Design and Implemented Buck-Boost Converter Based Fuzzy Logic …ijens.org/Vol_20_I_01/203301-7575-IJMME-IJENS.pdf · 2020-03-03 · control of power converter for battery charger

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:01 115

203301-7575-IJMME-IJENS © February 2020 IJENS I J E N S

Design and Implemented Buck-Boost

Converter Based Fuzzy Logic Control on Wind

Power Plant R. D. Noriyati1), Ali Musyafa2), A. Rahmadiansah3), A. S. Utama4), M. K. Asy’ari5), M. Abdillah6)

Abstract— Nowadays, the need for energy in Indonesia increases

related to population growth rapidly where it leads to degrading

the reliability of the power system operation. Meanwhile,

conventional energy reserves such as oil, coal, and others are

running low. Therefore, the usage of alternative energy that

categorized as green technology and promising energy in the

future is a must. One of the most potentials of those energy

technologies is a wind power generation. Generator, a component

of wind power plant, is having the function to convert mechanical

energy into electrical energy. Due to the intermittent of wind

power generator output affected by the weather circumstances, a

converter device namely a buck-boost converter was utilized as

control of the wind generator to increase or decrease the voltage

depending on the desired voltage on the system. To enhance the

performance of the buck-boost converter, intelligent control namely fuzzy logic controller (FLC) was employed as feedback

control of the converter. Here, error and delta error signals of

the converter were utilized as the input data of FLC while the

duty cycle of the converter was employed as output data of FLC.

In this research work, the proposed control model has designed

to work for blade rotation within the range of 192-364 rpm. From

the simulation results, the performance of the converter

controlled by FLC had obtained the time delay of 0.037 s, found

the time up of 0.159 s, produced the peak time on 0.354 s,

attained the overshoot of 1.07 %, and reached the time for

steady-state on 0.164 s. Meanwhile, when the proposed control

model had connected to the hardware, it had resulted on 2.6 s for

the delay time, produced the uptime of 3.5 s, found the peak time

of 4.9 s, achieved the time for steady-state on 3.2s and generated

the steady-state error of 1.92 %.

Index Term-- Blade, pitch angle, control system, wind turbine, neuro-fuzzy.

_____________________________________________ This work was supported in part by Institute for Research and Community

Service (LPPM ITS), Institut Teknologi Sepuluh Nopember, Surabaya. with

supporting financing the research. Also, the authors would like to thank the

anonymous referees for their comments on the eelier version of this work.

A.Musyafai, Department of Engineering Physics, Faculty of Industrial

Technology and System Engineering,, Institut Teknologi Sepuluh Nopember

Surabaya, 60111,Indonesia, (corresponding author to provide

phone:+62087851240830;fax:+62-031-5923625; e-mail: [email protected] ; R.D.Noriyati, A.Rahmadiansyah, A.S.Utama, M.K. Asy’ari. He is now with

the Department of Enginering Physics, Faculty of Industrial Technology,

Institut Teknologi Sepuluh Nopember Surabaya, E-mail: [email protected],

[email protected]; [email protected] ; [email protected],

and M.Abdillah, as lecturer at Department of Electrical Engineering,

Universitas Pertamina, Jakarta, Indonesia. As author and co-author, he had

published 68 scientific papers in different journals and conferences. He was a

member of IEEJ, IAENG and IEEE. His research interests are power system

operation and control, power system optimization, robust power system

security, power system stability, intelligent control and system, and artificial

intelligences (optimization, machine learning, deep learning).E-mail:

[email protected].. _______________________________________________________________

I. INTRODUCTION Indonesia is the largest archipelago in the world

consisting of more than 17.000 islands and its population

increases each year where a total population in 2013 is around

246.757 million people and it was estimated at 269.54 million

people in 2019. Therefore, the need of energy for running the daily activities of human beings as primer energy is

proportional to the growth of the population annually in this

country. Unfortunately, this energy demand is inversely proportional to the availability of energy sources in Indonesia.

Reserves of energy sources in Indonesia are running low

where the use of coal at the latest 75 years will be exhausted.

In addition, gas energy resources can be utilized for up to 33 years and the fuel energy based on fossil energy is only

employed up to 12 years ahead if no other reserve energies

are found to replace fossil energy [1].

Moreover, due to the location of Indonesia lies in the tropical areas,

it gives many advantages where almost all areas in this country

receive direct solar light and wind over the years. To date, this

country is also endowed with rich natural resources including water,

solar light, wind, geothermal and other natural resources that can be

utilized as renewable energy (RE) sources [2]. The need and

necessity of adequate energy harvesting from natural resources for

multiple goals including economic, social, and cultural development

have been revealed by most of the researchers in the global sector for

decades. Along with the merit of wind possessed by almost all areas

in Indonesia, it had encouraged many researchers to develop an

advanced RE technology as a solution to energy problems [3] by

harvesting energy from wind, which is naturally replenished.

One of the aforementioned RE technologies utilized

worldwide is a wind power plant that caught the wind by its

blades as an energy source for rotating its turbine which then

converts into electrical energy [4]. A small-scale wind turbine is

appropriate to overcome seriously energy crisis for residential

areas especially in remote regions or isolated island areas. Due to

the wind generation unit depends on the weather condition, the

output of the wind turbine fluctuates which can damage the

component of electrical loads [5]. Therefore, the power

electronic component needs to install in order to stabilize the

output of the wind turbines. In this study, a buck-boost converter,

kind of power electronic component, is employed to stabilize the

output of wind turbines.

mailto:[email protected]






The chosen of this converter to overcome the problem of

wind turbine output is due to this converter can decrease or increase its output regarding the required input voltage or

current or power of electrical loads on the consumer side [6]. The pulse with modulation (PWM) is employed to regulate the

duty cycle as a trigger for the electrical switch of the converter. This switch has a function to regulate the inflow into the

inductor where the average value of the load voltage is proportional to the ratio between the opening and closing times

of the switch. This switch affects the converter’s output voltage

[7]. Several approaches have been reported to overcome the

stability issue of wind turbine output based on intelligent control such as advanced control design the pitch angle on a

blade of wind turbine using fuzzy logic controller [8]-[11], back propagation neural network (BPNN) with super capacitor

energy storage system (SCESS) to stabilize the wind turbine based on double-fed induction generators (DFIG) [12], and

optimized virtual inertia for controlling rotor angle stability of

wind turbine [13]. Furthermore, the prototype of a wind turbine and its

controller have been developed by some researchers such as

control of power converter for battery charger in wind turbine system [14], and PID controller optimized by PSO for

controlling the brake system on small-scale wind turbine prototype [15].

This paper proposes a fuzzy logic control (FLC) for

controlling the DC-DC buck-boost converter to stabilize the output of small-scale wind turbine generation units. The

comparison of simulation results and hardware implementation is provided in this paper.

The paper is organized as follows. Section II provides the material and method, Section III presents a brief overview of

research method. Section IV discusses the simulation results and some discussions, and Section V some conclusion given

from the simulation result.

II. MATERIAL AND METHOD

In this section, a brief overview of wind turbine prototype, prototype of buck-boost converter, hardware design of buck-boost converter, fuzzy logic controller (FLC), and design of

generator is provided. A. Prototype of Wind Turbine

A wind turbine is a device used to capture wind kinetic

energy before it is converted into electrical energy. Wind that passes through the area of the wind turbine blades will rotate

the wind turbine. The wind turbine input power equation can be written according to equation (2.1).

P 1 R2V

3 (2.1)

wt 2

Pwt is the input power of the wind turbine, in the form of

mechanical power (Watt), R is the diameter of the turbine (m), ρ is the air density (kg/m

3), and V is the air velocity (m/s).

There are two parameters needed to determine the efficiency

of a wind turbine. The first parameter is the power coefficient

(Cp) which is the ratio between the turbine output power (Pm)

and the turbine input power (Pwt). Mathematically, the Cp value can be written according to equation 2.2.

6

5

432

1 )( ceccc

cC i

c

ip

(2.2)

Cp is the power coefficient, β is the turbine blade angle, c1-

c6 is the turbine coefficient.

The mechanical output power generated by wind turbines

can be calculated using equation 2.3.

32

2

1mpm VCRP (2.3)

The prototype of a wind turbine constructed in this research work is a horizontal wind turbine that uses the upwind type rotor in which three wind turbine blades are utilized and made of glass fiber. The specification of this wind turbine prototype is listed in Table I and the wind turbine prototype is shown in Fig 1.

Fig. 1. Wind turbine prototype

TABLE I SPECIFICATIONS OF WIND TURBINE PROTOTYPE [1]

No. Component Parameter Specification

1. Componen Wind Turbine 3 unit

Blade

Length 110 cm

Weight 990 gr.

2. Blade Specification NREL S835(part of base 25

cm), S833(part of middle 35

cm), S834 (part of end 40 cm)

Material Glass Fiber

3. Rotor shaft Material Stainless Steel

Dimension D=1.5 cm L=30 cm

4. Support of Material PVC

Blade Diameter 28 cm

5. Main plater Material Iron Plate

Dimension D=40 cm, Thickness=0.2 cm

6. Support Base Material Iron Plate

Dimension 8 mm B. Prototype of Buck-Boost Converter

The development of this prototype model is conducted in two stages. First, we design a hardware including the

electronic circuit of a buck-boost converter, LCD, and opt coupler. Second, we use Mat lab software and Arduino IDE

program packages to design the system prototype model. In this study, the IRFZ44N type of power MOSFET is utilized as

a component of a buck-boost converter due to it has a better frequency switch than other switch electronic components.

The inductor and capacitor equation is shown in equation (2.4)-(2.8).



D Vo Vs (2.4)

1 D

L min RL max 1 Dmin 2

(2.5)

2 fs

C min

VoDmax

(2.6) VoRL min fs

L diL

Vs D (1 D) Vo iL RL (2.7)

dt

C dVc

(1 D) iL io (2.8) dt

Vo is the output voltage, Vs is the source voltage (input), D is

the duty cycle, L is the value of the inductor, C is the value of

the capacitor, R is the value of the resistor, fs is the switching

frequency, and i is the current.

TABLE II THE PARAMETERS OF BUCK-BOOST CONVERTER

No. Parameter Value

1 Input Voltage 2–17

2 Output Voltage 13 V

3 Current Input 2 A

4 Switching Frequency 28000 Hz

TABLE III THE PARAMETERS FOR HARDWARE MODEL OF BUCK-BOOST CONVERTER

No. Parameter Value

1 Duty cycle dmin = 0.866, dmax = 0.433

2 Load 6.5

3 V0 0.013

4 Inductor

Lmin = 20.833 H

Lmax = 388 H

5 Capacitor

Cmin = 4761F

Cmax = 2830.95F

Moreover, other electronic components such as inductor, resistor, and capacitor are also employed to construct the

hardware of buck-boost converter. The appropriate parameter of these converter components must be determined at first to

obtain the desired buck-boost converter output. The range of input voltage for this converter is 2-17 V and the desired

converter output is 13 V. The parameters of this converter are listed in Table II. Then, the parameters for hardware of buck-

boost converter are defined in Table III. After calculating the parameter values of the buck-boost

converter from Eqs. (2.4)-(2.8), these parameters are employed to design the hardware model of converter as depicted in Fig.2.

Fig. 2. The hardware model of buck-boost converter

C. Fuzzy Logic Controller (FLC) Model for Buck-Boost

Converter

The wind caught by the turbine blades triggers the turbine being spinning where the turbine shaft connected to the generator is rotating and that generator converts the mechanical energy into electrical energy. In this study, the system design works by first detecting the output voltage of generator. The buck-boost converter will alleviate or increase

the input voltage (Vin) of generator to result in the desired output voltage of generator due to this output voltage is fluctuating which depends on the wind speed affected by the weather circumstance. The micro-controller will send a command to open or close the switch of converter by setting of its duty cycle where generated by the PWM signal. The need for a good of PWM signal form can be obtained by using intelligent control namely a fuzzy logic controller (FLC). The usage of FLC to control the converter in this research work due to the FLC does not require a system mathematical model and its performance follows a series of simple rules. This proposed controller determines the PWM signal that provided to the MOSFET gate for setting the duty cycle of converter. The error of output voltage in this study is obtained by comparing the reference voltage to the generator output voltage measured by the sensor. Error e(t) and the change of error or delta error de(t) of output voltage as described in Eqs. (1)-(2) are employed as input FLC, while duty cycle (d) of the converter is computed by FLC based on the fuzzy rules and the error signal data.

e(t) = Vref - V0

de(t) = e(t) - e(t-1)

After computing for the required duty cycle, the system

model will initiate whether to buck or boost the input voltage of generator. This device will compare the output voltage with

the desired output voltage. The block diagram of the proposed FLC is illustrated in Fig. 3 where the FLC is developed by

using MATLAB software environment and is implemented on a micro-controller.

Fig. 3. FLC scheme for controlling buck-boost converter

Commonly, the basic type of FLC is classified into Takagi-Sugeno (TS) and Mamdani [16]. The difference between them

is in rule consequent. In this study, Takagi-Sugeno (TS)-FLC type is employed due to it can produce an infinite number of

gain variation characteristics.

Fig. 4. FLC model



Moreover, The FLC based on TS technique has more and

better solutions to a wide variety of non-linear control problems. A general description of FLC is shown in Fig. 4, where the basic structure of FLC is defined as follows. Input Fuzzifications

In this part, the process of converting a crisp value of input

FLC to a fuzzy value that is carried out using the information

in the knowledge base is conducted. This input FLC is derived from the obtained data from voltage sensor which processed by

micro-controller. The membership function (MF) of input FLC

named e and de in Eqs (1)-2 is divided into five membership

functions in triangular form as depicted in Fig. 5 (a)-(b). The MF value of e is set in the range [-4, 4], while the MF value for

de lies within [-0.8, 0.8]. The membership functions regarding

to the error (e), the change of error (de), and duty cycle (d) are represented by [Negative Big (NB), Negative Small (NS), Zero

(Z), Positive Small (PS), Positive Big (PB)].

(a) e

(b) de

Fig. 5. Fuzzy MFs of error (e) and delta error (de)

Rule Bases

The rule base of FLC employed in this research work is

provided in Table IV. Due to the rules are very important influencing the performance of the FLC, therefore these rules

in this study are examined extensively by observing the behavior of the converter output. The fuzzy rules utilized for

this control scheme has 25 rules.

TABLE IV

THE RULE BASE OF FLC de e

e PB PS Z NS NB

PB PB PB PS Z NS

de PS PB PS PS NS NS

Z PB PS Z NS NB

NS PS PS Z NB NB

NB Z NS NB NB

For example, each rule from Table IV can be defined as

follows Rule 1: if e is PB and de is PB then duty cycle (d) is PB

Rule 2: if e is PB and de is PS then duty cycle (d) is PB

Rule 25: if e is NB and de is NB then duty cycle (d) is NB To identify whether the rules are correct or incorrect, it can

be seen in the rule viewer process as shown in Fig 6. Here, the rule viewer exhibits the action performed by FLC.

Fig. 6. Rule viewer of FLC

Outputs defuzzificatin In this step , the fuzzy information produced by the inference

mechanism is convered into crips values. The center for area

(CoA) approach is selected for defuzzification process due to

Takagi Sugeno FLC is applied to control the buck-boost

converter.

D. Modeling of Generator

Modeling the generator in MATLAB software as depicted

in Fig. 7 plays a key role before it implemented into the hardware model due to the characteristics of the generator

including a relationship between generator input represented in rpm and generator output expressed in voltage variable

must be known in order to find out and estimate the behavior of the generator output. The characteristics of the generator

are shown in Table V.

Fig. 7. Generator model using MATLAB/SIMULINK



TABLE V

THE CHARACTERISTICS OF GENERATOR

No. Parameter Value

1. Armature Resistance 7 Ω

2. Inductance 0.00051 H

3. Torque 0.609 N.m/A

E. Modeling of Buck-Boost Converter

Due to each electronic component of the buck-boost converter has its own characteristics, the modeling of that converter in MATLAB software is required in order to analyze its output behavior before implemented into the hardware model. The buck-boost converter model can be seen in Fig. 8.

Fig. 8. Buck-boost converter model in MATLAB/SIMULINK

F. Combination of Buck-Boost Converter and Generator

Models After knowing the behavior output of the generator and

converter from the previous model that is constructed using MATLAB/SIMULINK, then, both models are concatenated being one model as shown in Fig. 9 in order to determine the relationship between the behavior of generator and converter outputs. The RPM generated by wind speed affects the output voltage of the generator which will be controlled by the converter.

Fig. 9. Buck-boost converter and generator models using

MATLAB/SIMULINK

Ω, 27Ω, 39Ω, and 47Ω for all RPM conditions. The obtained results of RPM on 240, 270, 300 are shown in the Tables VI-VIII, respectively.

TABLE VI THE OUTPUT OF CONVERTER USING FLC AT 240 RPM

R(Ω)

Input of converter Output of converter

V (V) I (A) P (W) V (V) I (A) P (W)

10 3.68 1.65 6.1 7.78 0.77 6.06

27 10.87 0.63 6.88 12.84 0.47 6.10

39 12.47 0.40 5.02 13.02 0.33 4.34

47 13.04 0.32 4.21 13.03 0.27 3.61

TABLE VII THE OUTPUT OF CONVERTER USING FLC AT 270 RPM

R(Ω)

Input of converter Output of converter

V (V) I (A) P(W) V (V) I (A) P (W)

10 3.79 1.91 7.27 7.78 0.77 6.05

27 13.56 0.52 7.07 13.03 0.48 6.28

39 14.81 0.34 5.08 13.08 0.33 4.39

47 15.22 0.28 4.33 13.09 0.27 3.64

TABLE VIII THE OUTPUT OF CONVERTER USING FLC AT 300 RPM

R(Ω) Input of converter Output of converter

V (V) I (A) P(W) V (V) I (A) P (W)

10 4.21 2.19 8.96 8.67 0.86 7.53

27 15.9 0.45 7.3 13.16 0.48 6.41

39 16.95 0.31 5.26 13.16 0.33 4.46

47 17.34 0.25 4.42 13.17 0.28 3.70 It could be concluded from Tables VI-VIII that the output

voltage of the converter matches the voltage reference around +/- 13 V with the loading of R is 27 Ω, 39 Ω, and 47 Ω. When

the load 10 Ω is connected to the generator, the trend in here is a good response for the obtained current instead of

voltages. While we can see that the behavior of the buck-boost converter to changes in rpm of the generator produces a

satisfactory response for loading of 47 Ω.

III. RESEARCH METHOD

a.Examination of Closed Loop Model for Buck-Boost

Converter

In this section, we provide the examination results of the usage of FLC as closed-loop control for DC-DC buck-boost

converter that connected to wind generator. To examine the efficacy of the proposed method, the variations of RPM

generated by wind speed and the load resistor R were utilized. The load is varied on each RPM condition to observe the

behavior of the output voltage of wind generator that controlled by DC-DC buck-boost converter. The load variations were 10

Fig. 10. Performance of the converter affected by the changes of generator’s

output voltage

b. Variety Loads of R and L To Examine The Characteristics of The Generator-Buck-Boost Converter

The various values of R and L loads are carried out to determine the output of buck-boost converter connected to

generator. The input voltage of the converter is directly derived from the generator output voltage. The values of R and

L are R = 10 Ω and 47 Ω, L = 150 H and 470 H, respectively. The results are illustrated in Tables. IX-XII.



TABLE IX

OUTPUT OF THE CONVERTER WITH R = 10 Ω AND L = 150 μH

RPM

Duty Input of Converter Output of Converter

(%) V (V) I (A) P (W) V (V) I (A) P (W)

135 20 7.71 0.03 0.23 1.21 0.17

131 25 7.20 0.05 0.36 1.44 0.13 0.19

124 30 6.70 0.07 0.47 1.54 0.16 0.25

123 35 6.50 0.10 0.65 1.96 0.18 0.35

112 40 5.76 0.14 0.81 2.04 0.18 0.37

101 45 4.71 0.17 0.80 2.22 0.19 0.42

95 50 3.76 0.21 0.79 2.28 0.22 0.50

92 55 3.96 0.25 0.99 2.25 0.21 0.47

83 60 2.40 0.30 0.72 1.97 0.27 0.53

75 65 1.87 0.33 0.62 1.77 0.27 0.48

72 70 1.42 0.34 0.48 1.52 0.27 0.41

TABLE X OUTPUT OF THE CONVERTER WITH R = 10 Ω AND L = 470 μH

RPM


(%) V (V) I (A) P (W) V (V) I (A) P (W)

151 20 9.10 0.01 0.09 2.43 0.02 0.05

149 25 8.70 0.02 0.17 2.50 0.03 0.08

149 30 8.60 0.02 0.17 2.88 0.05 0.14

147 35 8.40 0.04 0.34 3.35 0.05 0.17

140 40 7.93 0.06 0.48 4.40 0.07 0.31

136 45 7.30 0.08 0.58 4.44 0.07 0.31

126 50 6.55 0.11 0.72 5.10 0.09 0.46

126 55 6.10 0.14 0.85 5.33 0.10 0.53

125 60 5.30 0.19 1.01 5.62 0.10 0.56

115 65 4.60 0.22 1.01 5.72 0.10 0.57

103 70 3.76 0.26 0.98 5.84 0.09 0.53

The behaviors of the converter output by using a load resistor of 47 Ω and inductors of 50 μH and 470 μH are shown

in Tables XI-XII. It is shown that the use of load inductor

affects the output voltage of the converter. The inductor of 150 μH provides better performance of the converter compare to

470 μH due to when the duty cycle of converter is set to 50%,

then the output voltage of converter towards the input voltage

of converter, which is the input voltage of 5.71 V and output voltage gives 5.40 V. While as shown in Table XII, when the

input voltage of converter is 6.55 V, then the output voltage of

converter is 5.10 V

TABLE XI OUTPUT OF THE CONVERTER WITH R = 47 Ω AND L = 150 μH

RPM


(%) V (V) I (A) P (W) V (V) I (A) P (W)

136 20 7.52 0.03 0.23 3.05 0.06 0.18

135 25 7.36 0.04 0.29 3.49 0.07 0.24

134 30 7.19 0.06 0.43 3.90 0.08 0.31

133 35 6.51 0.08 0.52 4.13 0.09 0.37

127 40 6.12 0.10 0.61 4.92 0.10 0.49

125 45 5.97 0.12 0.72 5.07 0.10 0.51

123 50 5.71 0.14 0.80 5.40 0.11 0.59

115 55 5.20 0.16 0.83 5.57 0.11 0.61

109 60 4.78 0.18 0.86 5.44 0.11 0.60

103 65 3.98 0.22 0.88 5.68 0.12 0.68

90 70 3.03 0.27 0.82 5.70 0.11 0.63

. The greater the duty cycle produces the constant generator

rotation to be slow. When the duty cycle of the converter is small and the speed of the generator rotates rapidly, then it

produces a large output voltage of the converter. However, when the duty cycle of the converter is small, the output

voltage becomes small.

For Tables IX and XI, we use a load inductor of 150 μH and vary the load resistors of 10 Ω and 47 Ω. The use of a load

resistor 47 Ω gives a better performance for the converter due to the output voltage of the converter with a 50% duty cycle is

close to the input voltage of the converter.

TABLE XII OUTPUT OF THE CONVERTER WITH R = 47 Ω AND L = 470 μH

RPM


(%) V (V) I (A) P (W) V (V) I (A) P (W)

153 20 9.10 0.01 0.09 2.43 0.02 0.05 150 25 8.70 0.02 0.17 2.50 0.03 0.08

149 30 8.60 0.02 0.17 2.88 0.05 0.14

147 35 8.40 0.04 0.34 3.35 0.05 0.17

140 40 7.93 0.06 0.48 4.40 0.17 0.31

136 45 7.30 0.08 0.58 4.44 0.17 0.31

130 50 6.55 0.11 0.72 5.10 0.19 0.46

126 55 6.10 0.14 0.85 5.33 0.10 0.53

125 60 5.30 0.19 1.01 5.62 0.10 0.56

115 65 4.60 0.22 1.01 5.72 0.10 0.57

103 70 3.76 0.26 0.98 5.84 0.09 0.53

For the load resistor of 47 Ω where 50% duty cycle is

utilized for the converter, the input voltage of the converter is

5.71 V, then the output voltage of the converter is 5.40 V. For load resistor of 10 Ω, the input voltage of the converter is

3.76 V and the output voltage of the converter is 2.28 V. Here, the output voltage of the converter is improper to the

input voltage of the converter but produces a large current. Whereas for a load resistor of 47 Ω, the converter has a small

current, but the output voltage of the converter is appropriate

to the input voltage of the converter.

IV. RESULTS AND DISCUSSIONS a. The FLC for DC-DC Buck-Boost Converter with

Variation Values of RPM.

In this section, the examination is conducted to observe the behavior of the RPM which triggered by the changes of the converter’s output voltage. The relationship between RPM and time is shown in Figure 11.

Fig. 11. RPM vs time at for open loop buck-boost converter

As shown in Fig. 12, the changes of generator's RPM is affected by the changes of input and output voltages of the

converter due to the output voltage of converter affects the duty cycle value where when it value is improper to the

reference voltage or even gets away from setpoint value, the duty cycle becomes large, so it inhibits the generator rotation.

Fig. 12. Output voltage of converter using FLC, input and reference

voltages (set point)



From Fig. 12, it could be seen that the employment of FLC as closed-loop control of buck-boost converter provides a

good performance where there is changing of input voltage of

converter, the output voltage of converter remains at the

setpoint (reference voltage) around 12.74-13.2 volts. Meanwhile, it could be seen at time 3.9 s when the generator's

rotational speed is 263 RPM, the generator produces input

voltage for converter as of 5.28 V, then the converter yields the output voltage of 13.19 V. Due to the steady-state error of

the converter's output voltage is 1.92%, then, the FLC can

work properly for generator RPM in the range 192-308 as

shown in Figure 13.

Fig. 13. The output voltage of buck-boost converter using FLC

From Fig. 13, it is observed that the performance of the

hardware for buck-boost converter has 2.6 s as delay time,

3.835 s as uptime, peak time at 4.9 s, steady- time at 3.2 s and

steady-state error as 1.92%. While the performance of the converter using MATLAB software is having the delay time at

0.04 s, the time rises at 0.16 s, the steady-state at 0.16 s and

the steady-state error at 1.07%. The performance of converter using MATLAB software is better than hardware due to the

simulation of converter using MATLAB software is assumed

in an ideal state and there is no interference. The ideal

condition in question is that there is no change in energy, such as heat energy, whereas on the prototype design build

electrical components there is a change in energy that causes

the heat component, so that the electrical component function decreases.. The comparison between simulation results and

hardware is shown in table XIII. The current and power of

converter are also can be analyzed as shown in Fig. 14.

TABLE XIII THE COMPARISON BETWEEN SIMULATION RESULTS AND HARDWARE

Performance

Delay time Rise time Settling ESS (%)

(s) (s) time (s)

Simulation 0.04 0.16 0.16 1.07

Hardware 2.60 3.83 4.52 1.92

Fig. 14. Input and output currents of buck-boost converter From Fig. 14, it

is observed that the behavior of the converter's output current is smaller than the input current where

it is influenced by a duty cycle employed to the converter. If the

value of the duty cycle is changing from 20%-70%, the input

current range is in the range of 0.03 A - 0.28 A, and the output

current is in the interval of 0.03 A - 0.22 A. The changes of the

current of the converter will cause the change of power of

converter. The ranges of input power around 0.06

W-3.89 W yields the output power of converter in the range of 0.05-3.03W as shown in Fig. 15.

Fig. 15. Input and output powers of the converter

V. CONCLUSION

The application of FLC as a closed-loop control system for the buck-boost converter has been proposed in this paper. The

proposed controller approach had provided a satisfactory

performance compared to an open-loop system of the

converter. The proposed controller method can give a good performance for generator RPM in the range of 192 -364.

When generator RPM is set to 192, it produces an input

voltage for converter as 8.29 V. While the generator RPM is set to 364, it produces a converter's input voltage of 17.5 V.

Due to it can be controlled for some generator RPM

condition, so we can give a set point or reference voltage in

the range of 12.74 - 13.2 V.

VI. ACKNOWLEDGMENT

The authors would like to thank the Institute for Research and

Community Service (LPPM ITS), Institut Teknologi Sepuluh Nopember, Surabaya. with supporting financing the research. Also, the authors would like to thank the anonymous referees

for their comments on the eelier version of this work.

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