Among renewable energy sources, solar energy used in **photovoltaic** (PV) system is the most favorite list in renewable energy researches today. Due to its maintenance free, ease of implementation and free of pollution, its demand increases rapidly in residential and industrial applications. However, PV cell appears to have low **power** efficiency in the range of 15-30% and its market price is still expensive; these factors are the main disadvantages. Due to its nonlinear characteristic, a **control** technique, known as **maximum** **power** **point** **tracking** (MPPT), is a must in PV system in order to make sure that the output **power** of PV system is always staying at **maximum** **power** **point** (MPP). In general, MPPT can be divided into conventional and artificial intelligent algorithms. The most popular conventional algorithms are perturb and observe (P&O) and incremental conductance (IC). Their main weakness is these algorithms always fail to track MPP and high oscillation occurs whenever the sunlight (irradiance) changes frequently. Among artificial intelligent algorithms used in MPPT are neural network, **fuzzy** **logic** **control** (FLC) and genetic algorithm. In this work, FLC was selected because it is easy to be implemented and does not require mathematical model in its design.

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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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MPPT capacity is generally consolidated in the solar **based** **power** administration framework to guarantee that most extreme accessible **power** is gotten from the sun powered **photovoltaic** board. As of late, **fuzzy** **logic** controller has gotten an expanded thoughtfulness regarding specialists for **converter** **control** and MPPT plan. A dependable and exact PV imitating model is vital for quickening the improvement of a MPPT framework. The PV imitating model needs to give very much managed output voltage and current as indicated by the attributes of the PV model. A voltage controlled buck **converter** **based** PV emulator plan. Configuration of current controlled buck **converter** **based** PV emulator. In [3], an 8-bit microcontroller controller two-switch buck-support **converter** **based** PV emulator utilizing piecewise straight way to deal with speak to the PV qualities is accounted for; configuration of a double mode **power** controller for PV module imitating framework. To abstain from exchanging clamor and voltage great uses a direct controller for the **power** source as opposed to utilizing exchanging sort **power** converters.

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The grid connected **photovoltaic** system **Fuzzy** **Maximum** **Power** **Point** **Tracking** (Fuzzy.MPPT) **based** on Modified **Single** **Ended** **Primary** **Inductor** **Converter** (Mod.SEPIC) using **Fuzzy** **Maximum** **Power** **Point** **Tracking** (Fuzzy.MPPT) has been presented in this paper. The performance can be achieved with the help of **fuzzy** **based** MPPT to track the **maximum** **power** thereby increases the reliability and performance of the system.The voltage and frequency conditions were explained helps in turn to achieve grid synchronization. The purpose of three phase Hex bridge inverter was explained and the system was monitored using Embedded controller. The simulation was performed by using MATLAB/Simulink software. The whole system has been analyzed and tested and the results were tabulated for various radiations under different climatic and load conditions.

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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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between every two Perturb processes in search for the maxi- mum PV output (EPP) is proposed in Liu et al. (2004) . An intelligent approach for MPPT DC/DC Boost **converter** fo- cused on P&O algorithm and compared to a designed **fuzzy** lo- gic controller is presented in Farhat and Sbita (2011) . A comparative study of two type of **maximum** **power** **point** track- ing (MPPT) which is Perturb and Observe (P&O) and incre- mental conductance method are introduced in Kumar et al. (2011) . An Artiﬁcial Neural Networks is proposed in Amrouche et al. (2007) to detect the atmospheric conditions variations in order to adjust the perturbation step for the next perturbation cycle. The presented **tracking** algorithm shows better steady state and dynamical performance than tradi- tional P&O. The implementation of **fuzzy** **logic** controller **based** on the change of **power** and change of **power** with re- spect to change of voltage is studied in Chin et al. (2011) , **fuzzy** determines the size of the perturbed voltage. The performance of **Fuzzy** **Logic** with various membership functions (MFs) is tested to optimize the MPPT. **Fuzzy** **Logic** can facilitate the **tracking** of **maximum** **power** faster and minimize the voltage variation. A novel intelligent **fuzzy** **logic** controller for MPPT in grid-connected **photovoltaic** systems **based** on boost con- verter and **single** phase grid-connected inverter is introduced in Zeng et al. (2005) . This is simple to be implemented on

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Abstract— In this paper, a modified P&O technique that uses **fuzzy** **logic** to **control** the amount of perturbation depending on operating **point** of PV solar cell is proposed. **Fuzzy** **logic** is used to achieve smooth adaptations in duty cycles as well as to **control** the rate of adaptation. Proposed method helps reducing steady state oscillations and increasing convergence speed. SEPIC (**Single** **Ended** **Primary** **Inductor** **Converter**) is employed to realize appropriate output from proposed MPPT **control**. The limitation of SEPIC to track dynamic variations is overcome by fast operation of **control** algorithm. The system is simulated and tested in MATLAB/Simulink.

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DC/DC powerconverters are most widely used in **photovoltaic** conversion systems as an intermediate between the PV generator and the load to operate thePV system at its MPP. In this study a boost **converter** is applied. As is depicted in fig. 5, this **converter** includes capacitor, **inductor** and electronic **power** switch. All of these components in the ideal case do not consume **power**; this is the reason why the choppers have good yields [6].The switch is turned ON and turned off by the PWM signal given by MPPT controller. The voltage gain of this **converter** can be expressed as:

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By looking at the MPPT **tracking** algorithm **point** of view there are various methods of MPPT. These methods are implemented by designing various algorithms like Perturb-and-observe (P&O) method, Open- and Short-circuit method, Incremental Conductance algorithm, and other algorithms. The best MPPT technique base on cost versus energy generation is the P&O [2]. Since accuracy and fast **tracking** response conflict one from other, the mentioned **tracking** methods cannot satisfy, simultaneously, both of them. In place of the traditional and spread methods, some researcher have proposed complex MPPT algorithms, **based** on **fuzzy** **logic** and neural network, in order to accomplish fast **tracking** response and accuracy in a **single** system. These proposals, however, present some disadvantageous: needed for high processing capacity, increasing the complexity and cost of the design, in some cases, employment of extra sensors.

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In this next section, the modeling of the solar panel is explained. Since most of the theory and equations were presented in Chapter II, this section focuses on how those equations were constructed in Simulink. In Appendix B, one can examine the way the solar model equations were implemented using Simulink. This model accepts the solar irradiance, the temperature, the angle of incidence, the input voltage, and the input current as inputs. As stated before, this method takes the input voltage and current and finds the equivalent voltage and current on one cell assuming all other cells are operating the same. From there, it implements the equations that govern the solar cell. Equations (3) and (4) can be seen at the top of Figure 91, and in Figure 92, (5) can be identified. The model is built to accept the two-diode model, so it may seem like there are two versions of (5) within Figure 92. In this thesis research, only the **single**-diode model was used by setting the reference reverse saturation current for diode #2 I s2,ref to zero as seen in Appendix A. Equation (6) is throughout this entire model in various elements, which can be recognized in both Figures 91 and 92. After executing the equations, the current through the entire array is calculated as described in Chapter II near (7). Additionally, this model finds the solar cell efficiency, which is the amount of **power** the panel puts out divided by the amount of **power** incident upon its surface. This calculation can be seen in the lower right portion of Figure 92. Finally, it outputs the updated input current as well as the solar cell efficiency.

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Abstract: Due to the increase in demand of electric energy, people are shifting towards renewable energy resources. Among the various renewable sources, solar **power** is growing at a faster rate due to its abundant availability. Solar panel characteristics show that the output **power** from the panel peaks only at one operating **point**. For extracting **maximum** **power** from the panel, **tracking** of the operating **point** has to be done continuously by a **control** algorithm. In this work, a solar system connected to the grid is considered, where there is a necessity of synchronizing the solar inverter with the grid apart from MPPT. Hysteresis current **control** is implemented in the inverter for synchronization and for **tracking** the **maximum** **power**, perturb and observe algorithm is applied. Grid connected PV systems are used in main cities by domestic and industrial consumers, where the excess **power** can be sold to the utility. Simulation of the grid connected PV system is done in Simulink and the results are presented.

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Abstract:- This article examines models of **photovoltaic** solar panels, the non-inverting Buck-boost **converter**. The **control** strategy of the **converter** using the MPPT with the PI regulator is presented. The simulation is performed in the PSCAD- EMTDC software. The results show a good performance of the used models and controls. This article can be considered as an update of the models used and a complement in the **control** of the non- inverting Buck-boost **converter**.

Chapter 2 provides an overview of solar **photovoltaic** (PV) DC-DC **converter** system focused on the development of the **photovoltaic** cells modelling methods to identify its dynamic and transient characteristic. It will also identify the factors that influence the characteristics of the **photovoltaic** cells, and the effects of partial shading on the solar PV system. It will discuss the overview of MPPT **control** methods of **power** **converter**, and methods of interfacing of the solar PV with DC-DC **converter** used by other researchers; Different MPPT methods are analyzed and compared on the basis of hardware requirement, speed, accuracy, applicability, cost and the sensors used. The merits and drawbacks of the **control** methods are discussed lastly.

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by connecting many PV cells of the same type in series chains. These long chains of PV cells incur operational complications; the characteristics of the chained PV cells are not identical, hence they may not conduct the same current at their operating points. The cells which are least efficient set the safe operating current of the string. A more serious problem occurs when illumination of the chains is uneven, i.e. a subset of the PV cells is shaded. The current must be limited to the **maximum** forward current in the shaded set to avoid driving any of these into a reverse voltage condition. This always absorbs **power** and can result in reverse breakdown and overheating. The use of by-pass diodes has limited this problem, but **power** potentially available from by-passed cells is lost. Various **control** methods, such as such as perturb and observe (P&O) (Femia et al.,2009; Killi and Samanta, 2015; Elbaset et al., 2016), Incremental conductance (IncCond) (Elgendy et al., 2012; Radjai et al., 2014; Li et al., 2016), hill climbing (HC) (Alajmi et al., 2011; Xiao et al., 2016), **fuzzy** **logic** (Messai et al., 2011; Letting et al., 2012; Cheng et al., 2015; Rezvani and Gandomkar, 2016), artificial neural network (ANN) ( Liu et al., 2013; Boumaaraf et al. 2015; Lin et al., 2016; Messalti et al., 2017), particle swarm optimization (PSO) (Ishaque et al., 2012; Cheng et al., 2015; Letting et al., 2012; Manickam et al., 2016; Renaudineau et al., 2015), sliding mode (Kim, 2007; Chu and Chen, 2009; Zhang et al., 2015; Mojallizadeh et al., 2016; Ouchen et al., 2016) and so on, have been proposed to enable optimal **power** generation from the chained PV strings with by-pass diodes and under partial shading conditions, but satisfactory solutions in terms of simultaneously maximizing the **power** generated and protecting the PV panels have still been a challenge (Rezk and Eltamaly, 2015; Chen et al., 2015; Liu et al., 2016; Kumar and Chatterjee, 2016; Gupta et al., 2016).

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Learning rate parameter is selected by the user and, as it can be deduced from equation (2), it plays an important role in the convergence of the network in terms of success and speed. For our experiments the most commonly used parameters are selected. The inspection of advanced possibilities related to neural network learning procedures confirms a broad field of investigation and could be, therefore, a **point** of further experimentation. In the back reproduction learning algorithm online training is usually considerably quicker than batch training, especially in the case of large training sets with many similar training illustrations. On the other hand, results of the training with back propagation and update after every pattern presentation, heavily depend on a proper choice of the parameter η. The back propagation weight update rule, also called generalized delta-rule, for the NN software reads as follows:

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Ph.D degrees all in Electrical Engineering from Kyoto University Japan in 1969, 1971, and 1980, respectively. He joined Kumamoto University in 1971 and has been a Professor from 1989. During the period of June 1985 through September 1986, he was at Clarkson University, and was involved with **power** system harmonic research. His current interests include intelligent system applications to electric **power** systems and the applications of renewable energy **power** sources to **power** distribution systems operation, **control** and management. He is a Senior Member of IEEE, a member of IEE of Japan and Japan Solar Energy Society.

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The responce of the system when we use ANFIS **control** is better than FLC **control** but with an overshooting in the dynamic response. In the both system of controls produce a **maximum** **power** **point** voltage of 20.17V as shown in Fig. 10a and Fig. 11b corresponding to characteristic P-V and I-V. The outputs of FLC and ANFIS regulators are connected to the boost **converter**, so they produce a duty cycle as shown in Fig. 13d. At steady state conditions output **power** reached the value of 112 W.

The variables are identified and the set values of each linguistic variable are determined. The input variables of the FLC controller are the input variables of the **fuzzy** **logic** controller are the slope of the **power** variation, E (k) and the slope, CE (k) of the PV panel. The output of the FLC controller is the duty cycle of the PWM signal controls the **converter** switching gate. The triangular membership functions is used for the FLC. The **fuzzy** **logic** **control** system can be generalized presented in Figure 3.

Abstract—The output **power** of PV module varies with module temperature, solar insolation and loads. And in order to quickly and accurately track the sun, it is necessary to track the **maximum** **power** **point** (MPP) all the time. After studying various algorithms, a new algorithm was presented in this paper **based** on online short-circuit current,open- circuit voltage, and variable step of perturbation and observation method. This algorithm could track MPP change rapidly and accurately without the disturbance of **photovoltaic** system, and also can reduce the **power** oscillation around MPP and the light mutation of the false judgement phenomenon. A theoretical analysis and the designed principle of the proposed algorithm are described in detail. And some experiment and simulation results are made to demonstrate that the effectiveness of the proposed algorithm and also the proposed could reach MPP which is faster than traditional P&O method about 0.2s. The system has a good dynamic and steady-state performance.

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the left side of P-I characteristic (e.g., **point** B in Fig.3). In the proposed **fuzzy** MPPT of this paper, this problem is solved by considering the variation of **converter** duty cycle as the input of **fuzzy** controller, and therefore the operating **point** is moved to MPP2. The **fuzzy** MPPT of reference [9] behaves like the hill climbing technique and has similar disadvantages (e.g., steady-state oscillation about the solution, wrong **tracking** direction under rapidly changing irradiation, …).