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Implementation of integrated double buck boost converter and fuzzy logic controller for power factor improvement of LED lamps.

INTRODUCTION

All "Homo sapiens" and even an invertebrates demands for the light for its existence. Sunlight is a natural light source created by nature and our ancestors found the fire as an artificial light. As man explore to develop new sources of lighting in order to improve efficiency and quality, researchers concentrate towards novel technology for light sources [1-4]. The LED light source is used based on the following advantages: energy saving, high luminous efficacy, ease to drive, avoids the issues of mercury, long lifetime, high color rendering index and small size [5-6]. The benefits of white LED's overshadowed the generic fluorescent and other lamps. Hence these LED's are predominantly used in countless applications like street lighting, domestic applications, automotive lighting, and other fashion applications etc. LED technology is widely used since life expectancy is about 20, 000 hours which is comparatively high that makes this technology more attractive in recent years [7-8].

Fig. 1 depicts the block diagram of the operating stages of the IDBB converter. Input AC supply voltage is converted into DC voltage by using rectifier. Then this DC voltage is applied to the load through IDBB converter circuit. The gate pulse for the IDBB converter is generated by using controller (PI/Fuzzy Logic controller) in closed loop operation. In this proposed system, IDBB converter possesses a controlled switch that is shared in two stages.

The IDBB converter comprises of two inductors, two capacitors, three diodes and one ground referenced switch, that renders many benefits like it is affordable, cost- effective, reliable & high efficiency [1-3].

The output power of IDBB converter relies on the duty cycle of the control switch. The high bulk capacitance is not compulsory, since the voltage ripple across this capacitor at the double line frequency can be attained by closed-loop operation. Hence, the change of duty cycle will affect only the output voltage, thus accomplishing it possible for a fast output voltage regulation [9-12]. Fuzzy logic controller can be used to improve the speed response of IDBB converter rather than PI controller.

Fuzzy Logic Controller (FLC) is dependent on fuzzy logic theory and engage a mode of approximate reasoning that features the decision making process of humans. The notion of the FLC is easier for professionals, as knowledge is denoted by means of perceptive, linguistic rules. In contrast to the generic linear and nonlinear control theory, a FLC doesn't follows mathematical model and it is broadly used to solve issues under some uncertain and dubious environments, with high non linearity. Since their advent, FLCs have been implemented in different kinds of applications such as insurance and robotics [13]-[16]. The main objective of this proposed work is to analyze the closed loop control of IDBB converter based on PI controller and Fuzzy logic controller.

Idbb converter:

A. Operation of IDBB converter:

Integrated double buck-boost (IDBB) converter is expected to supply power-LED lamps from the AC mains. IDBB converter consists of two inductors, two capacitors, three diodes and one ground-referenced controlled switch. This converter has been arranged in equivalent to two buck -boost converters and it is connected in cascade, in which, one grounded reference controlled switch is shared by the two stages.

Fig. 2 shows the schematic diagram of IDBB converter. As already explained in introduction, the IDBB converter behaves like two buck-boost converters and this two buck boost converters are connected in series. The input buck-boost converter consists of Li, [D.sub.1], [C.sub.B] and [M.sub.1] and the output buck-boost converter consists of [L.sub.0], [D.sub.2], [D.sub.3], Co and [M.sub.1].

B. Analysis of IDBB converter:

In this section, the analysis of IDBB converter is carried out. For this analysis, equivalent circuit of IDBB converter operating at three intervals within a switching period is considered. I.

I. Interval I [0<t< DTs]:

In this interval, MOSFET switch is ON, the input inductor [L.sub.i] is charged to [V.sub.Li], such that [V.sub.Li] = [V.sub.gi]. At that time the capacitor [C.sub.b] is discharged through [L.sub.o] and [D.sub.2], such that [L.sub.0]=[C.sub.B]. The output voltage [V.sub.0] is supplied by the output capacitor [C.sub.o]. Fig. 3.a. shows the equivalent circuit for this interval I.

II. Interval II [DTs<t<DTs+t1]:

In this interval, the MOSFET switch is turned OFF and the input inductor [L.sub.i] discharges to [C.sub.B] through [D.sub.1]. The charge stored in the output inductor [L.sub.0] is given to output capacitor [C.sub.0] and load through diode [D.sub.3.] Fig. 3.b. shows the equivalent circuit for interval II.

III. IntervalIII[DTs+t1<t<Ts]:

In the third interval, the MOSFET switch remain OFF. The current through input inductor becomes zero because the input inductor completely discharges. The diode [D.sub.1] is turned OFF. The output inductor is charged which stores the energy given to the output capacitor and load through diode [D.sub.3]. Fig. 3.c. shows the equivalent circuit for interval III. Fig. 4. shows the waveform of the IDBB Converter within a high frequency switching period around the peak line voltage.

C. Design of IDBB converter:

In this section the design of IDBB converter is considered. The converter is analyzed by considering a near unity power factor and a low ripple current through the power LED load. It is assumed that the input line voltage is a sinusoidal waveform given by [V.sub.g](t) = [V.sub.g] sin ([[omega].sub.L]D).

(i) Line Current and Input Power:

The value of the input current averaged at line frequency can be calculated as follows: it DaVs

[I.sub.g] = 1/[T.sub.s] 1/2 [I.sub.g-peak][Dt.sub.s] = [D.sup.2][v.sub.g]/2[L.sub.i][f.sub.s] sin [w.sub.L]t (1)

where [i.sub.g-peak] is the instantaneous peak current in each switching period, [f.sub.s] is the switching frequency, [V.sub.g] is the peak line voltage, and [[omega].sub.L] is the line angular frequency. The mean input power [P.sub.g] can be calculated by considering both input wavefonns as sinusoidal.

[P.sub.g] = 1/2 [v.sub.g][I.sub.g-peak] = [D.sub.2][v.sup.2.sub.g]/4[L.sub.i][f.sub.s] (2)

where [i.sub.g-peak] is the peak value of the averaged input current.

(ii) Output and Bus Voltages:

The output power is obtained as follows

[P.sub.o] = [V.sup.2.sub.o]/R (3)

with R being the static equivalent resistance of the LED load.

R = [V.sub.LED]/[I.sub.LED] = [V.sub.[gamma]]/[I.sub.LED] + [R.sub.[gamma]] (4)

where [V.sub.[gamma]] and Rr are the voltage and resistance parameters of equivalent circuit of the LED lamp. Assuming 100% efficiency and by equating (2) and (3), the output voltage is obtained as:

[V.sub.0] = D[V.sub.g]/2[square root of (k)] (5)

where K is a non-dimensional factor given by

k = [f.sub.s][L.sub.i]/R (6)

(iii) Reactive Components:

The input inductance Li can be calculated for a given output power using (2)

[L.sub.i] = [D.sup.2][v.sup.2.sub.g]/4[P.sub.o][f.sub.s] (7)

The low frequency peak-to-peak ripple voltage across capacitor Cb, AVb-lf can be obtained as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (8)

The necessary bus capacitance for a given peak-to-peak ripple in the bus voltage is then calculated from (8) as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9)

Since the output buck-boost converter operates in CCM, the low-frequency voltage ripple at the output will be given by:

[DELTA][V.sub.OLF] = D/ 1-D [DELTA]V[B.sub.LF] (10)

This ripple voltage will produce a low-frequency ripple current through the LED load that can be determined by the following:

[DELTA][I.sub.LED_LF] = [DELTA][V.sub.o_LF]/R[gamma](11)

Finally, the output inductance and capacitance [L.sub.o] and [C.sub.o] are obtained using the well-known expressions for a buck-boost converter operating in CCM,

[L.sub.O] = D[V.sub.B]/0.5[DELTA][I.sub.Lo_HF][f.sub.s] (12)

[C.sub.O] = D[I.sub.0]/0.5[DELTA][I.sub.Lo_HF][f.sub.s] (13)

where, [DELTA]ILo_HF is the high-frequency peak-to-peak current ripple, [DELTA]Vo_HF is the high-frequency peak-topeak output voltage ripple, and Io is the DC current through the LED load.

Controllers:

Feedback control of a system is necessary to get proper output. With the help of control technique, power factor can be improved and constant output current waveform can be obtained. Harmonic free source current can be achieved using control technique. In this paper, the control strategies are implemented through PI Controller and Fuzzy logic controller.

A. PI Controller:

The controllers are commonly employed in the system to produce control signal which is necessary to reduce the error signal approximately to zero. The output from the PI controller is the sum of product of proportional gain and error signal and product of integral gain and integral of the error.

Transfer Function = [k.sub.i]/S + [K.sub.p]

Transfer Function = [K.sub.p] (1/S[T.sub.i] + 1); [T.sub.i] = [K.sub.i]/S

where, [T.sub.i] is the Integral time of the Proportional Integral (PI) controller. Fig. 5. shows the basic circuit of PI controller.

B. Fuzzy Logic Controller:

Fuzzy logic controller is a logical system which is more advantageous to human thinking and natural language than ordinary language system. It is based on an I/O function and it is mapping between the input domain and output domain. These two domains are very low resolution quantization interval [17-20]. Fig. 6. shows the block diagram of fuzzy logic controller.

The components available in a Fuzzy system includes fuzzification, inference mechanism, rule base and defuzzification. In the fuzzy logic controller proposed in this paper, two inputs are considered for the fuzzy controller. They are 1) Error = Vref-Vo 2) Delta error = rate of change or error. (i)

(i) Fuzzifier/Fuzzification:

The input given to a fuzzy controller is a crisp value which needs to be fuzzified by fuzzifier. The fuzzifier converts the crisp input in to fuzzy sets using the triangular membership functions stored in the fuzzy knowledge base. Fig. 7. & Fig. 8.show the membership function of input Error & input Delta Error.

(ii) Fuzzy Inference System (FIS):

The fuzzy inference engine converts the fuzzy inputs to fuzzy outputs using the "If-Then" type fuzzy rules. There are two types of FIS: (1) Mamdani (2) Sugeno. The most fundamental difference between Mamdani-type FIS and Sugeno-type FIS is based on the way the crisp output is generated from the fuzzy inputs.

(iii) Defuzzifier:

Defuzzifier converts the fuzzy output from the fuzzy inference engine to crisp using triangular membership functions. The output of the defuzzifier is analogous to the ones used by the fuzzifier. Fig. 9. shows membership function defined for the output [21-23].

(iv) Fuzzy Rules:

Fuzzy rules can be used in mapping between the input and output domain for a fuzzy system and it is characterized by a set of conditions. These conditions are in terms of action rules or in IF -THEN form.

Based on the rule base, output fuzzy set is found and the membership degree is given by,

[mu]ctrl = min {[mu]e, [mu]ce}

Consider the following four rule evaluation,

IF error is PS and change in error is NM THEN control is NS IF error is NM and change in error is PM THEN control is Z IF error is Z and change in error is PS THEN control is PS IF error is PM and change in error is Z THEN control is PM

where, E is Error, [DELTA]E is Change in Error, PS is Positive Small, PM is Positive Medium, PB is Positive Big, ZE is Zero Error, NS is Negative Small, NM is Negative Medium, NB is Negative Big.

Simulation and results:

Simulation of the IDBB converter was done using MATLAB R2012a Software. MATLAB R2012a is one of the most popular software used in various fields of application. Its graphical simulation environment SIMULINK is very suitable for dynamic system simulation because there are plenty of toolboxes and modules. A simulation prototype for a LED application has been developed using MATLAB R2012a/SIMULINK.

A. Simulation of IDBB converter in open loop:

The IDBB converter consists of one switch, two inductors, two capacitors and three diodes. Fig. 10. shows the simulink model of the IDBB converter in open loop configuration.

Fig. 11. shows the input voltage and input current waveforms for IDBB converter in open loop. Fig. 12. shows the output voltage and output current waveforms for IDBB converter in open loop.

B. Simulation of IDBB converter in closed loop using PI controller:

As the output waveform of IDBB converter in open loop circuit has more distortion, we have to use control technique to minimize distortion. In this work the control technique is implemented using PI controller and Fuzzy logic controller. Fig.14 .shows the simulation model of PI controller. The simulation circuit of IDBB converter with PI controller is shown in Fig. 15.

Fig. 16. & Fig. 17. shows the input voltage & input current waveforms of IDBB converter and output voltage & output current waveforms of IDBB converter in closed loop based on PI controller respectively.

C. Simulation of IDBB converter in closed loop using Fuzzy Logic Controller:

In closed loop operation based on PI controller technique have some distortion and high settling time. These drawbacks are overcome by operating the IDBB converter in closed loop using Fuzzy logic controller technique. Fig. 19. shows the simulation model of Fuzzy logic controller and Fig. 20. shows the simulation circuit of IDBB converter with Fuzzy logic controller. Fig. 21 and Fig. 22 shows the input voltage & current and output voltage & current waveform of IDBB converter respectively in closed loop using fuzzy logic controller.

Fig. 21. & Fig. 22. shows the input voltage & input current waveforms of IDBB converter and output voltage & output current waveforms of IDBB converter in closed loop based on Fuzzy logic controller respectively.

Conclusion:

IDBB converter has been implemented to reach a high power factor for LED lighting applications. The performance of IDBB converter circuit has been studied by using MATLAB Simulink model in both open loop and closed loop. By operating IDBB converter in two stages of DCM and CCM, the power factor has been improved and ripple current get reduced. The dynamic response of a converter has been made quite fast since two stages of converter are treated as a single stage. The idea in this converter is to keep it as simple as possible in order to curtail cost and attain high efficiency with only one controlled switch. The load is considered to be 70-W LED lamp. In case of the closed loop operation, IDBB converter with PI Controller and FUZZY LOGIC controller are simulated and from the simulation results it is inferred that, IDBB converter improves the Power factor by correcting both voltage and load current harmonics, thus making the LED lamp system reliable. Thus the proposed converter has provided high power factor and good efficiency with longer life time.

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(1) Dr. I. Kathir and (2) S. Seetha Priya

(1) Associate Professor, Dept of EEE, Mepco Schlenk Engineering College, Sivakasi, Tamiinadu, India.

(2) P. G Scholar [PE], Dept of EEE, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India.

Received 28 March 2017; Accepted 7 June 2017; Available online 12 June 2017

Address For Correspondence:

Dr. I. Kathir, Associate Professor, Dept of EEE, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India.

Caption: Fig. 1: Block diagram of IDBB converter

Caption: Fig. 2: Schematic diagram of the IDBB converter

Caption: Fig. 3: Equivalent circuits of the IDBB converter. (a) Interval I: 0 < t < DTs. (b) Interval II: DTs < t < DTs+ti. (c) Interval III: DTs+ti < t < Ts

Caption: Fig. 4: Waveform of the IDBB Converter within a high frequency switching period around the peak line voltage

Caption: Fig. 5: Block diagram of PI controller

Caption: Fig. 6: Block diagram of Fuzzy Logic controller

Caption: Fig. 7: Membership function of "Error" input

Caption: Fig. 8: Membership function of "Delta Error" input

Caption: Fig. 9: Output Membership Function

Caption: Fig. 10: Simulation model for IDBB converter in open loop

Caption: Fig. 11: Input voltage & Input current waveforms of IDBB Converter in open loop configuration

Caption: Fig. 12: Output voltage & Output current waveforms of IDBB Converter in open loop configuration

Caption: Fig. 13: Harmonic spectrum graph for open loop

Caption: Fig. 14: Simulation model of PI controller

Caption: Fig. 15: Simulation model for IDBB converter in closed loop based on PI controller

Caption: Fig. 16: Input voltage & Input current waveforms of IDBB converter in closed loop based on PI controller

Caption: Fig. 17: Output voltage & Output current waveforms of IDBB converter in closed loop based on PI controller

Caption: Fig. 18: Harmonic spectrum graph for closed loop operation based on PI controller

Caption: Fig. 19: Simulation model of Fuzzy Logic controller

Caption: Fig. 20: Simulation for model IDBB converter in closed loop based on Fuzzy logic controller

Caption: Fig. 21: Input voltage & Input current waveforms of IDBB converter in closed loop based on Fuzzy logic controller

Caption: Fig. 22: Output voltage & Output current waveform of IDBB converter in closed loop using Fuzzy logic controller

Caption: Fig. 23: Harmonic spectrum graph for closed loop operation based on Fuzzy logic controller
Table 1: Fuzzy rule based matrix.

E          NB   NM   NS   Z    PS   PM   PB
[DELTA]E

NB         NB   NB   NB   NB   NM   NS   Z
NM         NB   NB   NB   NM   NS   Z    PS
NS         NB   NB   NM   NS   Z    PS   PM
Z          NB   NM   NS   Z    PS   PM   PB
PS         NM   NS   Z    PS   PM   PB   PB
PM         NS   Z    PS   PM   PB   PB   PB
PB         Z    PS   PM   PB   PB   PB   PB

Table 2: Comparison Results

Parameters        Open loop     Closed loop     Closed loop Fuzzy
                                PI Controller    Logic Controller

Output Voltage     215.3V          200.7V            200.4V
Output Current     0.5129A         0.349A            0.348A
Power Factor       0.9179          0.9784            0.9939
THD                39.42%          15.46%             0.81%
Settling Time      0.06sec        0.05sec            0.02sec
Maximum Peak         75%            60%                50%
  Overshoot
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Author:Kathir, I.; Priya, S. Seetha
Publication:Advances in Natural and Applied Sciences
Article Type:Report
Date:Jun 1, 2017
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