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The thesis focus is to develop **Maximum** **Power** **Point** **Tracking** (MPPT) algorithm that **based** on the Simplified **Fuzzy** **Logic** Controller (SFLC). It is known that the output of the PV panel is always fluctuates due to the changes in the irradiation of the sunlight and surrounding temperature. This inconsistency of the output voltage is no good to the load. To compensate the problem, a **power** **converter** is used to reduce the fluctuation. However, the efficiency of the **power** **converter** is much depends on the **maximum** **power** **point** tracked of the solar panel. Therefore, there is a need for a MPPT algorithm to be implemented in the **power** **converter**. In this thesis, Simplified **Fuzzy** **Logic** Controller (SFLC) is employed. The proposed of Simplified **Fuzzy** **logic** Controller (SFLC) has several advantages as compared to the conventional **Fuzzy** **Logic** Controller (CFLC), such as less number of rules and tuning parameters. To verify, full system model is developed in MATLAB-Simulink software and simulated. From the results, it was shown that the features of the proposed Simplified **Fuzzy** **Logic** Controller (SFLC) are justified.

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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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Dr.K. Sekar, received the B.E., degree in Electrical and Electronics Engineering from Government College of Technology, Coimbatore and the Masters degree in **Power** Electronics and Industrial Drives from Sathiyabama Institute of Science and Technology, Chennai, Tamil Nadu, India. He has completed doctorate degree in Electrical Engineering from Anna University, Chennai. He has about 21 years of teaching experience. He is now Associate professor at Hindusthan College of Engineering and Technology, in Electrical and Electronics Engineering department Coimbatore. His areas of specializations are Soft computing techniques, Renewable Energy Technology with **Power** Electronics, DC-DC Converters, Image processing, Control engineering and Renewable Energy applications for industry and he has published 20 papers in International Journals and more than 14 papers in IEEE sponsored International Conferences / International and National conferences to his credit

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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 ΔD output, by determining the centroid of area composed which is the **fuzzy** output function.

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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 Boost **converter** circuit consists of inductor (L), diode (D), capacitor (C), and MOSFET switch (M). Inductors and diodes on the boost **converter** serve as current and voltage sources and to limit the ripple of the output current. Capacitor in the boost **converter** serves to limit the output voltage ripple. This boost **converter** circuit is used to raise the input voltage for 2 **Photovoltaic** to about 48 Volts. The way the circuit works on the incoming voltage conditions to the inductor then causes the rise of current **based** on time. And when the switch condition is off (M) then the inductor tip (L) is positive, the forward bias diode will charge the voltage on the capacitor and with the voltage across the capacitor greater than the input voltage then in the same time the inductor current will flow on the capacitor and load. When the switch conditions are on again, the voltage and current on the load will only be supplied by the capacitor. The Boost **Converter** circuit is shown in Figure-2.

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The voltage-**power** characteristic of a **photovoltaic** (PV) array is nonlinear and time varying because of the changes caused by the atmospheric conditions. The task of a **maximum** **power** **point** (MPP) **tracking** (MPPT) in a PV **power** system is to continuously tune the system so that it draws **maximum** **power** from the PV array. In recent years, the grid connected PV systems have become more popular because they do not need battery backups to ensure MPPT [1] . The two typical configurations of a grid-connected PV system are single or two stages. In two stages, the first is used to boost the PV array voltage and track the **maximum** **power**; the second allows the conversion of this **power** into high-quality ac voltage. The presence of several **power** stages undermines the overall efficiency, reliability, and compactness of the system besides increasing the cost [2] and [3]. The single stage has numerous advantages, such as simple topology, high efficiency, etc.

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Defuzzification of the inference engine evaluates the rules **based** on a set of control actions for a given **fuzzy** inputs set. This operation converts the inferred **fuzzy** control action into a numerical value at the output by forming the “union” of the outputs resulting from each rule. In other words, the deffuzification plays the role of a linguistic-to-numerical data **converter**. The center of area (COA) algorithm is used for defuzzification of output control parameter Iref. The duty cycle of the boost **converter** is adjusted thought Iref such that the system operates at the **maximum** **power** **point**. The coding of the membership functions for the otput Iref is identical to that of the input P: Negative Big (NB), Negative Medium (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Medium (PM) and Positive Big (PB). In Table 1 it is summarized the different **fuzzy** rules used in the **fuzzy** controller to track the **maximum** **power** **point**.

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The heart of MPPT hardware is a switch-mode DC–DC con- verter. It is widely used in DC **power** supplies and DC motor drives for the purpose of converting unregulated DC input into a controlled DC output at a desired voltage level ( Mohan 2003 ). MPPT uses the same **converter** for a different purpose: regulating the input voltage at the PV MPP and providing load matching for the **maximum** **power** transfer. The topologies of DC–DC converters are further categorized into three types: step down (buck), step up (boost), and step up & down (buck–boost). The buck topology is used for voltage step- down. In PV applications, the buck type **converter** is usually used for charging batteries and in LCB for water pumping sys- tems. The boost topology is used for stepping up the voltage. The grid-tied systems use a boost type **converter** to step up the output voltage to the utility level before the inverter stage.

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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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The solar energy conversion efficiency of a **photovoltaic** cell is around 20-21%. For **maximum** utilization of the energy under varying atmospheric conditions mppt trackers are employed in most PV applications. Some commonly used mppt techniques are discussed below:

Abstract— This paper presents a high performance **tracking** method for **maximum** **power** generated by **photovoltaic** (PV) systems. **Based** on adaptive Neuro-**Fuzzy** inference systems (ANFIS), this method combines the learning abilities of artificial neural networks and the ability of **fuzzy** **logic** to handle imprecise data. It is able to handle non-linear and time varying problems hence making it suitable for accurate **maximum** **power** **point** **tracking** (MPPT) to ensure PV systems work effectively. The performance of the proposed method is compared to that of a **fuzzy** **logic** **based** MPPT algorithm to demonstrate its effectiveness.

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The **power** extracted from a **photovoltaic** array and obtain unitary **power** factor in varying weather conditions. A desired array voltage is designed online using an MPPT searching algorithm to seek the unknown optimal array voltage. To track the designed trajectory, a tow back stepping controller are developed to modulate the duty cycle of the interleaved boost converters and buck inverter. The proposed controller is proven to yield global asymptotic stability with respect to the

IJEDR1604033 International Journal of Engineering Development and Research ( www.ijedr.org) 196 linear voltage output is eliminated. The switching losses are reduced as the **converter** operates only in boost condition. The figure 3 shows that interleaved boost **converter** for the MPPT **technique**. The circuit diagram consists of the Solar PV Panel connected to the proposed **converter**. The output of the panel is controlled by the proposed **converter**. The **converter** produces a constant boosted output for the load by eliminating the ripples. The stepped voltage can be used for DC and AC drives.

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Like in the fractional voltage method, k2 is not constant. It is found to be between 0.78 and 0.92. The accuracy of the method and **tracking** efficiency depends on the accuracy of K2 and periodic measurement of short circuit current. C.Perturb and Observe: In P&O method, the MPPT algorithm is **based** on the calculation of the PV output **power** and the **power** change by sampling both the PV current and voltage. The tracker operates by periodically incrementing or decrementing the solar array voltage. If a given perturbation leads to an increase (decrease) in the output **power** of the PV, then the subsequent perturbation is generated in the same (opposite) direction. So, the duty cycle of the dc chopper is changed and the process is repeated until the **maximum** **power** **point** has been reached. Actually, the system oscillates about the MPP. Reducing the perturbation step size can minimize the oscillation. However, small step size slows down the MPPT. To solve this problem, a variable perturbation size that gets smaller towards the MPP. However, the P&O method can fail under rapidly changing atmospheric conditions. Several research activities have been carried out to improve the traditional **Hill**-**climbing** and P&O

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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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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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For the past decades, many MPPT algorithms have been proposed, in which many centered on obtaining optimum **maximum** **power** **point**. Among the renowned **power** maximizing methods are perturb and observe (P&O), **Hill** **climbing**, incremental conductance (INC) and conventional PSO. These methods, nonetheless, fail to track the **maximum** **power** **point** when the irradiance level is not consistent for all PV solar cells or the panels are partially shaded. P&O method frequently leads to wrongful conclusion, oscillation around the **maximum** **power** **point** and it’s generally needs to link one or many modifications for general usage. Incremental conductance methods overcome these shortfalls of Perturb and Observe methods but need relatively elaborate detection devices and the choice of the step and threshold is also distressing.

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In In the last few decades, **photovoltaic** (PV) energy has been the subject of several research projects. It has been considered as a clean form of energy because it does not emit toxic gases into the atmosphere. It is well known that the energy extracted from a PV array panel depends on the operating **point**. In order to increase the output **power** of a PV energy system, it is crucial to force the PV array panel to work at the **maximum** **power** **point** (MPP). In this case, a DC-DC **converter** must be used to link the PV array panel to the load. However, the **maximum** **power** produced by the PV array varies with

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