Fuzzy Logic Maximum Power Point Tracking Research Articles

Design and implementation of Type-3 intuitionistic fuzzy logic MPPT controller for PV solar system: Comparative study

The performance of a photovoltaic (PV) solar system is affected by partial shading conditions (PSC) and environmental conditions, such as solar irradiance and ambient temperature, which vary throughout the day. This results in variations in the maximum power point (MPP) on the solar PV output characteristic curve. Therefore, various classical MPP tracking (MPPT) techniques have been used to track the MPP and extract maximum power from PV systems. However, these techniques have drawbacks such as lower stability, increased oscillation around the steady state, and slower convergence to the MPP. To overcome this problem, the newly proposed interval Type-3 intuitionistic fuzzy logic (T3IFL) controller has been proposed. The T3IFL MPPT controller combines the uncertainty of Type-3 fuzzy logic (T3FL) controller with intuitionistic concepts. The T3IFL controller is more accurate and offers faster convergence to the MPP under changing climatic and steady-state conditions than classical techniques and T3FL controller. The T3IFL algorithm provides better performance with excellent MPP tracking by controlling the duty cycle of the DC-DC buck converter. Four cases studied were investigated: uniform radiation conditions, a step change in solar radiation with constant temperature, replacing the battery load with the ohmic load with constant radiation and temperature, and partial shading conditions. Experimental validation of the T3IFL was performed on a DC-DC buck converter using real-time hardware-in-the-loop (HIL). Finally, the simulation and experimental results with comparative studies verified the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with better response.

Implementation of fuzzy logic MPPT based on Arduino in small scale PV pumping system

The performance of photovoltaic (PV) affected directly by climatic changes, The controller maintain maximum potential energy conversation to operate the pimping system at nominal conditions, fuzzy logic intelligent controllers are successfully suitable and applicable in engineering and applied science. The aim of this paper is present an experimental approach in Implementation of fuzzy logic maximum power point tracking (MPPT) with boost converter based on Arduino Mega micro-controller to maximize energy production in different weather condition applied to small scale pumping system for water and chemical fluid analyses in isolated area. The system is supplied by 20 (W) solar photovoltaic (PV) panel. This paper present a real-time MATLAB/Simulink fuzzy logic method controlling and monitoring MPPT application using an low cost Arduino Mega micro-controller combined with (LV25, LP55) sensors controlling boost converter interconnected with solar panel and plastic pump.

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.

Improved Fuzzy Logic MPPT Controller of Stand-alone WECS-based PMSG under Stochastic Wind Environment

This paper discusses the modeling and control of a Standalone WECS-based PMSG, the aim is to achieve an optimal operation of the studied WECS under a typically stochastic wind environment. The Maximum Power Point Tracking (MPPT) is guaranteed through the tracking of an optimal generator speed using an Improved Fuzzy Logic Controller (IFLC) based on intelligent algorithms. The effectiveness and the benefits of the proposed approach are demonstrated by numerical simulation using Matlab/SIMULINK. The obtained results indeed confirm a good tracking performance of the proposed controller.

Analysis of interleaved fuzzy Logic MPPT approach for PV system

In this article, a detailed novel interleaved fuzzy logic control based Maximum Power Point Algorithm was designed. It was derived from the conventional hill climbing method, which is used to track solar Photo Voltaic (PV) systems over changing temperature and irradiance. The proposed system composed of the PV panel source, Maximum Power Point Tracking (MPPT) with designed interleaved Fuzzy Logic controller, DC to DC charge controller, and load. The designed fuzzy logic controller contains the two fuzzy subsets, one fuzzy concentrate the change in power with respect to change in voltage and another fuzzy concentrate the change in power with respect to change in current, compare with two fuzzy model uses to track the path more perfect compare to existing method as a novelty of the article. The proposed interleaved fuzzy MPPT optimally tune the parameters of the controller. The proposed system analyzed the few sub criteria, such as time response, stability and power efficiency. The performance of the presented controller has been validated with conventional controllers such as fractional open circuit approach, fractional short circuit approach and hill climbing approach. The proposed leads the highest tracking performance compared to existing. In addition, the proposed performed effectively in different environmental conditions.

Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage.

This paper highlights a robust optimization and power management algorithm that supervises the energy transfer flow to meet the photovoltaic (PV) electric vehicle demand, even when the traction system is in motion. The power stage of the studied system consists of a triple-junction PV generator as the main energy source, a lithium-ion battery as an auxiliary energy source, and an electric vehicle. The input–output signal adaptation is made by using a stage of energy conversion. A bidirectional DC-DC buck-boost connects the battery to the DC-link. Two unidirectional boost converters interface between the PV generator and the DC link. One is controlled with a maximum power point tracking (MPPT) algorithm to reach the maximum power points. The other is used to control the voltage across the DC-link. The converters are connected to the electric vehicle via a three-phase inverter via the same DC-link. By considering the nonlinear behavior of these elements, dynamic models are developed. A robust nonlinear MPPT algorithm has been developed owing to the nonlinear dynamics of the PV generator, metrological condition variations, and load changes. The high performance of the MPPT algorithm is effectively highlighted over a comparative study with two classical P & O and the fuzzy logic MPPT algorithms. A nonlinear control based on the Lyapunov function has been developed to simultaneously regulate the DC-link voltage and control battery charging and discharging operations. An energy management rule-based strategy is presented to effectively supervise the power flow. The conceived system, energy management, and control algorithms are implemented and verified in the Matlab/Simulink environment. Obtained results are presented and discussed under different operating conditions.

Development and real time implementation of intelligent holistic power control for stand alone solar photovoltaic generation system

This paper focusses on real-time implementation of an intelligent Holistic Power-Control system which is suitable for uniform, dynamic-irradiance and shaded-conditions for a stand-alone solar-PV system. The model consists of two intelligent Maximum Power Point Tracking(MPPT) control algorithms under different environmental conditions and a novel shading-detection mechanism. A fuzzy logic MPPT system with rule-base developed from real-time solar panel have been implemented. A partial shading detection mechanism based on power loss generated from solar PV array without any sensors is incorporated in the real-time platform. A fuzzy-logic Sweep-Inclusion algorithm is proposed to reach global maximum power point with fast tracking-time under partially-shaded conditions. To test the performance, real-time PV-system with data acquisition using LabVIEW is developed. The standalone solar-PV system is benefitted by the proposed-method by its less tracking-time and more efficiency, accurate partial shading detection through modified power loss scheme and Fuzzy Logic Sweep-Inclusion algorithm for identifying the global-MPPT point.

Modeling and Analysis of PV System with Fuzzy Logic MPPT Technique for a DC Microgrid under Variable Atmospheric Conditions

Due to the easiness of setup and great energy efficiency, direct current (DC) microgrids (MGs) have become more common. Solar photovoltaic (PV) and fuel cell (FC) systems drive the DC MG. Under varying irradiance and temperature, this work proposes a fuzzy logic controller (FLC) based maximum power point tracking (MPPT) approach deployed to PV panel and FC generated boost converter. PV panels must be operated at their maximum power point (MPP) to enhance efficiency and shorten the system’s payback period. There are different kinds of MPPT approaches for using PV panels at that moment. Still, the FLC-based MPPT approach was chosen in this study because it responds instantaneously to environmental changes and is unaffected by circuit parameter changes. Similarly, this research proposes a better design strategy for FLC systems. It will improve the system reliability and stability of the response of the system. An FLC evaluates PV and FC via DC–DC boost converters to obtain this enhanced response time and accuracy.

A new design of transformerless, non‐isolated, high step‐up DC‐DC converter with hybrid fuzzy logic MPPT controller

AbstractUnder Partial Shading Conditions (PSCs), the solar PV nonlinear characteristics consist of multiple Maximum Power Points (MPPs). As a result, it is very difficult to extract the peak power of the solar PV. In this article, an Improved Grey Wolf Optimization‐based Fuzzy Logic Controller (IGWO‐FLC) is proposed to track the MPP of the solar PV. The advantages of the proposed hybrid soft computing Maximum Power Point Tracking (MPPT) controller are high accuracy, very less oscillations across MPP, and high tracking speed. Also, a new transformerless, high step‐up, Non‐isolated Boost Converter (NIBC) is introduced in this work to improve the voltage profile of the solar PV system at different atmospheric conditions. The introduced converter circuit makes use of the network LC3D3 to improve the voltage conversion ratio, reduced voltage stress on switches, and universal input voltage. In addition, the network LC3D3 acts as a filter to reduce the output power ripples of the converter. The steady‐state performance of the proposed converter is analyzed at Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM) of operations. The MATLAB/Simulink window is used to validate the proposed PV fed NIBC system at uniform and different PSCs of the solar PV. Also, the NIBC performance is investigated by applying an M62252P8020 type programmable DC‐source.

MPPT Control of Photovoltaic Systems Using Statechart With Abstraction and Its Comparison With Fuzzy Logic

Maximum power point tracking(MPPT) control is an indispensable aspect of photovoltaic(PV) systems. Many MPPT techniques including a few based on soft-computing have been employed earlier. The soft-computing techniques include fuzzy-FSMs (Finite State Machines)which are integration of fuzzy logic (FL) into states or transitions of FSMs which are used for control and modeling of real-time systems. However, FSMs pose certain disadvantages as compared to its advanced variant called ‘statecharts’. In this work, statecharts with abstraction layers are proposed for MPPT control of PV system. An abstract-statechart MPPT(ASM) controller is developed and is verified with PV system using co-simulation. A C++ based FL MPPT program is also developed, which is independent of any predefined and simulation-only functions. A conceptual estimation of execution time of such a FL MPPT program is presented and compared with the execution times delivered by proposed ASM controller. It can be observed that the ASM controller gives accurate, fast tracking speeds, along with the advantage of abstraction.

Fuzzy logic MPPT control algorithm for a Proton Exchange Membrane Fuel Cells System

Fuel cells output power depends on the operating conditions, including cell temperature, oxygen pressure, hydrogen pressure, tempureter . 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 PEMFC systems. In this paper an efficient method fuzzy logic controller is proposed for MPPT of the proton exchange membrane (PEM) fuel cells, boost converter. FLC 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 sumulated in two cases, in normel condution and variation in temperature .the FLC algorithm with fast convergence, high accuracy and very low power fluctuations tracks the maximum power point of the fuel cell system

Modified Fuzzy Logic MPPT for PV System under Severe Climatic Profiles

Photovoltaic energy is considered highly favorable due to the environment's pleasant nature. After analyzing different maximum power point tracking (MPPT) algorithms, an effective control scheme is proposed to obtain stabilized maximum output power throughout the PV system. Therefore, this article presents an efficient control algorithm for the extraction of maximum power through a PV system under severe climatic drifts. The modified fuzzy logic controller sustains the maximum output power of the system by defining fuzzy rules to control the duty cycle appropriately. A DC-DC boost converter is also modeled to stabilize and maintain output power under variant climatic uncertainties. Furthermore, charging management control is also implemented on lead-acid battery bank to store PV energy for backup usage. It defines charging-discharging time and state of charge for keeping the battery bank healthier.

Fuzzy logic controller based maximum power point tracking and its optimal tuning in photovoltaic systems

Conventionally, the parameters of a fuzzy logic controller (FLC) are obtained by a trial and error method or by human experience. In this paper, the problem of designing a FLC for maximum power point tracking (MPPT) of a photovoltaic system (PV) that consists of a PV generator, a DC-DC boost converter and a lead-acid battery is studied. The normalization gains, the membership functions and the fuzzy rules are automatically adjusted using a particles swarm optimization algorithm (PSO) in order to maximize the criterion based on the integration of the PV module power under standard temperature condition (STC) (T=25?C and S=1000 W/m2). The robustness test of the optimized fuzzy logic MPPT controller (FLC-MPPT) is carried out under different scenarios. Simulation results of the system clearly show that the proposed optimized FLCMPPT controller outperforms in terms of maximum efficiency the FLC-MPPT controller not optimized, the FLC-MPPT controller with optimized normalization gains and the FLC-MPPT controller with optimized normalization gains and membership functions.

ANFIS-Based MPPT Controller of the Thermoelectric Energy Harvesting System for DC Micro-grid Applications

The power-current characteristics of a thermoelectric generator are nonlinear, which relies on the hot and cold side temperature difference. It is, therefore, vital that the thermoelectric modules (TEMs) are made to work at maximum power point (MPP). For continuous tracking of the MPP, a maximum power point tracking (MPPT) techniques may be used. In this research work, for thermoelectric energy harvesting systems (TEEHSs), an adaptive neuro-fuzzy inference system (ANFIS)-based MPPT controller is proposed to optimize the output power from the TEM array. The TEMs are connected in series–parallel configuration and that are placed near to the stator windings (SWs) of the wind generator (WG) for generating electrical energy from waste heat energy. The TEM array with ANFIS-based MPPT controller is compared to the fuzzy logic (FL)-based MPPT controller under a uniform wind velocity (UWV) and non-uniform wind velocity (NUWV) condition. From the obtained results, it is observed that the MPPT controller based on ANFIS tracks the maximum power extremely faster than the FL controller. The time taken by the ANFIS controller to reach the MPP is decreased by 0.15 s compared to the FL MPPT controller. The average maximum power obtained from ANFIS-based MPPT controller is 109 W and 119 W higher than the FL-based MPPT controller under UWV and NUWV conditions, respectively. In comparison, the tracking accuracy of the ANFIS-based MPPT controller is higher than the FL-based MPPT controller with an average relative error of 1.696% and 1.171% under both conditions.

Design and analysis of robust fuzzy logic maximum power point tracking based isolated photovoltaic energy system

SummaryPhotovoltaic (PV) energy is highly promising because of its renewable, green, and environment‐friendly nature. In this article, the design and analysis of an isolated PV system using a push‐pull converter with a fuzzy logic‐based maximum power point tracking (MPPT) algorithm is presented. Furthermore, DC‐DC converters, along with intelligent controllers fed with MPPT algorithms, are used to ensure the maximum extraction of incident energy. The proposed methodology utilizes fuzzy logic MPPT techniques based on an isolated push‐pull boost converter to optimize the power output of PV modules, as well as to achieve isolation and high DC gain for DC/AC inversion. This work also presents a single‐phase inverter with fuzzy logic close loop control analysis with LCL filter design. A Canadian solar panel of 250 W is assumed in this research work, which has an open circuit voltage 59.9 V, short circuit current 5.49 A at 25°C temperature, and 1000 W/m2 irradiance. The voltages are tracked, through the MPPT algorithm. These voltages represent a boost to 340 V DC through push‐pull boost converter and are inverted up to 220 V AC through fuzzy logic voltage source inverter. In addition, a unipolar switching technique is used to remove the total harmonic distortion under linear load. The proposed methodology is simulated in MATLAB/Simulink. The simulation results verify that the proposed methodology can efficiently track the MPPT. Finally, the hardware prototype of the proposed system has been experimentally validated.

Improved Efficiency of photovoltaic Module Based on Fuzzy Logic MPPT Technique

In photovoltaic (PV) systems, the maximum power point trackers are very important for increasing their efficiency. Maximum power can be achieved by using different methods such as P&O, INC so that the PV module can operate under different weather conditions and still produces maximum output power. In this paper, a smart method is presented for a maximum power point tracking (MPPT) based on a fuzzy logic control (FLC). The photovoltaic system consists of a solar panel type MXS 60W, DC-DC boost converter, FLC (MPP) tracker and resistive load are analyzed and simulated in MATLAB program. The output voltage of the solar panel is increased by the boost converter and it depends upon the duty cycle (D) of the MOSFET of the boost converter. Any change in the duty cycle is performed through sensing the output power of the solar panel. The controller tracks the maximum power of a solar panel by adjusting the duty cycle of the DC-DC converter switch. The simulation results show that the FLC controller can track the Maximum Power Point in a shorter time with less power oscillation around the MPP as compared to the most widely conventional method known as perturb and observe (P&O). Both techniques have been modeled and simulated in (MATLAB/Simulink).

A Differential Evolution-Based Optimized Fuzzy Logic MPPT Method for Enhancing the Maximum Power Extraction of Proton Exchange Membrane Fuel Cells

Recently, fuel cells (FCs) have found vast employment in several applications. However, unique maximum power point tracking (MPPT) exists for each set of operating condition for the efficient operation of FCs. Therefore, this paper presents a differential evolution optimization algorithm (DEOA)-based optimized fuzzy-logic (OFLC) MPPT method for enhancing the maximum power extraction of FCs. The various settings for the membership functions (MFs) of the input and output variables are optimized in the proposed method. Thence, more degree-of-freedom can be employed for accurate and fast tracking of the optimal power point of the proton exchange membrane FCs (PEMFCs). Whereas, existing MPPT methods in the literature for FC applications suffer from decreased degree-of-freedom for optimizing their performance, and lack of adaptivity, which obstructs their suitability for the wide operating range of FCs. The superiority and performance effectiveness of the proposed OFLC MPPT method have been validated and compared with the most prevalent techniques in the literature. Moreover, the robustness and sensitivity of the proposed OFLC MPPT method have been tested at various step changes in the water content of membrane and various temperature changes. Moreover, the proposed design of the suggested OFLC MPPT is general and it can be implemented on low-cost microcontrollers. The results verify the superior performance of the proposed OFLC MPPT method from the accurate and fast MPPT extraction, smooth output power with low ripple, and simplicity of the design point of views.

Particle Swarm Optimization based Fuzzy Logic MPPT Inverter Controller for Grid Connected Wind Turbine

The wind energy sources as an alternative to conventional sources to generate power has been increasingly popular due to abundant in nature and pollution free. The wind energy sources are undepletable, however, they are not able to generate as much electricity due to their intermittent nature. Therefore, a wind energy conversion system (WECS) with accurate maximum power point tracking (MPPT) inverter controller is essential in order to improve the wind energy capturing capability. This paper aims to develop an optimal Fuzzy logic MPPT inverter controller for grid connected wind turbine so that the energy capturing efficiency of the system can be maximized. In this research work, the MPPT inverter controller is designed on the basis of fuzzy logic control and heuristic particle swarm optimization (PSO) algorithm. The performance of WECS are demonstrated with the generated output voltage, current and power waveforms, the rectifier current waveform, DC link voltage waveform, and the generator speed. According to the results obtained for both normal Fuzzy logic MPPT control and optimized Fuzzy logic MPPT control WECS, the performance of PSO optimized Fuzzy logic MPPT inverter controller has better maximum power point performance and efficiency in tracking wind energy in terms of much smooth, less distortion and fewer fluctuation outcomes at the various step wind speed conditions. The proposed optimal Fuzzy logic MPPT inverter controller has a good potentiality for WECS in sustainable grid application.

Rapid Control Prototyping based on 32-Bit ARM Cortex-M3 Microcontroller for Photovoltaic MPPT Algorithms

Since the beginning of the war in Syria, most of the electricity infrastructure has been destroyed, leaving millions with unreliable energy. Syrians have encountered high electricity production costs and environmental damage costs resulting from the utilization of fossil fuels. Similarly, Syria has abundant solar energy can be exploited to meet its electrical power needs. However, because of a lack of expertise in solar energy conversion and the high cost of smart technology, Syrians have typically used photovoltaic systems in primitive ways, in which the efficiency of solar energy conversion is low. There is, therefore, a need for inexpensive, easy-to-implement, yet highly efficient and high performing solutions. Using the STMicroelectronics 32-bit ARM as a maximum power point tracking (MPPT) controller offers a potential solution to the problem of low conversion efficiency in standalone solar systems. In this study, using Matlab-Simulink and STMicrelectronics-32 bit ARM board, simulation and practical test is set up to evaluate the performance of the Perturbation & Observation, Incremental Conductance and Fuzzy Logic MPPT algorithms, in order to determine the most appropriate algorithm to use in small scale solar energy systems. Therefore, one main objective of this study is to explore rapid control prototyping tools for saving time and effort to the experts in the implementation process of the proposed systems. The results indicate the effectiveness of Fuzzy logic algorithm to draw more energy, decrease oscillation and provide a fast response under variable weather conditions. Furthermore, the three algorithms were able to find and track MPP.

Hardware implementation of the fuzzy logic MPPT in an Arduino card using a Simulink support package for PV application

The work presented in this study aims to develop an intelligent algorithm, based on fuzzy logic, to track the maximum power point (MPP) of a photovoltaic (PV) panel. Modelling and simulation steps of the PV panel are made by using the MATLAB/Simulink environment, before passing to the description of fuzzy logic MPP tracking (MPPT) algorithm. On an Arduino Mega 2560 controller board, a real-time implementation of the MPPT algorithm by using Simulink Support Package for Arduino Hardware in MATLAB/Simulink was conducted to experimentally validate the preliminary results of simulations. The proposed work outlines also the solution to modify pulse-width modulation frequency of Arduino when it is used with Simulink.