Tujuan fokus tesis ini adalah membangunkan algoritma penjejakan takat kuasa maksimum berdasarkan konsep kaedah dipermudahkan Pengawal Logik Kabur (SFLC) aplikasi. Dikenalpasti bahawa hasil keluaran panel **PV** sentiasa berubah-ubah kerana berdasarkan kepada perubahan penyinaran cahaya matahari dan suhu persekitarannya. Ketidakan konsisten perubahan kerap berterusan adalah tidak sesuai untuk beban pada keluaran. Untuk mengatasi masalah ini, maka memperkenalkan litar pernukar kuasa bagi mengurangkan kekerapan perubah-ubahan berlaku. Bagi kecekapan untuk litar pernukar kuasa adalah bergantung kepada penjejak takat kuasa maksimum pada panel **PV** tersebut. Oleh itu, algoritma penjejakan takat kuasa maksimum perlu ada dalam melaksanakan dalam litar penukar kuasa. Dalam tesis ini, dipermudahkan pengawal logik kabur (SFLC) diperkenalkan. Tujuan kaedah dipermudahkan pengawal logik kabur (SFLC) mempunyai banyak kebaikan berbandingkan kepada kaedah konvensional pengawal logik kabur (CFLC), seperti mengurangkan jumlah langkah peraturan logik dan penalaan parameter. Bagi kenalpastikannya, semua model keseluruhan dibangunkan di dalam MATLAB- simulink perisian dan disimulasikannya. Daripada keseluruhan keputusan, ini menunjukkan tujuan ciri-ciri dipermudahkan pengawal logik kabur (SFLC) telah dijustifikasikan.

Show more
25 Read more

Online setting of **fuzzy** controller parameters is a timely and complex process. In this state, the time assigned for setting controller parameters must be less than the time of temperature and radiation intensity change. Otherwise, **tracking** will not be proper and the response obtained in this state may even be worse than the **fuzzy** method. In order to prevent this situation, we have conducted the CFLC parameter setting in a dier- ent manner. In this state, we have added a second **fuzzy** controller, namely \decision-making", to the system. The decision-making controller is a **fuzzy** controller, which constitutes temperature and radiation intensity as inputs. The output of this controller changes the primary **fuzzy** controller parameters proportional with these instantaneous changes in order to obtain better responses compared to the **fuzzy** method. Simula- tions have been carried out using MATLAB/Simulink software. By comparing the proposed method with the **fuzzy** method, in simultaneous temperature and radiation change conditions, simulation results show that the proposed method follows the **maximum** **point** of **power** with better speed and precision.

Show more
10 Read more

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.

Show more
26 Read more

For example if a **PV** array is subjected to two different irradiance levels, then the modules that get high radiance(HS) level are known as insolated modules and the modules that get very low irradiance(LS) level are known as shaded modules. Generally the insolated modules generate the current in the string. The string current generated in the insolated modules is greater than the current generated in shaded modules. This current passes through the parallel resistance of the shaded modules and generates a negative voltage across them. Hence the shaded modules consume energy instead of generating energy which leads to the drop of the overall efficiency of the string and the development of the hot spots around the shaded modules which may get damaged due to these hot spots. To overcome this problem a bypass diode is connected in parallel to each module so that it will carry the extra current of the

Show more
10 Read more

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.

Show more
10 Read more

The key limitation to the extensive spread usage of **PV** system is the low efficiency of the **PV** module due to Variations in ambient conditions (irradiation) [4]. The current –voltage (I- V) or **power** –voltage (P-V) curve of a photovoltaic system describes the characteristic of the **PV** module for a set of temperature and irradiance. An operating **point** on the P-V curve matches to a typical **power** that is produced and delivered to the rest of the **PV** systems and finally the load. It is therefore clearly beneficial that a solar module operates at **maximum** **power**. Without any form of external electrical manipulations, the **PV** module’s operating **point** is mostly dictated by the electrical load seen at its output. To get

Show more
Firstly, a 50 W **PV** is used to generate **power** and placed in the shade so that the temperature of the **PV** is not too high hence MPP can be obtained as much as possible. Several tests using computer simulation were performed at irradiation 1000 W/m 2 and temperature 25°C. Then, an experiment was implemented for verifying the proposed MPPT algorithm. **PV** connected to MPPT device with a load at 12 V / 45 Ah battery. The test was conducted at 12:00 AM. The solar irradiation was measured using a pyranometer at 1000 W/m 2 with a temperature of 25°C. Onsite setting can be seen in Figure 9.

Show more
Currently the photovoltaic solar energy is considered as one of the most promising renewable energy sources because of its high availability anywhere in the world and the absence of contaminating effects. Many cells are grouped in one module (or panel), and many modules form a photovoltaic (**PV**) generator. A **PV** module has nonlinear steady-state characteristics expressed as either current versus voltage (the I-V curve), or as **power** versus voltage (the P-V curve). The I-V and P-V curves of a **PV** system vary with the solar insulation (irradiance) and cell temperature. The MPP is defined as the **maximum** **power** where the **power** drawn from the **PV** cell is high. The value of the **maximum** **power** (PMPP) is obtained by multiplying the voltage at the **maximum** **power** **point** (VMPP) by the current at that **point** (IMPP) [1].

Show more
In this work, five subsets for each input and twenty five rules have been used. **Based** on the results of Simulink model, tuning the rules is performed to design the **fuzzy** **logic** controller. The proposed **fuzzy** rules of the system are shown in Table 1. Values of **fuzzy** controller inputs are compared with twenty-five rules of the system and are implicated with the membership functions. The implication has been chosen to be “and” operator. The rules were implicating by taking the minimum value of the membership function of the inputs for all the truth rules. The implicated rules were aggregated using **maximum** method. Figure 8 shows the surface view for the relation between **fuzzy** inputs and output. The simulated model needs a crisp value from the **fuzzy** controller, thus a defuzzification of the output membership function after aggregation is a must. Centroid method has been selected for defuzzification by calculating centre of mass of the aggregated membership function. The centroid method is one of the most physically appealing and prevalent method of all defuzzification methods.The crisp value represents the duty cycle of the switching signal that triggers the IGBT in the boost converter. This pro- cess is usually called Center of Gravity or Center of Area. The final membership function after aggregation is shown in Figure 9, while Z* is the value of the centroid and can be calculated as [20].

Show more
14 Read more

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.

Show more
10 Read more

In this paper, an intelligent control strategy **based** on the Takagi-Sugeno type **fuzzy** system has been proposed for the MPPT of a **PV** energy system. The **PV** system was described by four local models to compute the coordinates of the optimal operating **power** **point**. The trapezoidal type membership functions have been used to compute the weight of each local model. The simulation results show that the **Fuzzy** algorithm can track the MPP quickly and steadily exhibits good robustness despite sudden variations of temperature and irradiation. It is worth noting that there are no oscillations in the various figures compared with traditional algorithms.

Show more
A seven-term **fuzzy** sets, Negative big (NB), Negative medium (NM), Negative small (NS), zero (ZZ), Positive small (PS), Positive medium (PM), and Positive big (PB) are defined to describe each linguistic variable term. Each linguistic term associated with a set linguistic variable has a degree of membership that ranges from zero to one both inclusive as shown in table 1.

ABSTRACT: The shading due to clouds, buildings, tree’s etc. may affect the performance of a photovoltaic array. Including this variation in temperature, solar separation is some more factors which affect the performance. The complete or partial shadowing of **PV** array tremendously affects its performance. In case of large **PV** array systems installed for distributed **power** generation schemes, the situation may get more complex under partially shaded condition. The multiple peaks generated due the shadowing affect may make **PV** characteristics more complex. It is significant to understand the **maximum** peak possibilities in order to extract the **maximum** possible **power**. This paper is an attempt to describe the MATLAB-**based** modeling and simulation scheme suitable for studying the I–V and P–V characteristics of a **PV** array under a non-uniform isolation due to partial shading. The present paper will explain a comparative study and give a glimpse on different method for **maximum** **power** **point** **tracking** system.

Show more
that allows the **PV** cells to produce all the **power** they are capable of. It is not a mechanical **tracking** system which moves physically the modules to make them **point** more directly at the sun. Since MPPT is a fully electronic system, it varies the module’s operating **point** so that the modules will be able to deliver [5] **maximum** available **power**. As the outputs of **PV** system are dependent on the temperature, irradiation, and the load characteristics MPPT alone cannot deliver the output voltage perfectly. For this reason MPPT is required to be implementing in the **PV** system to maximize the **PV** array output **power**. **Maximum** **power** **point** tracker (or MPPT) is a high efficiency DC to DC converter that presents an optimal electrical load to a solar panel or array and produces a voltage suitable for the load. **PV** cells have a single operating **point** where the values of the current (I) and Voltage (V) of the cell result in a **maximum** **power** output. These values correspond to a particular load Resistance which is equal to V/I as specified by Ohm's Law. A **PV** cell has an exponential relationship between current and voltage, where as the resistance is equal to the negative of the differential resistance (V/I = -dV/dI). **Maximum** **power** **point** trackers utilize some type of control circuit or **logic** to search for this **point** and thus to allow the converter circuit to extract the **maximum** **power** available from a cell. In the **power** versus voltage curve of a **PV** module there exists a single maxima of **power**, i.e. there exists a peak **power** corresponding to a particular voltage and current.

Show more
11 Read more

Several MPPT techniques have been discussed in this paper. From this, it is clear that it can be very difficult to choose the best; each MPPT method has its own advantages and disadvantages and the choice is highly applica- tion dependent. For example, solar vehicles require fast convergence to the MPP; in this case good options are **fuzzy** **logic** control, and neural network. In orbital stations and space satellites, which involve large cost, the performance and reliability of the MPPT are most important. The tracker must be able to continuously track the true MPP in the minimum amount of time and should not require periodic tuning. In this case, the appropriate methods are O & P/Hill-climbing and IC [5]. When using solar panels in residential locations, the objective is to reduce the payback time. To do so, it is necessary to constantly and quickly track the **maximum** **power** **point**. Furthermore, the MPPT should be capable of minimising the ripple around the MPP. Therefore, the two stage IC and optimised P & O methods are suitable.

Show more
15 Read more

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.

Show more
67 Read more

controller is an intelligent way of **tracking** the **maximum** **power** **point** (MPP). **Fuzzy** **Logic** (FL) has been used for **tracking** the MPP of **PV** modules because it has the advantages of being robust, relatively simple to design and does not require the knowledge of an exact model [3]. Artificial Neural Network (ANN) is an artificial network that mimics the human biological neural networks behaviour. The primary significance of the neural network is the ability of the network to learn from its environments and to improve its performance through learning [5]. **PV** array current and voltage are the two inputs given to ANN and it computes an optimized duty cycle to track **maximum** **power** **point** . In this paper, using MATLAB /SIMULINK a **PV** array model is used to simulate actual **PV** arrays behaviour and then a **Maximum** **Power** **Point** **tracking** method using **Fuzzy** **logic**, ANN is proposed in order to control the DC-DC converter. DC-DC converter is followed by voltage source inverter. VSC is controlled in the rotating dq frame to inject a controllable three phase AC current into the Utility grid to achieve unity **power** factor operation, current is injected in phase with the grid voltage[12][13]. A phase locked loop (PLL) is used to lock on the grid frequency and provide a stable reference synchronization signal for the inverter control system [13]. A grid-connected complete photovoltaic model is generated to simulate the actual life case.

Show more
10 Read more

The algorithm of a battery charge controller determines the effectiveness of battery charging as well as the **PV** array utilization, and ultimately the ability of the system to meet the electrical load demands. The most common approaches for charge controllers are the shunt, series, pulse width modulation (PWM) and MPPT charge controllers. The shunt regulator controls the charging of a battery from the **PV** array by short -circuiting the array internal to the controller. The series controller utilizes some type of control element connected in series between the array and the battery. While this type of controller is commonly used in small **PV** systems, it is also a practical choice for larger systems due to the current limitations of shunt controllers. The MPPT battery charge controller incorporates a DC-DC converter such that the **PV** array can operate at the **maximum** **power** **point** at the prevailing solar irradiance. The structure of battery charge controllers depends on the type of the controller. In the series and shunt controllers, it simply consists of a switching element, such as a relay that is switched on/off **based** on the value of a predefined set **point**. In a PWM and MPPT control lers, the circuits are more sophisticated. In PWM generator circuits, microcontrollers are needed in order to drive the switches of a DC–DC converter while MPPT controller consists of a controller that manages the **maximum** **power** **point** **tracking** process and DC-DC converter [8]. In this paper, a DC-DC SEPIC converter is selected and has been employed for standalone **PV** system application. Using this converter, the **PV** system is able to execute good MPPT and charging control performance.

Show more
14 Read more