Abstract — Due to scarcity of fossil fuel and increasing demand of power supply, we are forced to utilize the renewable energy resources. Considering easy availability and vast potential, world has turned to solar photovoltaic energy to meet out its ever increasing energy demand. The mathematical modeling and simulation of the photovoltaicsystem is implemented in the MATLAB/Simulink environment and the same thing is tested and validated using Artificial Intelligent (AI) like ANFIS. This paper presents MaximumPowerPointTrackingControl for PhotovoltaicSystemUsingAdaptiveNeuro- Fuzzy “ANFIS”. The PV array has an optimum operating point to generate maximumpower at some particular point called maximumpowerpoint (MPP). To track this maximumpowerpoint and to draw maximumpower from PV arrays, MPPT controller is required in a stand-alone PV system. Due to the nonlinearity in the output characteristics of PV array, it is very much essential to track the MPPT of the PV array for varying maximumpowerpoint due to the insolation variation. In order to track the MPPT conventional controller like AdaptiveNeuro-Fuzzy “ANFIS” and fuzzy logic controller is proposed and simulated. The output of the controller, pulse generated from PWM can switch MOSFET to change the duty cycle of boost DC-DC converter. The result reveals that the maximumpowerpoint is tracked satisfactorily for varying insolation condition.
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 10Ω 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.
The power obtained from the PV system is affected by amount of irradiance falling on the panel, change in temperature and load condition. Therefore maximumpowerpointtrackingcontrol circuitry is used to track the peak point and ensures load matching between PV system and the converter. In this work PV array is simulated along with boost converter for a resistive load. Several MPPT techniques are implemented to get the best duty cycle so that output power obtained from the converter can be maximum. Investigation of the effectiveness of the proposed technique implemented in simulink using resistive load. The simulated result shows that the proposed technique have higher efficiency i.e. 98.88% at standard irradiance condition in comparison to other conventional methods like P and O, Incremental Conductance method, Fuzzy logic, ANN, combined fuzzy and InC , direct method. At various irradiance level also the proposed MPPT method shows better result in comparison to other methods. Hence this technique improves both dynamic and steady state responses of the Photovoltaicsystem.
ABSTRACT: At present, we are more concerned about the environmental issues caused by the consumption of conventional energy sources. That‟s why; we are focusing on the non-conventional energy sources like solar energy, wind energy, biomass energy, etc. to generate the required amount of electrical energy. Out of all the available energy resources solar energy is the most promising source of energy. But a photovoltaicsystem has two major drawbacks; the high installation cost and the low conversion efficiency of a PV system. To increase the efficiency of photovoltaic systems different types of MPPT techniques have been developed in past. In this paper we study and designed a PV system with incremental conductance (INC) based MPPT controller in SIMULINK. It also consists of a DC-DC converter. The implemented model extracts the maximumpower from PV system and makes it available to the load hence it enhances the efficiency of the system.
Because of the increasing of population growth, demand for electricity also increases rapidly. In fact, most power sources obtained today are from fossil fuels that are not renewable and limited. Then, we need renewable energy sources to overcome the crisis in future. Photovoltaic is one of the renewable energy resources in recent years. However, PV modules still have low efficiency due to the atmospheric conditions until right now although the earth receives huge energy from the sun. Therefore, a system that can control and gain maximumpowerpointtracking for the solar array is urgently needed .
According to the basic theory of fuzzy PID controller, the controlling simulation programmer is compiled by MATLAB software, at the same time the traditional PID controller is applied in controlling the same system. A chemical photovoltaicpowersystem is used as researching object, the corresponding parameters are listed as follows: short-circuit current I sc is equal to 4.88A, the open circuit voltage V oc is equal to 20.6V, the current of maximumpowerpoint I m is equal to 4.18A , the voltage of maximumpowerpoint V m is equal to 16.8V.
To extract the maximumpower from the solar PV module and transfer that power to the load, a MPPT system has been developed using Boost type DC-DC converter. A DC-DC boost converter transfers maximumpower from the solar PV system to the load and it acts as an interface between the load and the system. Maximumpower is transferred by varying the load impedance as seen by the source and matching it at the peak power of it when the duty cycle is changed. In order to maintain PV array’s operating at its MPP, different MPPT techniques are studied. In the literature many MPPT techniques are proposed such as, the P&O method, IncCon method, Fuzzy Logic Method etc (1,10,14) . Of these, the two most popular MPPT techniques P&O and IncCon methods are studied (15,16,17) . This paper presents a practical implementation of P&O and IncCon algorithms based on PIC18F452 microcontroller for tracking of the maximumpower generation from PV system under a rapid change in the radiation level. The proposed controlsystem algorithm obtains the Data from the PV system through microcontroller’s Analog and Digital ports to perform the pulse width modulation to the DC-DC boost converter. These techniques vary in many aspects as: simplicity, convergence speed, digital or analogical implementation, sensors required, cost, range of effectiveness, and in other aspects. Incremental conductance algorithm is used to track the MPP because it performs precise control under rapidly changing atmospheric conditions.
much as possible power from PV and various MPPT method is used to track MPP of PV. Fuzzy logic is a form of many-valued logic. It deals with reasoning that is approximate rather than fixed and exact. Fuzzy logic control based on operator experience is an ideal solution for applications where mathematical model is known or not precisely known especially for problems with varied parameters and nonlinear models . The fuzzy logic method cannot avoid the output vibration. So, MPPT method is necessary in order to improve the output efficiency of costly PV powersystem. Furthermore, the DC/DC circuit is used to track the actual MPP, which will consume partial electric power and an efficiency DC/DC circuit is important to track the MPP such as Buck, Boost, Buck-Boost and Sepic circuit have been used in MPPT of PV generating system.
generate the maximum voltage on its operating point while the current is depends on the load supply. The higher the load supply, the higher the current value would generated by the photovoltaic cell. There are several MPP trackingcontrol methods in the literature, such as fuzzy logic control, neural network control, pilot cells and digital signal processor based implementation. Nevertheless, Perturb and Observe (P and O) and Incremental Conductance (INC) algorithms are most widely used, especially for low-cost implementations 2013; Mahmoud, A.M.A., 2000; Marouani, R. and F. Bacha, 2009). As shown in the Fig. 3 and 4 in the previous part, the MPP changes as a consequence of the variation of the irradiance and temperature level. Therefore, it is necessary to ensure that the PV system always operates at the MPP under uniform irradiance in order to maximize the power harvesting the prevailing environmental conditions.
20 hardware complexity each approach has certain advantages and disadvantages for the present application. The MPPT using artificial neural network proposed can reduce the noises and oscillations generated by classical methods and can be competitive against other MPPT algorithms . Other researchers presented a method for the control of the PV system through the MPPT using a Fuzzy Logic controller. This method succeeded to reduce the PV array area and increase their output, and used for control of MPPT for stand-alone PV system giving a minimum economic cost. Developed controller can be improved by changing the form of the functions of memberships as well as the number of subsets  Intelligent control technique usingfuzzy logic control is associated with an MPPT controller in order to improve energy conversion efficiency and this fuzzy logic controller is then improved by using genetic algorithms (GA) and (PSO) .
4. 2. 3. Fuzzy Rules The phases of fuzzification and defuzzification are directly related. Acquiring numerical values, normalized by the input variables, are generated in a form of discrete control signals which will become the control variable. This relationship between the inputs ΔIpv, ΔVpv and the output ΔD is performed by fuzzy inference step. Figure 4 illustrates the connection between the input and the output of the controller. In the fuzzy inference of this project, for the composition of each control rule and the relationship between them, we used the MAX-MIN inference technique. The fuzzy method applied to the modeling of controllers was proposed by Mamdani. This method enabled preparing strictly linguistic rules. All developed control actions are inserted in the rules Table fuzzy controllers . This Table was constructed initially on the basis of suggestions and for typical response curves of a closed-loop system. They propose a controller with two input variables and one control variable, which are associated with five membership functions triangular for each variable. The fuzzy based rules used in this paper are shown in Table 1. Five linguistic variables have been used. Specifically, NB represents a “Negative Big” value, NM is “Negative Middle,” ZE is zero, PM is “Positive Middle,” and PB is “Positive Big”.
power from PV system is always achieved, the MPPT algorithm/controller is employed in conjunction with the power converter (dc-dc converter and/or inverter). To date, numerous MPPT algorithms have been reported in the literature; they are broadly classified into two categories, namely 1) the conventional and 2) soft computing methods.  and  have reviewed various techniques in both categories excellently. For conventional MPPT, the widely used methods include perturb and observe (P&O) , hill climbing (HC)  and incremental conductance (InCond) . Besides these, there are other simpler methods such as the fractional short circuit current , fractional open circuit voltage , ripple correlation control , sliding control  and mathematical-graphical approach . Under normal conditions, i.e. uniform irradiance, they are capable of tracking the MPP quite efficiently and exhibit very good convergence speed. Despite these advantages, each of these methods exhibit some serious drawbacks. These methods fail to track the MPP under varying environmental conditions and partial shading (when some part of the PV array experiences different irradiance than the other parts).
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 maximumpower 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 maximumpowerpoint of power - voltage curve of PV array. MaximumPowerPointTracking (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- . 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 maximumpowerpoint.
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 system’s stability . 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.
In this paper a new MPPT method, AdaptiveNeuroFuzzy Inference system (ANFIS) is used for tracking MPP, which is described in this section. The proposed Matlab/Simulink model of ANFIS based maximumpowerpointtracking controller is depicted in Fig.5. Irradiance level and operating temperature of PV module are taken as the input training data set for the ANFIS. The ANFIS reference model gives out the crisp value of maximum available power from the PV module at a speciﬁc temperature and irradiance level. At the same temperature and irradiance level, the actual output power from the PV module, is calculated using the multiplication algorithm of sensed operating voltage and current. Two powers are compared and the error is given to a proportional integral (PI) controller, to generate control signals. The control signal generated by the PI controller is given to the PWM generator. The PWM signal is generated using high frequency of carrier signal as compared to the control or modulating signal. The frequency of carrier signal used is 50 kHz. The generated PWM signals control the duty cycle of DC–DC converter, in order to adjust the operating point of the PV module.
The designed adaptivefuzzy proportional integral derivative control based MPPT strategy includes two stages control which an online PID gain (proportional gain Kp, integral constant Ki and derivative constant Kd) adjustment has been done using a fuzzy inference mechanism. The block diagram of the designed controller has been presented in fig. 7.
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
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:
Abstract. This paper presents a proposed method to search MaximumPowerPoint (MPP) based on the AdaptiveFuzzy 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.
In order to improve the efficiency of photovoltaic systems, MPPT control algorithms are used to optimize the power output of the systems. The essential considerations are the accuracy and convergence time. As the simulation result extensively discussed in Section 4, the statement that the proposed controlsystem, by coupling two control algorithms, optimizes the performance of the solution to the maximumpowerpointtracking is convincing. As reported in the literature, the duty cycle is obtained faster through the RCC method, which is in the unit of millisecond. In the MRAC controlsystem, the time constant of converging to the characteristic of the reference model is couple of seconds and it is longer, compared to the time constant of RCC. By tuning the adaptive gain in the adaptive law, the time constant can be modulated to guarantee the stability of the entire system. By simulating the system through different types of inputs, the proposed controlsystem is sufficient robust and stable to endure the small perturbance in the input and the measurements.