Page 1 of 8

Journal for Studies in Management and Planning

Available at http://edupediapublications.org/journals/index.php/JSMaP/

ISSN: 2395-0463

Volume 04 Issue 03

March 2018

Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 173

Fuzzy-Logic-Controller-Based SEPIC Converter for MPPT in

Standalone PV systems

Challa Venkatesh1,P. Raghava Rani 2,

1 M.Tech, Department of EEE, sai tirumala NVR engineering college, A.P, India

2.Assoc.professor, Department of EEE, sai tirumala NVR engineering college, A.P, India

Abstract - This paper presents 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 scheme uses the convergent distribution of the

membership function. The fuzzy controller for the SEPIC

MPPT scheme shows the voltage without any changes in

different load conditions at the inverter output (load) side.

The behaviour of the converter is tested in simulation at

different operating conditions. The load is fed from the

battery storage continuously with constant voltage. The

battery will be charge with the help of PV module and the

SEPIC converter, which is controlled by FLC-based MPPT.

The proposed FLC-based MPPT with battery will supply

more power to the load than the without battery system.

Key Words: SEPIC converter , fuzzy controller, photovoltaic(PV)

modules, battery ,inverter.

I.INTRODUCTION

The single-ended primary inductor converter (SEPIC) acts as a buck–boost dc–dc converter, where it changes its output

voltage according to its duty cycle.The selection of a proper dc–dc converter plays an important role for maximum power

point tracking (MPPT) operation. Due to its output gain flexibility. Among known converters, the SEPIC, conventional buck–

boost, and Cuk converters have the ability to step up and step down the input voltage. Hence, this converter can transfer

energy for all irradiation levels. Another desirable feature is continuous output current, which allows converter output

parallel connection, or conversion to a voltage source with minimal capacitance. The buck or boost converters are not

preferable, due to the lack of output voltage flexibility.

The SEPIC is chosen because the output voltage can be higher or lower than the input voltage. Also the input and output

voltages are dc isolated. The isolation is provided by the

series capacitor c, which blocks the dc from the supply side to the output side[1]. An auxiliary switch and a clamp capacitor

are connected. A coupled inductor and an auxiliary inductor are utilized to obtain ripple-free input current. The voltage

multiplier technique and active clamp technique are applied to the conventional SEPIC converter to increase the voltage

gain, reduce the voltage stresses of the power switches and diode. Moreover, by utilizing the resonance between the

resonant inductor and the capacitor in the voltage multiplier circuit, the zero-current-switching operation of the output

diode is achieved and its reverse-recovery loss is significantly reduced. Both the SEPIC and the Cuk converter provide the

choice to have either higher or lower output voltage compared to the input voltage. The MPPT algorithm represents

optimal load for PV array, producing opportune voltage for the load. SEPIC converters can have a low input current ripple,

which is one of the advantages of SEPIC converters. However, a bulk inductor should be used to minimize the current

ripple. Input current ripple becomes one of important requirements due to the wide use of low voltage sources such as

batteries, super capacitors, and fuel cells. The PV panel yields exponential curves for current and voltage, where the

maximum power occurs at the curve’s mutual knee. The applied MPPT uses a type of control and logic to look for the

knee, which in turn allows the SEPIC converter to extract the maximum power from the PV array. The tracking method

used, i.e., perturb and observe (P&O). A tracking method based on parabolic function is proposed to perform the

photovoltaic maximum power point tracking. With the proposed method, the maximum power calculation is made from a

parabolic convex function. Then a systematic scheme is developed to adjust the concavity and optimal region of the

approximate parabola for ensuring the iterative convergence of the proposed method. In order to confirm the effectiveness of

this proposed design, the approach has been applied to investigate different atmospheric scenarios. Among different

intelligent controllers, fuzzy logic is the simplest to integrate with the system. Recently, the fuzzy logic controller (FLC) has

Page 2 of 8

Journal for Studies in Management and Planning

Available at http://edupediapublications.org/journals/index.php/JSMaP/

ISSN: 2395-0463

Volume 04 Issue 03

March 2018

Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 174

received an increasing attention to researchers for converter control, motor drives, and other

process control because it provides better responses than other conventional controllers. The imprecision of the weather

variations that can be reflected by PV arrays can be addressed accurately using a fuzzy controller. In order to take the

advantages of the fuzzy logic algorithm, the MPPT algorithm is integrated with the FLC so that the overall control system

can always provide maximum power transfer from the PV array to the inverter side, in spite of the unpredictable weather

conditions.

II. PROPOSED SYSTEM

Fig:2 overall control scheme for the proposed FLC based MPPT scheme for the SEPIC converter with battery.

In this paper, the voltage level increases or decreases depending on the maximum power. Furthermore, the controller

changes the voltage level by changing the duty cycle of the pulsewidth-modulated (PWM) signal, which tracks the reference

signal. A sinusoidal reference signal is compared with the output signal to produce a supposedly zero error signal. Another

reference signal is used to compare the SEPIC’s output, to achieve the maximum power. This reference signal is adaptive,

changing its shape according to weather conditions.

Fig. 1. Circuit diagram of the SEPIC converter for the FLC-based MPPT with battery scheme.

Fig. 1 is the circuit diagram of the SEPIC dc–dc converter together with the MPPT and the fuzzy controller with battery. The

design of the fuzzy controller was done using Mamdani’s method for the converter. The maximum power point can be achieved

in case of a grid-connected system, a full-load condition, or using battery charging in case of a standalone system. However,

if the load need is lower than PV capacity, the PV voltage will move right in the PV curve, achieving the opportune power. This

case happens even if the batteries of the standalone system are full and the load is lower than PV power. In grid-connected

systems, the load is always there due to the huge number of clients. Therefore, the maximum power point can always be

achieved subject to the load need.

III. FLC ALGORITHM

In FLC design, one should identify the main control variables and determine the sets that describe the values of each

linguistic variable. The input variables of the FLC are the output voltage error e(n) and the change of this error e_(n). The

output of the FLC is the duty cycle of d(n) of the PWM signal, which regulates the output voltage.

Fig. 3. Unsymmetrical focused membership function of the proposed FLC:

(a) e(n), (b) e_(n), and (c) d(n).

Fig.3 show the membership functions of the inputs and the outputs of the SEPIC-side FLCs. The triangular membership

Page 3 of 8

Journal for Studies in Management and Planning

Available at http://edupediapublications.org/journals/index.php/JSMaP/

ISSN: 2395-0463

Volume 04 Issue 03

March 2018

Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 175

functions are used for the FLC for easier computation. A five- term fuzzy set, i.e., negative big (N-II), negative small (N-I),

zero (Z), positive small (P-I), and positive big (P-II), is defined to describe each linguistic variable.

TABLE-I

FUZZYRULE-BASED

The fuzzy rules of the proposed PV SEPIC dc–dc converter can be represented in

a symmetric form, as shown in Table I. Moreover, as in Fig.5, the membership

functions of the output variables are nine term fuzzy sets with classical

triangular shapes, i.e., negative very big (N4), negative big (N3), negative small

(N2), negative very small (N1), zero (Z), positive very small (P1), positive small

(P2), positive big (P3), and positive very big (P4). The Mamdani fuzzy inference

method is used for the proposed FLC, where the maximum of minimum

composition technique is used for the inference and the center-of-gravity

method is used for the defuzzification process. Fig. 3 illustrates a focused

membership function, where the sets go toward zero. the membership functions in Fig. 3 are guaranteed to produce the

stable output signal. The design of the focused membership function values depends on the nature of the signal.

Fig. 4. Three-dimensional surface corresponding to the memberships in Fig. 3 and the rules in Table I.

The control signal value is confined between −1 and 1,owing to the PWM carrier wave. The input signal values are between

−100 and 100 because of the error signal, which is resultant from the difference between the output signal and the desired

reference signal. In addition, most of error values are centered from −20 to 20. The sharpness of the control signal is very

essential for minimizing the error signal to zero in short time; wherefore, the pulse membership function is used to configure

the control signal fuzzy sets. The FLC performance changes with unsymmetrical distribution of membership functions, where

both convergent and divergent types of asymmetry will be considered with varying degrees of the unsymmetrical membership

functions.

IV. PROPOSED MPPT-BASED SEPIC CONVERTER

The fuzzy controller is applied to the SEPIC converter to mimic the new reference signal coming from the MPPT. The

new duty cycle δ(k) of the SEPIC converter switch was adjusted either by adding or by subtracting the previous duty cycle

δ(k−1) with the duty cycle’s perturbation step size. Equation (1) presents the relation between the present and previous

duty cycles, i.e.,

δ(k) = δ(k−1) Δδ (1)

e'/e N-II N-I Z P-I P-II

N-II Z4 Z4 Z4 Z3 Z

N-I Z4 Z2 Z1 Z3 P3

Z Z4 Z1 Z P1 P4

P-I Z3 Z P1 P2 P4

P-II Z P3 P3 P3 P4