The dust particles deposited on a **PV** surface at a controlled surface mass density, and the **power** output was measured. The effects of dust deposition on solar panels related to their use in the study of Mars and the Moon [39]. The role of wavelength in the transmittance and reflectance efficiency variations of glass samples coated with dust. The wavelength ranged from 190 to 900 nm because the majority of **PV** modules are not responsive outside this range. Dust accumulation on solar collectors located in deserts zones vary widely; these areas also experience dust storms that not evenly distributed over the year. Most large-scale **PV** modules installed at a fixed tilt angle. Photovoltaic systems equipped with solar trackers can be used to produce **maximum** **power** output and to minimize dust accumulation. **Tracking** also can provide panel orientation that can be used for convenient cleaning and for showing the groups facing down at night and during dust storms. High relative humidity attachment of dust on **PV** surface, high relative humidity also causes more absorption of solar radiation by the concentration of vapor water by the environment. Dust storms cause major loss of the performance of **PV** installations. These storms are mostly unpredictable, except that they occur more frequently in certain months of the year. Solar collectors equipped with **tracking** systems can reduce the adverse effect of such dust episodes if they are stored at peak positions to minimize the impact of dust storms. Frequency of cleaning is critical, as the adhesion of dust increases with the residence time of the dust on the collectors before each cleaning. Light rain in dusty weather leaves the collector surface spotty with a sticky soil layer that drastically degrades performance. Immediate cleaning after such events is recommended to restore systems efficiency [40].

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characteristics of **PV** modules. These techniques provide fast and powerful computational solution to the problem of MPPT. In recent years, much research has been done on the use of adaptive neuro **fuzzy** inference systems (ANFIS) to track the **maximum** **power** **point** (MPP) of **PV** **power** generators. ANFIS systems are actually **fuzzy** inference systems tuned by neural networks. Thus, they combine the computation **power** of neural networks with the reasoning capability of **fuzzy** inference systems. In addition, they can automate the generation of **fuzzy** rules. Fig.3 depicts the block diagram of the proposed MPPT Controller. The objective of the controller is to determine the duty cycle, D, of the converter, by which the converter delivers the **maximum** attainable **power** to the load at any given temperature and irradiance. Controller generates PWM signal for the converter. The first part of the controller, Adaptive Neuro-**Fuzzy** Inference **System** (ANFIS), works as a reference model of the **PV** array and finds the suitable **maximum** voltage under a given temperature and irradiance while the FL controller produces the change of D by comparing the **maximum** voltage of reference model and the output voltage of the **PV** array.

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where πΌ ππ£,ππππ is the current generated by the incident light, πΌ π is the Shockley diode equation, πΌ 0,ππππ is the reverse saturation or leakage current of the diode, q is the electron charge [1.60217646Γ10 β19 C], k is the Boltzmann constant [1.3806503Γ10 β23 J.K -1 ], T [K] is the temperature of the p-n junction, and a is the diode ideality constant. A shunt resistance (R p ) and a series resistance (R s ) component are added to the model since no solar cell is ideal in practice. A typical characteristic of **PV** **power** model curve and voltage curve is shown in Figure 3. In this study, it is only used a single **PV** panel with the parameters shown in Table 1.

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Where ΞD is the step variety of the duty cycle in the former sampling period. The performance of the MPPT **system** is essentially decided by the scaling factor N for the variable step-size MPPT algorithm. Manual adjusting of this **parameter** is slow and tedious, and the acquired optimal values may be just suitable for a given **system** and certain operating conditions. To guarantee the convergence of the MPPT update rule, the variable step rule must meet the following inequality:

The P and O algorithm was simulated **using** matlab/Simulink software on the model presented in fig.8 for the same conditions applied in **fuzzy** **logic**. The P and O succeed in changing the duty cycle of the buck-boost converter to attain the output **power** of the module at its **maximum** value. Fig.11 and fig.12 show the results in this case. Under 10β¦ load, the output **power** remained at steady state with no overshoot, but with chattering behaviour around the MPP which may lead to loss of **power**.

In times that require a numeric answer, the **fuzzy** output set is transformed into a unique value for the defuzzification process, ie, the output value of linguistic variable inferred by the **fuzzy** rules is translated into a numerical value (crisp) that will act in the process to regulate it. The term defuzzification is equivalent to processing **fuzzy**- scale, corresponding to a mapping of the **fuzzy** control actions space and set on the universe of discourse for the space not **fuzzy** or scalar actions. The used methods are Center of Gravity (CoG) or Area of Center (CoA) as presented in Figure 4. This method calculates the duty cycle variation ΞD output, by determining the centroid of area composed which is the **fuzzy** output function.

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Many techniques to track **maximum** **power** **point** (MPP) have been proposed since earlier nineties. These me- thods differ in terms of requiring sensors, cost, efficiencies, complexity, and in convergence speed. Conven- tional algorithms, such as P & O, fail to reach the global **maximum** **power** **point** of **PV** **system** under partial shading conditions because of fixed step size [7]. Chao et al. proposed an extension method that uses a variable step size to ensure finding the global **power** **point** but the **system** takes long time to recover and reach the steady state under sudden changes [8]. Recently, Heydari-Doostabad et al. proposed a new approach **based** on Extre- mum Seeking Control (ESC) algorithm [9]. Although their proposed method found the global **maximum** **point**, it is considered highly complicated.

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As the demand of energy is increasing day by day it is more desirable to switch to the renewable energy sources and solar photovoltaic **system** is ideal source of green energy. These **PV** systems can be operated by either connecting to the grid or as stand-alone structures. The major disadvantage of **PV** **power** generation systems is that the amount of electric **power** generated by **PV** module varies with the change in the weather conditions, i.e., irradiation level. Under Partial shading condition [2] it is important to extract **maximum** **power** in **PV**- fed applications. Hence it is really crucial to use a **maximum** **power** **point** **tracking** (MPPT) control method to achieve **maximum** **power** (MP) output in real time in **PV** generation systems. Till now many **maximum** **power** **point** **tracking** (MPPT) methods have been presented [4-21] and used. Most of these consist of two step techniques. In one of the methods [4] after the PSC is detected the load line is moved **based** on short circuit current and open circuit voltage of the array. In order to obtain GMPP But none of them is able to track the **Maximum**

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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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configuration of three-phase grid-connected photovoltaic **system** consists of solar array, a three-phase Hex bridge inverter, and a grid voltage. The three-phase Hex bridge inverter with filter inductor converts a DC input voltage into an AC voltage by **using** a corresponding switch signals to make the output current in phase with the utility voltage and obtain a unity **power** factor. Many algorithms have been developed for the MPPT of a **PV** array. The **fuzzy** logics are applied for the **maximum** **power** **point** **tracking** controller in which the MPPT techniques are more popular because of the simplicity of its control structure. The sliding-mode controller is famous for signal stability and easy implementation [14]-[18]. Different authors used one cycle control for MPPT [19] whereas, the authors used conventional PI regulator along with the MPPT scheme [20]. Among different intelligent controllers, the **fuzzy** **logic** is the simplest way to integrate with the **system** and provides greater response than conventional controllers. .

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Sun and wind are an inexhaustible pollution free source of energy and solar cell have little to no operating cost with lifetime on the 30 years, and also same way of wind **power** energy have continuous flow of **power**, the only monetary investment is the initial manufacturing cost. By **using** photovoltaic effect, in which photons are converted into electricity, solar energy can be used for direct electricity generation. The efficiency of **PV** cell is affected by various atmospheric parameters like irradiance, temperature, humidity, dust, wind etc. However, solar energy is free and inexhaustible **PV** cell are not used commonly because of their initial cost and efficiency. Many engineers, scientists and researchers are trying to improve the solar cell efficiency by considering the effect of atmospheric **parameter** like radiation, temperature, humidity etc. and changing the material of **PV** cell such as Si, GaAS etc.

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A **maximum** **power** **point** tracker is a high-efficiency DC-DC converter, which functions as an optimal electrical load for photovoltaic cell, most commonly used for a solar panel or array and converts the **power** to a voltage or current level which is more suitable to whatever load the **system** is design to drive. **PV** cells have a single operating **point** where the values of current and voltage result in a **maximum** **power** output for the cell. **Maximum** **power** **point** tracker is basically an electronic **system** that controls the duty circuit of the converter to enable the photovoltaic module operate at **maximum** operating **power** at all condition. The advantages of MPPT regulators are greatest during cloudy or hazy days or cold weather. There are different types of **maximum** **power** **point** **tracking** methods developed over the years and they are listed below as follows (1) Perturb and observe method, (2)Incremental conductance method, (3) Artificial neutral network method, (4) **Fuzzy** **logic** method, (5) Peak **power** **point** method, (6) Open circuit voltage method, and (7) Temperature method etc.

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In this paper, an intelligent control method for the **maximum** **power** **point** **tracking** (MPPT) of a photovoltaic **system** under variable temperature and insolation conditions is discussed. The MPPT controller for boost converter **based** on **fuzzy** **logic** (FLC) is developed and compared to conventional **tracking** algorithm (P&O). The different steps of the design of these controllers are presented together with its simulation. Results of this simulation show that the **system** with MPPT **using** **fuzzy** **logic** controller increase the efficiency of energy production from **PV**.

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To adapt a behavior means to alter it and to reach a new state; thus, an adaptive controller is a controller that its behavior in response to modifying in the dy- namics of the process can change [25]. The non-linear nature of solar cell **system** requires a kind of controller which not only works appropriately at the constant temperature conditions and irradiation, **based** on which the controller is designed, but also has an acceptable function at the variable temperature conditions, as well as the irradiation close to design conditions. However, if the condition variations are wide, the controller parameters proportionate to such variations should be updated. Adaptive **fuzzy** **logic** controller consists of two controllers, CFLC and decision-making. Figure 4 shows the **system** under control with the proposed AFLC.

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also it is known that P&O algorithm can be jumbled during those time intervals characterized by rapidly changing the environmental conditions. This paper it is shown that, to limit the negative effects related to above drawbacks, the P&O MPPT parameters must be modified to the dynamic behavior of specific converter adopted. A theoretical analysis permitting optimal choice of such parameters is carried out. [7]For large **Power** Generation **System**, probability for partially shaded condition to occur is high. Under Partially shaded condition(PSC), the P-V curve of **PV** **system** has multiple peaks, which reduces effectiveness of conventional **maximum** **power** **point** **tracking** methods. In this paper, particle swarm optimization (PSO) **based** MPPT algorithm for **PV** **system** operating under PSC is proposed. Standard version of PSO is modified to meet practical consideration of PGS operating under PSC. Problem formulation, design method and **parameter** setting method which takes hardware limitation into account are styled and explained in detail. The proposed method claims the advantages such as very easy to implement, **pv** **system** independent and has high **maximum** **power** **point** **tracking** efficiency. To confirm correctness of the proposed method simulation results, and experimental results of 500W **PV** **system** will be provided to demonstrate effectiveness of proposed technique.

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In this study, the characteristics of a **PV** module (Kyocera KD210GH) were mathematically modeled and simulated **using** MATLAB simulation tool. Then, the proposed MPPT algorithm and dc-dc boost converter were designed and developed in the same tool. Simulation results are presented to validate performance of the algorithm under different irradiation schemes, and to compare with the results obtained from conventional algorithm. Further experimental setup was carried out for comparative evaluation and the MPPT algorithm was implemented to performance verification of the algorithm by **using** digital signal processor (TMS320F28335).

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59 The NFC is initialized **using** the expert knowledge from the traditional **fuzzy** control, which reduces the burden of the lengthy pre-learning with a derived learning algorithm , the parameters in the NFC are updated adaptively by observing the **tracking** errors. A radial basis function neural network (RBFNN) is designed to provide the NFC with gradient information, which reduces the complexity of the neural **system** [5]. The Adaptive Neuro-**Fuzzy** Inference **System** (ANFIS) has recently attracted the attention of researchers in various scientific and engineering areas. The ANFIS is designed as a combination of the surgeon **fuzzy** model and neural network. The **fuzzy** **logic** controller (FLC) utilizes the ANFIS output voltage to track the MPP, to acquire high efficiency with low fluctuation [6]. The modeling of a photovoltaic **power** supply PVPS-**system** **using** an ANFIS, was presented in [7]. For the modeling of the PVPS-**system**, it is required to find suitable models for its different components (ANFIS-**PV**-array, ANFIS- battery and ANFIS-regulator) under variable climatic conditions. Test results provided that the ANFIS performed better than the neural networks. The results obtained from ANFIS can also be used for the prediction of the optimal configuration of **PV** systems, for the control of **PV** systems and for the prediction of the performance of the systems. Intelligent control technique **using** **fuzzy** **logic** control is associated to an MPPT controller in order to improve energy conversion efficiency and this **fuzzy** **logic** controller is then improved by **using** genetic algorithms (GA) [8].

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The years of research in solar **power** applications have reached to a greater extent in the field of **power** generation and utilization. Problems caused due to non-renewable resources are being explained for years which introduced the renewable energy resources **using** wind, solar, tidal, water as its major source of **power** generation. The influence of these renewable resources requires a higher **power** generation compared to the non-renewable resources. This is being a major problem for years, in comparison to the existing non-renewable energy units. In developed counties, the problem is solved by introducing the advanced control units for the renewable energy resources to compete the production capacity of the non-renewable energy resources. However the research and developments have a challenging environment in producing high **power** generation units **using** renewable energy. The use of the solar is widely seen in many applications. However the **power** conversion from the renewable energy is the challenging task. It is well known that the **power** extraction from the renewable energy is not possible for 100% extraction. This challenging task over years has developed various **power** electronics techniques to extract the **maximum** **power** from the renewable source. In solar, the solar panels require the **maximum** **power** **point** tracing **system** (MPPT) to obtain the **maximum** **power**. In conventional methods the MPPT is performed **using** the DC-DC converter through the controllers. This DC- DC converter is operated both in buck and boost operation to obtain the wide range of voltage values. There are various methods to obtaining the **maximum** **power** from the solar energy. Each method has its own significance. The solar energy form the solar **power** is converted to electrical form **using** the solar **PV** panel. The solar radiations are not the constant source so

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In this paper, the main component of the single-stage grid connected **PV** **system** is the three-phase voltage source inverter (VSI). Typically, simple inductors L are used as a filter interfacing inverter and mains, Fig 1 shows LCL filter provides advantages in costs and dynamics since smaller inductors can be used. However, in a grid-connected **system**, an LCL filter may cause resonance, which is a disaster for the systemβs stability [4]. Hence, control systems involving LCL filters are inevitably more complicated. The voltage-oriented control (VOC) method used for VSI employs an outer dc link voltage control loop and an inner current control loop to achieve fast dynamic response. The performance of the **power** flow depends largely on the quality of the applied current control strategy. In this paper, the current control has been implemented in a rotating synchronous reference frame d, q because the controller can eliminate a steady-state error and has fast transient response by decoupling control.

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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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