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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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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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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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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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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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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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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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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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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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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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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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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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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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.

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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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 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].

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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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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