Top PDF Comparison of Perturb & Observe and Fuzzy Logic in Maximum Power Point Tracker for PV Systems

Comparison of Perturb & Observe and Fuzzy Logic in Maximum Power Point Tracker for PV Systems

Comparison of Perturb & Observe and Fuzzy Logic in Maximum Power Point Tracker for PV Systems

Due to the fossil fuel exhaustion and the environmental problems caused by the conventional power generation such as gasoline, coal, etc..., renewable energy sources and among them photovoltaic panels and wind-generators are now widely used [1]. Photovoltaic (PV) energy is one of the most promising renewable energy it is clean, inexhaustible and free to harvest. However, there are two main drawbacks of PV system, namely the high installation cost and the low conversion efficiency of PV modules [2]. Besides that, PV characteristics are non linear and it is very much weather dependent. Fig.1 and Fig.2 show the I-V and P-V characteristics of a typical PV module for a series of temperatures and solar irradiance levels [3, 4]. It can be noticed that PV output voltage greatly governed by temperature while PV output current has approximate linear relationship with solar irradiances. In general, there is a unique point on the I-V or P-V curve, called the Maximum Power Point (MPP), at which the entire PV system (array, converter, etc…) operates with maximum efficiency and produces its maximum output power. However, since the MPP varies with insolation and seasons, it is difficult to maintain MPP operation at all solar insolations without changes in the system parameters. To overcome this problem an intermediate DC-DC converter is proposed. The MPP tracking is applied to PV systems in order to extract maximum available power © 2014 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
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A comparison of perturb & observe and fuzzy-logic based MPPT methods for uniform environment conditions

A comparison of perturb & observe and fuzzy-logic based MPPT methods for uniform environment conditions

The power generation from photovoltaic (PV) system is not constant and it varies based on solar irradiance and temperature. For any environmental condition, to convert maximum available solar energy, PV systems must be operated at maximum power point. To accomplish that two different maximum power point tracking (MPPT) methods have been presented in this study. The first method can determine MPP point by measuring the derivative of PV cell power (dP) and PV cell voltage (dV) which is called Perturb & Observe (P&O) method. The second method uses fuzzy-logic-control (FLC) based MPPT method to determine MPP point for actual environment conditions. In this paper, 3kW PV system model is studied in MATLAB. According to the simulated results, FLC based MPPT method has better performance than P&O method. Compared to the P&O method, FLC-based MPPT can increase tracking accuracy and efficiency performance 0.13% under standard test conditions (STC).
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Comparison Between Perturb & Observe, Incremental Conductance and Fuzzy Logic MPPT Techniques at Different Weather Conditions

Comparison Between Perturb & Observe, Incremental Conductance and Fuzzy Logic MPPT Techniques at Different Weather Conditions

This method is commonly employed in PV systems since its simple, low computational demand and requires only measure the PV panel voltage and current. Here, the voltage of the PV panel is perturbed by a small magnitude (ΔV) and the changing of power (ΔP) is observed. Adjustments are made in the same direction until there is no more increment in power [3,7]. If the ΔP is less than zero, the working point will move far from the MPP, and the direction of perturbation must be inverted to return toward the MPP. This technique is summarized in Table 1.
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Implementation Of Perturb & Observe And Fuzzy Logic Control Mppt Of Pv System Using Sepic Converter

Implementation Of Perturb & Observe And Fuzzy Logic Control Mppt Of Pv System Using Sepic Converter

Significant progress has been made over the last few years in the research and development of renewable energy systems such as wind, sea wave and solar energy systems. Among these resources, solar energy is considered nowadays as one of the most reliable, daily available, and environment friendly renewable energy source [1], [2]. However, solar energy systems generally suffer from their low efficiencies and high costs [3]. In order to overcome these drawbacks, maximum power should be extracted from the PV panel using different MPPT techniques to optimize the efficiency of overall PV system. MPPT is a real-time control scheme applied to the PV power converter in order to extract the maximum power possible from the PV panel. For a fixed load, the equivalent resistance seen by the panel can be adjusted by changing the power converter duty cycle [4]. The Fuzzy Logic (FL) and the Perturb and Observe (P&O) are most known and commercially used techniques [5]-[8]. Other modified methods have been also reported to improve the performance of these techniques.
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Simulation Of A SEPIC DC-DC Converter Using Perturb And Observe And Fuzzy Logic Control

Simulation Of A SEPIC DC-DC Converter Using Perturb And Observe And Fuzzy Logic Control

The sun emits energy that is the best option for renewable energy as it is available almost everywhere which can be harnessed freely. Radiation from the sun is converted to electrical energy by using PV solar cells which exhibit photo- voltaic effect [1]. Photo-voltaic (PV) solar panel is a very simple as well as reliable technology which directly converts energy from the sun into electricity for home and industrial utilization [2-5]. Maximum power can be obtained from photo- voltaic panels using controllers [6]. Various approaches can be used to implement MPPT such as perturb and observe (P&O), incremental conductance (INC), constant voltage and short-circuit current techniques [7-8]. These techniques are used to obtain the point at which maximum power is tracked for specified solar irradiation as well as temperature but they display oscillatory behavior around the maximum power point when operating under normal conditions. Moreover the system’s response to rapid changes in temperature or irradiance is slow [10]. On the other hand the conventional PI controllers are fixed gain feedback controllers. Therefore they cannot compensate the parameter variations in the process and cannot adapt fast to changes that occur within the environment. PI-controlled system is less responsive to real and relatively fast alterations in state and so the system will be slower to reach the set point. Recently, intelligent based techniques have been introduced [9-11]. Amongst the methods which are intelligent-based, fuzzy logic has its own merit. The shape of the membership function of the fuzzy logic controllers can be adjusted such that the gap between the operation point and maximum power point is optimized. A SEPIC converter is used to provide a constant dc bus voltage [10-12]. The analysis of two MPPT techniques (P & O and FLC) of PV solar SEPIC converter that extracts maximum power from the module for varied solar irradiation and temperature is presented. Comparison and Modeling of the system is done using MATLAB/SIMULINK.
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A Review on Different Maximum Power Point Tracker (MPPT) Techniques for PV and Wind Systems

A Review on Different Maximum Power Point Tracker (MPPT) Techniques for PV and Wind Systems

Sensor less technique are more reliable, efficient, fast and cost- effective compared to sensor based [6]. These MPPT algorithms can be categorized mainly into two types; Power Signal Feedback (PSF) or look-up table based method and Hill climb search (HCS) or Perturb & Observe (P&O) method. These methods do not require measurement of wind speed via anemometer. To converge the power at the maximum point, firstly optimum TSR is obtained by adjusting rotor speeds. For this purpose some rules are developed to control the shaft speed.
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Maximum Power Point Tracking For Photovoltaic System Using Modified Perturb and Observe Method

Maximum Power Point Tracking For Photovoltaic System Using Modified Perturb and Observe Method

As one of the prominent renewable energy resources, photovoltaic (PV) generation has been increasingly gaining considerable attention among industry players all around the world . In most of the PV applications, the key function of PV system is to extract maximum power from PV array during the daytime. The power-voltage characteristics have nonlinear characteristics that depend on environmental conditions like irradiance and temperature . At each irradiance level, there exists a unique maximum power point of power - voltage curve of PV array. Maximum Power Point Tracking (MPPT) control algorithm of PV power converter is the function to maximize the power generation efficiency by regulating the PV array voltage, i.e. the input voltage of the converter. There have been many algorithms developed for MPPT, e.g. perturbation and observation (P&O) method, the fractional open circuit voltage, short circuit current, the fuzzy logic control among which P&O method is well preferred duo to its ease of implementation and low cost[1]- [4]. Instead of these advantages this method has the drawback of high time and low tracking speed. Therefore new proposed method eliminates these drawbacks by increasing the tracking speed and locating the exact maximum power point.
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A Proposed Maximum Power Point Tracking by Using Adaptive Fuzzy Logic Controller for Photovoltaic Systems

A Proposed Maximum Power Point Tracking by Using Adaptive Fuzzy Logic Controller for Photovoltaic Systems

Abstract. This paper presents a proposed method to search Maximum Power Point (MPP) based on the Adaptive Fuzzy Logic Control (AFLC), which is applied to photo- voltaic (PV) systems under varying temperatures and radiations. The proposed system is composed of boost converter, two fuzzy controllers and load. Whenever environmental conditions change in wide range, using only Conventional Fuzzy Logic Controller (CFLC) is not adequate and causes more errors in tracking. The proposed AFLC comprises two stages: Online and Oine tuning. The oine method, by accurately setting CFLC controller parameters, is applied for relatively stable atmospheric conditions. Meanwhile, the online method is considered for unstable atmospheric conditions and contains two fuzzy controllers - one primary, one secondary. The primary fuzzy controller is the CFLC, and the secondary controller is the decision-making, which due to atmospheric conditions, alters the primary fuzzy controller parameters in order to achieve a better answer compared to utilizing CFLC. Decision-making controller with changing in irradiation and temperature changes gain of inputs of CFLC, simultaneously, that it increases rate and accuracy of tracking in comparison with using only fuzzy controller. By simulating results using CFLC and AFLC controllers, the proposed method is able to improve performance indicators with respect to CFLC.
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Comparison between Standard Perturb & Observe Technique and Modified P&O with Fuzzy Logic Control

Comparison between Standard Perturb & Observe Technique and Modified P&O with Fuzzy Logic Control

The necessity of using a clean electric power can be found in the attempt to try avoiding degrading our environment any more. Sunlight is an excellent source of energy which can be utilized for electric power generation. The maximum power point varies with radiation intensity and temperature, and it is important that any solar photovoltaic (SPV) array is used to its maximum potential by using an appropriate Maximum Power Point Tracking (MPPT) technique. Many research papers have focused on increasing the efficiency of the overall solar PV system by ensuring maximum power capture and have compared MPPT techniques. It has been concluded that the Perturb & Observe (P&O) and Incremental Conductance (IC) algorithms are the most efficient of all the MPPT techniques that have been analysed.
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PV maximum power point tracking based on single-input fuzzy logic controller

PV maximum power point tracking based on single-input fuzzy logic controller

to find voltage and current of a photovoltaic module at which it will operate at maximum power output under certain temperature and irradiance. MPPT methods are categorized in two types which are conventional methods and intelligent methods. Examples of conventional MPPT are perturb and observe method (P & O), incremental conductance method (IC) and hill climbing method (HC). Examples of intelligent MPPT are fuzzy logic control method (FLC), artificial neural network method (ANN) and evolutionary algorithm method (EA). P & O method and IC method are usual because there are easy and simple to be implemented [5]. However, P & O method has two disadvantages which are power oscillation at maximum power point (MPP) and divergence of MPP under rapid atmospheric change [2]. IC method also has a problem of power oscillation when fast tracking of the maximum power is desired [5]. Fast convergence to MPP and minimal oscillation about MPP can be achieved using fuzzy logic control method [2]. However, conventional fuzzy logic control method yields complex control rules. The conventional FLC presents difficulty of modification and tuning of control rules. Due to this problem, author in [6] simplified the inputs of FLC to one input which is known as simplified fuzzy logic controller (S-FLC). The control rules are reduced, hence it is easier to modify and tune the control rules.
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Fuzzy Logic Based SEPIC Converter for Maximum Power Point Tracking in PV System

Fuzzy Logic Based SEPIC Converter for Maximum Power Point Tracking in PV System

ABSTRACT: In this paper, a fuzzy logic control (FLC) is proposed to control the maximum power point tracking(MPPT) for a photovoltaic (PV) system. The proposed technique uses the fuzzy logic control to specify the size of incremental current in the current command of MPPT. As results indicated, the convergence time of maximum power point (MPP) of the proposed algorithm is better than that of the conventional Perturb and Observation (P&O) technique.This paper DEVELOPS a fuzzy controller (FC)-based single-ended primary-inductor converter (SEPIC) for maximum power point tracking (MPPT) operation of a photovoltaic (PV) system along with battery. The FLC proposed plan utilizes the focalized circulation of the enrollment capacity. The heap is encouraged from the battery stockpiling consistently with steady voltage. The battery will be accused of the assistance of PV module and the SEPIC converter, which is controlled by FLC-based MPPT. The proposed FLC-based MPPT with battery will supply more energy to the heap than the with perturb & observe framework.
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A robust maximum power point tracking control for PV panel using adaptive PI controller based on fuzzy logic

A robust maximum power point tracking control for PV panel using adaptive PI controller based on fuzzy logic

We can also classify MPPT control methods into direct MPPT control and indirect MPPT control. A controller that maximizes power conversion by directly modifying the duty ratio is a direct MPPT control. Several articles on direct MPPT control method have been published i.e. perturb and observe (PO) [4, 5], modified PO [6], IC [4, 5, 7, 8], incremental resistance (IR) [9], cuckoo search algorithm (CSA) [10], ICM with fuzzy logic [11], FLC [12-20], adaptive FLC based on two layers FLC [21], FLC using auto scaling variable step-size [22], and constant PID control [15, 16]. Genetic algorithm (GA) is used to optimize constant proportional-integral (PI) control [23], Ant colony algorithm (ACO) is utilized for optimizing constant PI control [24], gradient descend method is adopted for PID control optimization [25], FLC is used for adaptive PID control [26, 27], adaptive scaling factor is used for fuzzy gain scheduling (FGS) PID control [28], and Big Bang-Big Crunch (BB-BC) algorithm is used to tune a fuzzy PID controller [29]. In an indirect MPPT control an external controller sends a reference command signal to an internal controller. For the external controller, various methods have been proposed i.e. dP/dV feedback control [30-32], modified PO [33, 34], and PO with FLC [35]. For the inner loop controller, several controllers have been published i.e. proportional (P) [30, 31], proportional integral (PI) [33-37], the root-locus technique based PID controller [38], fuzzy logic based adaptive PID controller [39], adaptive MPPT using auto-tuning [40], and constant controller based on Youla parameterization [41].
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Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control

Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control

MCU chip and needs no memory space to save fuzzy rules, and that optimizing factor in the fuzzy inference equation can adjust fuzzy rules on-line automatically to improve system control effect, which provides the system with an intelligent characteristic. An intelligent control method for MPPT of a photovoltaic system under variable temperature and insolation conditions which uses a fuzzy logic controller applied to a DC– DC converter device is proposed in Cheikh Aı¨t et al. (2007) . Results of this simulation are compared to those obtained by the perturbation and observation controller. A fuzzy logic con- trol (FLC) is proposed in Takun et al. (2011) to control MPPT for a photovoltaic (PV) system; this technique uses the fuzzy logic control to specify the size of incremental current in the current command of MPPT. This paper presents a maximum power point tracker (MPPT) using Fuzzy Logic for a PV sys- tem. The work focused on the well known Perturb and Ob- serve (P&O) algorithm and compared to a designed fuzzy logic controller (FLC). A simulation work dealing with MPPT controller, a DC/DC C´uk converter feeding a load is achieved. The results will show the validity of the proposed Fuzzy Logic MPPT in the PV system. Most of the performed works in the literature reviews in this point is based on assumed not actual solar radiation data but this paper is used a real data for solar radiation measured by solar radiation and meteorological sta- tion located at National Research Institute of Astronomy and Geophysics Helwan, Cairo, Egypt which is located at latitude 29.87N and longitude 31.30E. The station is over a hill top of about 114 m height above sea level. Example of the daily re- corded measured solar radiation is shown in Fig. 6 .
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Maximum Power Point Tracking using Fuzzy Logic Control for Photovoltaic Systems

Maximum Power Point Tracking using Fuzzy Logic Control for Photovoltaic Systems

Abstract—In this paper, a fuzzy logic control (FLC) is proposed to control the maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed technique uses the fuzzy logic control to specify the size of incremental current in the current command of MPPT. As results indicated, the convergence time of maximum power point (MPP) of the proposed algorithm is better than that of the conventional Perturb and Observation (P&O) technique.

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A simple checking algorithm with perturb and observe maximum power point tracking for partially shaded photovoltaic system

A simple checking algorithm with perturb and observe maximum power point tracking for partially shaded photovoltaic system

In the meantime, some researchers suggest the use of some soft computing based method such as Artificial Neural Network (ANN) and Fuzzy Logic Control (FLC) [6-9]. These methods can assure a good performance and can perform well in various atmospheric condition, besides not requiring accurate mathematical modelling. Nevertheless, the effectiveness of these methods depends on the user knowledge. The users must have background knowledge about PV arrays study to get optimization use. Particle Swarm Optimization (PSO) is an advance technique used by researches [10-12]. It gives great performance in any condition compared to the others but the method is too complex and expensive for domestic use.
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PV maximum power point tracking based simplified fuzzy logic

PV maximum power point tracking based simplified fuzzy logic

Maximum Power Point Tracking (MPPT) algorithm are consist Perturb and Observe or Hill Climbing, Incremental Conductance, fractional open circuit voltage and fractional short circuit current. All the functions of the Maximum Power Point Tracking (MPPT) stated as before, are in order to control the swing of the power maximum values. This is to prevent the power maximum values decrease sharply or rapidly drop neither left nor right of the characteristic Power vs. Voltage curve. On the other hand, by optimize and simplified its algorithm by using Fuzzy Logic Controller (FLC) cum MATLAB software application.
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A Simple Checking Algorithm with Perturb and Observe Maximum Power Point Tracking for Partially Shaded Photovoltaic System

A Simple Checking Algorithm with Perturb and Observe Maximum Power Point Tracking for Partially Shaded Photovoltaic System

In the meantime, some researchers suggest the use of some soft computing based method such as Artificial Neural Network (ANN) and Fuzzy Logic Control (FLC) [6-9]. These methods can assure a good performance and can perform well in various atmospheric condition, besides not requiring accurate mathematical modelling. Nevertheless, the effectiveness of these methods depends on the user knowledge. The users must have background knowledge about PV arrays study to get optimization use. Particle Swarm Optimization (PSO) is an advance technique used by researches [10-12]. It gives great performance in any condition compared to the others but the method is too complex and expensive for domestic use.
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A Review on Perturb and Observe Maximum Power Point Tracking in Photovoltaic System

A Review on Perturb and Observe Maximum Power Point Tracking in Photovoltaic System

A number of soft computing based MPPT methods also have been proposed, such as Fuzzy Logic Control (FLC) [10, 11] and Artificial Neural Network (ANN) [12]. The advantages of using FLC method are it does not require accurate mathematical model and can operate well under varying atmospheric conditions. Yet, the effectiveness of this method depends a lot on the user knowledge. Even though MPPT with ANN controller can provide good performance, the neural network must be trained specifically beforehand with the PV array [4, 13].

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Overview of Maximum Power Point Tracking Control Methods for PV Systems

Overview of Maximum Power Point Tracking Control Methods for PV Systems

The classic perturb and observe (P & O) method has the disadvantage of poor efficiency at low irradiation. For this reason, alternative solutions have been proposed. For example, Cristinel, Uffe, and Frede [14] combine a constant voltage (CV) algorithm with a modified P & O method as shown in (Figure 3) to track the MPP with high efficiency under both low and high solar irradiation conditions. The algorithm operates by increasing the duty cycle until the PV out put voltage is close to the open circuit voltage of the panel (VOC), this is then used as the initial conditions for the MPP tracker. The algorithm then evaluates the current output; if the current is higher than (0.7 A) the algorithm adopts the PO method; if it is lower it converts to the CV method. Simulation results demonstrate that overall greater energy can be extracted from the PV panel; efficiency levels of 95% to 99% are quoted over a wide irradiation range [14]. However, there is complication of combining the two methods.
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Simple maximum power point tracker based 
		on perturb and observe technique for PV module

Simple maximum power point tracker based on perturb and observe technique for PV module

voltage and current of the PV system. NN can be used to estimate the MPP voltage of the system and adjust the duty ratio required for driving the operating point to MPP [20]. The calculation system also can be used in MPPT applying fuzzy control method to track the MPP through a mathematical system to preciously detect the parameters of the MPP without requirement a lot of measurements [13]. Among the MPP methods, the perturb and observe can be considered the simplest technique for tracking the optimal operating point in any PV system. In this technique, according to the desired intervals, the system periodically increases or decreases the operating point by a certain increment and continuously compares the voltage and current (power) with the previous perturbation to reach the MPP. This method can lead to some oscillations around the MPP [21 & 22].The hybrid MPPT uses more than one technique to increase the system accuracy but it becomes more complicated. It can use, for example, NN with fuzzy controller [19] or NN with perturb and observe [23].
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