A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids

As the solar PV system (SPVS) suffered from an unavoidable complication that it has nonlinearity in I–V curves, the optimum maximum power point (MPP) measurement is difficult under fluctuating climatic conditions. For maximizing SPVS output power, MPP tracking (MPPT) controllers are used. In this paper, a new adaptive fuzzy logic controller (AFLC) based MPPT technique is proposed. In this proposed AFLC, the membership functions (MFs) are optimized using the Grey Wolf Optimization (GWO) technique to generate the optimal duty cycle for MPPT. Four shading patterns are used to experiment with the performance of the proposed AFLC. The proposed approach tracks the global MPP for all shading conditions and also enhances the tracking speed and tracking efficiency with reduced oscillations. The effectiveness and robustness of proposed AFLC based tracker results over P&O and FLC are validated using Matlab/Simulink environment. The proposed AFLC overcome the drawbacks of the classical P&O, and FLC approaches.     

Optimization of fuzzy-based MPPT controller via metaheuristic techniques for stand-alone PV systems

Due to the alteration of power-voltage characteristics of solar module output under multiple environmental conditions such as solar irradiation and ambient temperature, these systems hardly function at maximum power point (MPP). However, maximum power point tracking (MPPT) plays a significant role in their efficiency. On the other hand, solar module characteristics are extremely nonlinear and their slope on either side of MPP is asymmetric. Thus using a nonlinear control method which has the potential of adapting the operating point of the system to MPP seems useful. This has motivated authors to present MPPT method which maximizes PV's output power by tracking MPP continuously. In the present study, a fuzzy logic controller (FLC) is presented for MPPT in photovoltaic systems. Four optimization algorithms are presented in this paper for optimizing fuzzy membership functions (MFs) and generating proper duty cycle for MPPT. The presented algorithms include: Teaching Learning Based Optimization (TLBO), Firefly Algorithm (FFA), Biogeography based optimization (BBO), and Particle Swarm Optimization (PSO), which are all described and simulated. Finally, to validate performance of the proposed optimized FLC, it is compared with other algorithms such as symmetrical fuzzy logic controller (SFLC) and conventional Perturbation and Observation (P&O). According to the simulation results, P&O algorithm shows significant oscillations, energy loss, and in some cases, it cannot obtain MPP. Simulation results also indicate that TLBO and FFA based asymmetric fuzzy MFs not only increase MPPT convergence speed but also enhance tracking accuracy in comparison with symmetric fuzzy MFs and asymmetric fuzzy MFs based on BBO and PSO.     

Adaptive maximum power point tracking based on Kalman filter for hydrogen fuel cell in hybrid unmanned aerial vehicle applications

In this paper, an adaptive real-time estimation method based on Kalman filter is proposed for tracking the maximum power point (MPP) of a hydrogen fuel cell (FC) in hybrid unmanned aerial vehicle (UAV) applications. To achieve the adaptive MPP tracking (MPPT), a mathematical model for the hydrogen FC is established. Then, the recursive least square method is employed to identify the initial values of model parameters. On this basis, the MPP of the hydrogen FC under steady conditions can be derived. Furthermore, the state and observation equations based on Kalman filter are introduced to adaptively estimate the model parameters in real-time. Moreover, the real-time model parameters would be used to optimize the MPP in accordance with the operating conditions such that the adaptive MPPT can be achieved. Finally, various simulations and experiments are conducted to verify the effectiveness and accuracy of the adaptive MPPT for the hydrogen FC in hybrid UAV applications. Results show that the adaptive MPPT can not only track the MPP accurately in real-time, but also reduce the oscillation of the hydrogen FC. Compared with the MPPT methods based on perturb and observe (P&O) and particle swarm optimization (PSO), the maximum power tracking error of the adaptive MPPT can be improved by 2.83% and 1.10%, respectively.     

Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system

This paper presents an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and irradiance conditions. First, for the purpose of comparison and because of its proven and good performances, the perturbation and observation (P&O) technique is briefly introduced. A fuzzy logic controller based MPPT (FLC) is then proposed which has shown better performances compared to the P&O MPPT based approach. The proposed FLC has been also improved using genetic algorithms (GA) for optimisation. Different development stages are presented and the optimized fuzzy logic MPPT controller (OFLC) is then simulated and evaluated, which has shown better performances.     

Adaptive maximum power point tracking control of fuel cell power plants

A fuel cell's output power depends nonlinearly on the applied current or voltage, and there exists a unique maximum power point (MPP). This paper reports a first attempt to trace MPPs by an extremum seeking controller. The locus of MPPs varies nonlinearly with the unpredictable variations in the fuel cell's operation conditions. Thus, a maximum power point tracking (MPPT) controller is needed to continuously deliver the highest possible power to the load when variations in operation conditions occur. A two-loop cascade controller with an intermediate converter is designed to operate fuel cell power plants at their MPPs. The outer loop uses an adaptive extremum seeking algorithm to estimate the real-time MPP, and then gives the estimated value to the inner loop as the set-point, at which the inner loop forces the fuel cell to operate. The proposed MPPT control system provides a simple and robust control law that can keep the fuel cell working at MPPs in real time. Simulation shows that this control approach can yield satisfactory results in terms of robustness toward variations in fuel cell operation conditions.     

Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions

The one of main causes of reducing energy yield of photovoltaic systems is partially shaded conditions. Although the conventional maximum power point tracking (MPPT) control algorithms operate well under uniform insolation, they do not operate well in non-uniform insolation. The non-uniform conditions cause multiple local maximum power points on the power?voltage curve. The conventional MPPT methods cannot distinguish between the global and local peaks. Since the global maximum power point (MPP) may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognise the global operating point under partially shaded conditions. In this paper, a novel MPPT system is proposed for partially shaded PV array using artificial neural network (ANN) and fuzzy logic with polar information controller. The ANN with three layer feed-forward is trained once for several partially shaded conditions to determine the global MPP voltage. The fuzzy logic with polar information controller uses the global MPP voltage as a reference voltage to generate the required control signal for the power converter. Another objective of this study is to determine the estimated maximum power and energy generation of PV system through the same ANN structure. The effectiveness of the proposed method is demonstrated under the experimental real-time simulation technique based dSPACE real-time interface system for different interconnected PV arrays such as series-parallel, bridge link and total cross tied configurations.     

A novel photovoltaic maximum power point tracking technique based on grasshopper optimized fuzzy logic approach

The maximum power point tracking (MPPT) in the PV system has become complex due to the stochastic nature of the load, intermittency in solar irradiance and ambient temperature. To address this problem, a novel Grasshopper optimized fuzzy logic control (FLC) approach based MPPT technique is proposed in this paper. In this proposed MPPT, grasshopper optimization is used to tune the membership functions (MFs) of FLC to handle all uncertainties caused by variable irradiances and temperatures. The performance of the proposed grasshopper optimized FLC based MPPT is studied under rapidly changing irradiance and temperature. The proposed MPPT overcomes the limitations such as slow convergence speed, steady-state oscillations, lower tracking efficiency as encountered in conventional methods viz. perturb & observed (P&O) and FLC techniques. The feasibility of the proposed MPPT is validated through experimentation. The effectiveness of the proposed scheme is compared with P&O and also with FLC MPPT.     

A fast and low cost analog maximum power point tracking method for low power photovoltaic systems

Low power photovoltaic (PV) systems are commonly used in stand-alone applications. For these systems, a simple and cost-effective maximum power point tracking (MPPT) solution is essential. In this paper, a fast and low cost analog MPPT method for low power PV systems is proposed. By using two voltage approximation lines (VALs) to approximate the maximum power point (MPP) locus, a low-complexity analog MPPT circuit can be developed. Theoretical derivation and detailed design procedure will be provided in this paper. The proposed method boasts the advantages such as simple structure, low cost, fast tracking speed and high tracking efficiency. To validate the correctness of the proposed method, simulation and experimental results of an 87 W PV system will also be provided to demonstrate the effectiveness of the proposed technique.     

Implementation of photovoltaic water pumping system with MPPT controls

To increase the output efficiency of a photovoltaic (PV) system, it is important to apply an efficient maximum power point tracking (MPPT) technique. This paper describes the analysis, the design and the experimental implementation of the tracking methods for a stand-alone PV system, using two approaches. The first one is the constant voltage (CV) MPPT method based on the optimum voltage, which was deduced experimentally, and considered as a reference value to extract the optimum power. The second one is the increment conductance (Inc-Cond) MPPT method based on the calculation of the power derivative extracted by the installation. The output controller can adjust the duty ratio to the optimum value. This optimum duty ratio is the input of a DC/DC boost converter which feeds a set of Moto-pump via a DC/AC inverter. This paper presents the details of the two approaches implemented, based on the system performance characteristics. Contributions are made in several aspects of the system, including converter design, system simulation, controller programming, and experimental setup. The MPPT control algorithms implemented extract the maximum power point (MPP), with satisfactory performance and without steady-state oscillation. MATLAB/Simulink and dSpace DS1104 are used to conduct studies and implement algorithms. The two proposed methods have been validated by implementing the performance of the PV pumping systems installed on the roof of the research laboratory in INSAT Tunisia. Experimental results verify the feasibility and the improved functionality of the system.     

Optimization of a fuzzy logic controller for PV grid inverter control using S-function based PSO

This paper presents implementation of particle swarm optimization (PSO) algorithm as a C-Mex S-function. The algorithm is used to optimize a 9-rule fuzzy logic controller (FLC) for maximum power point tracking (MPPT) in a grid-connected photovoltaic (PV) inverter. The FLC generates DC bus voltage reference for MPPT. A digital PI current control scheme in rotating dq-reference frame is used to regulate the DC bus voltage and reactive power. The proposed technique simplifies optimal controller design and ensures fast simulation speeds due to seamless integration with the simulation platform. Validity of the proposed method was verified using co-simulation in PSIM and MATLAB/Simulink. Simulation results show that the optimized FLC gives a better performance compared to fixed-step MPPT.     

A novel maximum power fuzzy logic controller for photovoltaic solar energy systems

The maximum power tracking problem and efficient energy utilization of a stand-alone photovoltaic array (PVA) feeding voltage controlled linear and nonlinear loads is studied. A novel and simple on-line fuzzy logic-based dynamic search, detection and tracking controller is developed to ensure maximum power point (MPP) operation under excursions in solar insolation, ambient temperature and electric load variations. A computer simulation model of the PVA renewable utilization scheme including the effects of temperature and solar irradiation changes was developed and fully simulated. The load voltage is controlled by a DC chopper and kept constant at the required rated voltage. A permanent magnet DC motor (PMDC) driving a fan-type load was connected in parallel to an RL passive load. A speed control scheme is also used for the PMDC motor drive so that the drive can be operated at specified speeds. Different controllers have been employed in the unified PVA scheme to control three separate loads at MPP tracking condition namely voltage at load bus and speed of the PMDC motor. The main objective of the paper is to present a novel enhanced, cost-effective MPP detector (MPPD) and dynamic MPP tracking (MPPT) controller for a hybrid mix of electric loads.     

An improved maximum power point tracking method for photovoltaic systems

In most of the maximum power point tracking (MPPT) methods described currently in the literature, the optimal operation point of the photovoltaic (PV) systems is estimated by linear approximations. However these approximations can lead to less than optimal operating conditions and hence reduce considerably the performances of the PV system. This paper proposes a new approach to determine the maximum power point (MPP) based on measurements of the open-circuit voltage of the PV modules, and a nonlinear expression for the optimal operating voltage is developed based on this open-circuit voltage. The approach is thus a combination of the nonlinear and perturbation and observation (P&O) methods. The experimental results show that the approach improves clearly the tracking efficiency of the maximum power available at the output of the PV modules. The new method reduces the oscillations around the MPP, and increases the average efficiency of the MPPT obtained. The new MPPT method will deliver more power to any generic load or energy storage media.     

Fractional order extremum seeking approach for maximum power point tracking of photovoltaic panels

Due to the high interest in renewable energy and diversity of research regarding photovoltaic (PV) array, a great research effort is focusing nowadays on solar power generation and its performance improvement under various weather conditions. In this paper, an integrated framework was proposed, which achieved both maximum power point tracking (MPPT) and minimum ripple signals. The proposed control scheme was based on extremum-seeking (ES) combined with fractional order systems (FOS). This auto-tuning strategy was developed to maximize the PV panel output power through the regulation of the voltage input to the DC/DC converter in order to lead the PV system steady-state to a stable oscillation behavior around the maximum power point (MPP). It is shown that fractional order operators can improve the plant dynamics with respect to time response and disturbance rejection. The effectiveness of the proposed controller scheme is illustrated with simulations using measured solar radiation data.     

Maximum power point tracking of a proton exchange membrane fuel cell system using PSO-PID controller

Fuel cells output power depends on the operating conditions, including cell temperature, oxygen partial pressure, hydrogen partial pressure, and membrane water content. In each particular condition, there is only one unique operating point for a fuel cell system with the maximum output. Thus, a maximum power point tracking (MPPT) controller is needed to increase the efficiency of the fuel cell systems. In this paper an efficient method based on the particle swarm optimization (PSO) and PID controller (PSO-PID) is proposed for MPPT of the proton exchange membrane (PEM) fuel cells. The closed loop system includes the PEM fuel cell, boost converter, battery and PSO-PID controller. PSO-PID controller adjusts the operating point of the PEM fuel cell to the maximum power by tuning of the boost converter duty cycle. To demonstrate the performance of the proposed algorithm, simulation results are compared with perturb and observe (P&O) and sliding mode (SM) algorithms under different operating conditions. PSO algorithm with fast convergence, high accuracy and very low power fluctuations tracks the maximum power point of the fuel cell system.     

Evaluation of a proper controller performance for maximum-power point tracking of a stand-alone PV system

In this paper the implementation of a suggested stand-alone PV system, for maximum-power point tracking (MPPT), is carried out. Also, this paper presents a comparative study, through experimental work, between the conventional PI controller and the fuzzy logic controller (FLC) under different atmospheric conditions. The implemented system with both the PI controller and the FLC gives a good maximum-power operation of the PV array, but the tracking capability for different optimum operating points is better and faster for the case of using the FLC compared to the case of using the PI controller.     

Terminal sliding mode control for maximum power point tracking of photovoltaic power generation systems

This paper presents a novel terminal sliding mode control (TSMC) method for maximum power tracking of photovoltaic (PV) power systems. First, an incremental conductance method is used for maximum power point (MPP) searching. It provides good efficiency under rapidly changing atmospheric conditions, but the accuracy for finding the MPP is highly related to the MPP tracking control. Therefore, a TSMC-based controller is developed to regulate the system to the searched reference MPP. Different from traditional sliding mode control, the developed TSMC assures finite convergence time for the MPP tracking. Furthermore, a common singularity problem that exists in traditional TSMC is removed in this paper. Even if considering uncertainty in the PV power system, the TSMC guarantees high robustness. Finally, several simulations and experiments show the expected control performance.     

An on-line MPPT algorithm for rapidly changing illuminations of solar arrays

Maximum power point tracking (MPPT) is usually used for a solar power system. Many maximum power tracking techniques have been considered in the past. The microprocessors with appropriate MPPT algorithms are favored because of their flexibility and compatibility with different solar arrays. Although the efficiency of MPPT algorithms is usually high, it drops noticeably in case of rapidly changing illumination conditions. The authors have proposed an improved MPPT algorithm based on the fact that the maximum power point (MPP) of solar arrays can be tracked accurately. The principle of energy conservation is used to develop the large- and small-signal model and transfer function for the solar power system. The work was carried out by both simulation and experiment on a current converter, by the digital signal processor (DSP) control, in MPPT mode under different illuminations. The results show that the proposed MPPT algorithm has successfully tracked the MPP in rapidly changing illumination conditions.     

Implementation of modified incremental conductance and fuzzy logic MPPT techniques using MCUK converter under various environmental conditions

Incremental Conductance (IC) technique is a cheap, and easy algorithm to implement for Maximum Power Point Tracking (MPPT). However, the IC technique usually takes time and suffers some delay to approach the MPP if the voltage is not near to the MPP or when subjected to rapid change in irradiance. In this paper, IC technique was implemented and compared to the modified Incremental Conductance technique (MIC) under various environmental conditions such as standard test conditions (STC) and partial shading conditions. Also the MIC method was compared to a Fuzzy Logic Controller (FLC) MPPT technique in order to evaluate the best and the accurate controller for the MPPT for different weather conditions. Results show that using FLC and MIC techniques are efficient for MPPT and may increase the PV system stability.     

Maximum power extraction from photo-voltaic power generator with adaptive MPP tracker

Power generation with the help of Photovoltaic (PV) arrays is emphasized increasingly and regarded as an important resource of power energy in the coming years. As the power supplied by PV arrays depends upon the insolation, temperature and array voltage, it is necessary to control the operating point to extract the maximum power from the PV arrays. A number of methods for Maximum Power Point Tracking (MPPT) has been reported in the literature. This paper discusses an adaptive method as well as compares with the conventional fixed step size method, effectively improves the MPPT speed and accuracy simultaneously. An adaptive algorithm and two phase dc-dc Converter is exercised as a MPP tracker. Ripple reduction is possible at input and output side of the converter. Mathematical models of converter are developed using state space averaging technique. The tracking responses of the system operating at the solar array MPP are evaluated. A theoretical analysis of the new algorithm in connection with dc-dc converter is provided and its feasibility is also verified by simulation results.