The effect of the proposed scheme is verified as compared to the adaptive backstepping terminal and new reaching law sliding mode controllers.