Fuzzy-based Maximum Power Point Tracking Research Articles

Fuzzy-based maximum power point tracking (MPPT) control system for photovoltaic power generation system

The ability of the Maximum Power Point Tracking (MPPT) technology to prevent losses by stabilizing power fluctuations during severe weather conditions is critical in improving photovoltaic power generation systems. Overall system stability is improved by carefully tracing the maximum power point (MPP). This research focuses on improving MPPT performance in solar systems by employing the "Fuzzy Logic" control method. The simulation, which is run in MATLAB/Simulink, includes a detailed model of the entire system. The primary circuit is designed with a DC-DC Boost architecture and a single MOSFET transistor. The Fuzzy Logic Controller (FLC) unit in MATLAB/Simulink generates an output variable led by two input variables via the Fuzzy Logic Controller unit. The Fuzzy Logic Controller (FLC) unit generates an output variable led by two input variables in MATLAB/Simulink. The simulation model, called "Fuzzy Disturbance," combines Perturb & Observe with Fuzzy Logic and runs for 3 s. The results show an essential increase in system efficiency to 97%. The simulation results show that the suggested approach tracks MPP, reduces output power fluctuations, and improves system efficiency.

Fuzzy-based MPPT algorithm implementation on FPGA chip for multi-channel photovoltaic system

<span lang="EN-US">Microprocessors and microcontrollers are mostly used to control electrical systems. These chips front into problems while monitoring systems that need heavy computing and important processing. Likewise, they fail while handling inputs and outputs speeds, especially with multi-channel photovoltaic (PV) systems. In comparison to a digital signal processor (DSP) and microcontroller implementations, field programmable gate array (FPGA) device is able to integrate a great number of PV channels and to achieve short development time, cost less and more flexible operation. As well, new control algorithms are increasingly complex; using new performing technologies is very motivating. Mainly, FPGA technology is adopted thanks to its ability to control complex applications and intelligent laws. In opposition to traditional controls, fuzzy logic based control presents more efficiency and reliability response for non-linear systems. Therefore, this paper deals with the execution of the fuzzy-based maximum power point tracking (MPPT) technique by the means of the FPGA chip for a multi-channel photovoltaic system. A multi-channel photovoltaic system is designed. Then, the FPGA circuit is investigated to get benefits from this hardware solution. Since software implementation way integrates a limited number of PV panels, hardware implementation is a promising solution that reduces execution time and therefore controls a huge number of photovoltaic channels. Finally, results of simulation of the fuzzy technique implementation on FPGA chip show that the proposed PV system controls more than 4400 channels. Therefore, the system output power is increased and the system profitability is improved.</span>

Metode Maximum Power Point Tracking (MPPT) dan Boost Converter Menggunakan Fuzzy Logic Controller (FLC) pada Modul Surya

Solar modules have current and voltage characteristics that are non-linear, so efforts must always be made to work at the maximum power point so that no energy is wasted. The characteristics of the solar module will change depending on the level of radiation and temperature which causes the output power of the solar module to fluctuate and become unstable. To reduce oscillations in the output power of the solar module, the Fuzzy Logic Control (FLC) method used using the boost converter. Several studies have been done to maximize the output power of solar panels, one of which is by using namely by using Maximum Power Point Tracking (MPPT). This study aims to obtain the maximum power point in a set of solar modules arranged in series and parallel through the performance of the FLC method. In tracking the maximum power point during normal operation, the fuzzy method works together with a boost converter. Fuzzy-based MPPT was tested on a solar module under several radiation and temperature conditions using Matlab / Simulink software. The Fuzzy design method shows better results compared to other methods. The results obtained show the advantages of the FLC method in terms settling time, power loss, and oscillation at the point of the operating system.

Indirect P&O type-2 fuzzy-based adaptive step MPPT for proton exchange membrane fuel cell

The output power of the fuel cell depends on the operating conditions, such as the temperature and membrane water content. Therefore, a robust maximum power point tracking (MPPT) approach is highly required to ensure the operation of the proton exchange membrane fuel cell (PEMFC) at the maximum power point. A new hybrid perturb & observe and type-2 fuzzy-based adaptive step size MPPT for PEMFC system is proposed to enhance the dynamic response and eliminates the oscillations around maximum power point at steady state. The type-2 fuzzy approach has been used to scale the variable step size of conventional perturb & observe algorithm to extract the maximum available power of the PEMFC system. The proposed hybrid controller has been tested and validated using MATLAB software via modeling of the fuel cell power system composed of 7 kW PEMFC powering a resistive load through a DC-DC boost converter. Comparative simulation results prove that our proposed hybrid P&O type-2 fuzzy-based MPPT reduces the response time between 14.62 and 84.72% compared to the P&O type-1 fuzzy-based MPPT. The response time is also reduced between 43.99 and 88.04% compared to the conventional P&O fixed step size MPPT. In addition, the proposed controller decreases the steady-state oscillation to an interval of 66.67% to 70.97% compared to the P&O type-1 fuzzy-based MPPT and an interval of 72.73% to 84.85% compared to the conventional P&O fixed step size MPPT.

Modelling and simulation of MPPT techniques for solar photovoltaic system using genetic algorithm optimised fuzzy logic controller

This paper focuses on the intelligent techniques used for tracking the maximum power point of the solar photovoltaic (SPV) system for varying environmental conditions. The most widely used perturb and observe (P&O) maximum power point tracking (MPPT) technique is discussed briefly for the comparison with the intelligent techniques. The paper proposes control technique for the SPV system by using fuzzy logic controller (FLC)-based MPPT algorithm and the optimisation of its various parameters by genetic algorithm (GA). The performance of the FLC optimised with GA is compared with the P&O and the fuzzy-based MPPT technique. MATLAB/simulink software is used to design the different stages of the MPPT controllers. Simulation results reported that GA optimised FLC perform much better than the P&O and fuzzy logic-based MPPT controllers.

Fuzzy logic-based maximum power point tracking solar battery charge controller with backup stand-by AC generator

<span>This paper presents a Fuzzy-based Maximum Power Point Tracking Solar Battery Charge Controller with backup stand-by AC generator. This study is developed to provide a maximum power point tracking battery charge controller using fuzzy logic algorithm for isolated areas that uses solar panels and AC generators. Fuzzy Logic Toolbox in MATLAB and Arduino IDE were used in implementing fuzzy logic algorithm. Fuzzy logic is a mathematical system where something can be represented in continuous values between 0 and 1. It basically represents systems based on human reasoning. The hardware comprises of four components – the switched mode power supply, the source switching circuit, buck-boost converter and the diversion load controller. The pre-testing conducted based on the methodology indicates that the proposed charge controller is efficient in maximizing the input power that enters the charge controller under different conditions. The current efficiency rate of the charge controller is 96.02%. The average battery charging time for a fully-discharged 12V Lead-Acid Battery using AC source, DC source and both AC and DC sources are 2 hours and 30 minutes, 8 hours and 15 minutes and 5 hours and 30 minutes, respectively, while discharging took 3 hours and 40 minutes with two 30-watt floodlight load.</span>

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.

An improved step‐up converter with a developed real‐time fuzzy‐based MPPT controller for PV‐based residential applications

Residential photovoltaic power plants (RPVPPs) have a wide area of utilization in PV applications. Thus, low-voltage penetration of these plants to the grid is a crucial issue for the high efficient operation of a photovoltaic (PV) system. The conventional maximum power point tracking (MPPT) methods have some drawbacks. Thus, intelligent MPPT methods are proposed in the literature to achieve these problems. This paper presents a new real-time fuzzy-based MPPT controller design for a new high gain transformer-less and single-switched power boost converter operating with different duty cycles for PV-based residential applications. The proposed structure can ensure an enhancement in voltage gain by eight times for duty cycle of 50% that is much more effective than a conventional boost converter that can gain the input voltage to two times at the output of the converter. The higher amounts of the DC voltage gain are possible by adding the novel and efficient switched capacitor (SC) blocks. The proposed control method uses designed fuzzy-based rules to control the duty cycle of the implemented power boost converter in the real-time domain. A data acquisition card is used to control the duty cycle and monitoring the PV system. The proposed fuzzy-based algorithm is performed in advanced LABVIEW software. The experimental results show that the developed fuzzy-based controller is independent of the circuit parameters and has a more reliable response for changing environmental conditions. The accuracy of the applied fuzzy-based MPPT method in the tested PV system varies between 95.8% and 99.6%.

Modelling and simulation of fuzzy-based MPPT control of grid connected PV system under variable load and irradiance

The photovoltaic (PV)-based distribution generation system has a nonlinear power characteristic curve under random variation in solar irradiance, ambient temperature and electric load. As a result, for the accurate detection and tracking of the maximum power points (MPPs), it is necessary to design an optimal controller with dynamic control capability. As solution to the above issue, this paper presents an intelligent Mamdani-based fuzzy logic controller (M-FLC) for maximum power point tracking (MPPT) of a PV system. Different test cases with respect to different possible load and irradiance variations in grid connected mode of operation are investigated. To confirm the power quality indices within IEEE standards specification, fast Fourier transform (FFT) analysis of voltage and current at the point of common coupling has been done. A detailed comparison has been made in between PV without MPPT, with incremental conductance and proposed fuzzy logic control (FLC). The results show an enhance efficiency of energy production from PV and reflects the effectiveness of the proposed scheme justifying its real time application.

Modelling and simulation of fuzzy-based MPPT control of grid connected PV system under variable load and irradiance

The photovoltaic (PV)-based distribution generation system has a nonlinear power characteristic curve under random variation in solar irradiance, ambient temperature and electric load. As a result, for the accurate detection and tracking of the maximum power points (MPPs), it is necessary to design an optimal controller with dynamic control capability. As solution to the above issue, this paper presents an intelligent Mamdani-based fuzzy logic controller (M-FLC) for maximum power point tracking (MPPT) of a PV system. Different test cases with respect to different possible load and irradiance variations in grid connected mode of operation are investigated. To confirm the power quality indices within IEEE standards specification, fast Fourier transform (FFT) analysis of voltage and current at the point of common coupling has been done. A detailed comparison has been made in between PV without MPPT, with incremental conductance and proposed fuzzy logic control (FLC). The results show an enhance efficiency of energy production from PV and reflects the effectiveness of the proposed scheme justifying its real time application.

A comparative study of maximum power point tracking methods for a photovoltaic-based water pumping system

In this paper the performance of the proposed fuzzy-based maximum power point tracking (MPPT) is investigated and compared with incremental conductance and constant voltage controller for a photovoltaic (PV) pumping system. A fuzzy logic controller with a mamdani inference engine using only nine rules is designed to track the optimum power point. An induction motor has been used to drive the centrifugal pump. The system performance is analysed for different weather conditions. A detailed comparative study presenting the merits and demerits of each technique is also presented in order to develop a relative relationship. Simulation results obtained indicate better performance of the fuzzy-based MPPT algorithm for the PV pumping system.