Fuzzy-logic Controller based Multi-port

DC-DC Converter for Photo-Voltaic Power

System

M VENKATESHKUMARa and Cheng Siong CHIN b,1

a Department of EEE, Aarupadai Veedu Institute of Technology (AVIT), Chennai, ,

Tamil Nadu 600128, India b

Faculty of Science, Agriculture, and Engineering, Newcastle University Singapore,

Singapore 599493, Singapore

Abstract. The mathematical model of the photovoltaic (PV) system is developed and simulated. The maximum level of power point tracking is implemented for

operating the PV system at optimal power generation using an intelligent controller

with analysis of the system performance. A multi-port converter design is proposed and simulated. The proposed converter is controlled by Fuzzy-logic controller for

effective utilization of PV power generation and battery management system. The

proposed Fuzzy logic based controller is designed for PV and battery management system at various load demand and weather conditions. The proposed system

performance is analyzed at the different weather and load demand. The simulated

results show the effectiveness of the proposed system.

Keywords. Fuzzy-logic controller, MPPT, multi-port converter, DC-DC, PV,

battery management system, MATLAB

Introduction

Over the past few years the environmental impact of carbon dioxide and other

greenhouse gases due to usage of fossil fuels based power generating station and other

applications. Nowadays many researchers are concentrated alternate fuels for power

generation without pollution[1]. The alternate resources should be friendly and naturally

replenished such as solar photovoltaic, wind, hydro system. Among various renewable

energy sources, the solar photovoltaic system has major role in generating clean

electricity by using sunlight. The sunlight has everlasting energy sources, but the

drawback is the generation of electricity is only possible during the sunny day. This paper,

focused on maximum power generation of solar photovoltaic (PV) system and effective

power supply to the consumer using advance DC-DC converters. The smart, intelligent

controller is innovative and provides the best solution to meet the increasing consumer

power demand and solve the challenges to penetrating the supply of solar photovoltaic

power into the nearby consumer end. Therefore, modeling and improvement of

distributed renewable energy sources are required. In global, the solar PV system is a

very popular energy source for generating clean electricity, but the output of photovoltaic

1 Corresponding Author. Cheng Siong CHIN, Faculty of Science, Agriculture, and Engineering,

Newcastle University Singapore, Singapore 599493, Singapore,cheng.chin@ncl.ac.uk

system power is nonlinear due to the variation sunlight. Therefore, maximum power

point tracking (MPPT) controller has played a major role in the solar photovoltaic system

due to the variation in sunlight irradiance. In this paper, the Fuzzy-logic controller is

selected for MPPT controller of solar PV system due to the drawbacks of conventional

MPPT controllers such as lack of robustness, slow in speed and poor efficiency [2-3].

The proposed Fuzzy-logic based MPPT controller circumvents the problems and

provides better performance under changing weather conditions. The paper proposes a

multiport DC-DC converters to regulate the voltage level in solar PV application under

variable weather conditions. However, the traditional DC networks topology has few

drawbacks such as a mismatch of voltage ratings in energy storage device and the poor

fault-tolerant design of power electronics. Hence, the proposed Fuzzy-logic controller

based multiport DC-DC converter will provide an alternate solution and better

performance under different operating conditions.

1. MPPT Controller

First paragraph. Among various renewable energy sources, the solar-based photovoltaic

based power generation is a very popular method. The solar photovoltaic power system

has played a vital role to meet consumer power demand because of their abundant supply.

But the power generation of solar photovoltaic could be disturbed due to nonlinear

irradiance and variable weather condition. The efficiency photovoltaic power system is

very less (9 – 17% in variable irradiance 0 W/M2 to 1000 W / M2) [4-5]. Many

investigators concentrated to generate the maximum power from the solar photovoltaic

system under various weather condition through MPPT methodology [6]. There exists

several MPPT algorithms established.

In feedback voltage and the current method, the photovoltaic power system

without storage device was evaluated. The voltage and current values of the PV

panel were compared with reference voltage value and current value. The DC

to DC converter was based on error value for a duty cycle that operates the PV

panel near to MPP.

In voltage and frequency (P-Q) method, two different controllers were used to

control the inverter side and battery power management. The duty cycle for the

DC-DC converter was generated by the PI controller, with respect to the change

of PV power generation.

In feedback of power variation with voltage and current approach, the

differences in voltage or current (dv/ dt) or (di/dt) are supervised [7]. The output

power has been calculated at load terminal due to power losses in the converter.

The duty cycle will be modified consequently to achieve the MPP. In

perturbation and observation method measures V and I at the present

atmosphere condition. The PV power P1, was calculated by considering the

minor changes from the duty cycle and the PV power P2 was also calculated.

The power of PV, P2 was compared with P1. The perturbation was used when

P2 was more than P1; This method has a significant drawback due to occasional

deviations [8].

In incremental conductance method, the voltage and current value were

measured from the PV cell. The above values were compared with a reference

value based on the error that the controller generated and fed to the PWM

generator for the inverter [9].

The proposed method will use the Fuzzy-logic controller [10]. This controller has

two inputs, namely, actual irradiation and PV voltage. The trapezoidal method is used to

convert these parameters to a Fuzzy set. The knowledge-based system has the reference

voltage and compares the observed value [11]. Based on the error, IF-THEN rules will

be used to select the duty cycle. Finally, the Fuzzy set value is converted into a crisp set

using the center of gravity model method, and then the signal is fed into a PWM generator

to generate the pulse for DC-DC converter [12-14].

2. Fuzzy-logic MPPT Controller

The MATLAB simulation model of 150 Watts PV system with boost converter and fuzzy

based MPPT [14] controller is presented in Figure 1. The PV module rating is as follows;

open circuit voltage is 41.8 V, the maximum voltage is 34.5 V, the maximum current is

4.35 A and short circuit current is 5.05 A. The simulation model has been simulated

under various weather condition and analysis the results without MPPT controller [15].

The results are not satisfied and do not generate maximum power under different weather

conditions. The MPPT controller generates maximum power from a solar PV system

under various weather condition. In this section, the Fuzzy-logic controller has been

modelled for MPPT of PV system. The proposed fuzzy MPPT controller has two inputs

and single output such as PV voltage. The PV current acts as an input signal and the Duty

cycle is shown in Figure 2. The input variables are converted into Fuzzy membership

function using the trapezoidal method as depicted in Figure 3 and 4. The input PV voltage

membership function has three memberships such as low voltage in between 0 V to 9 V,

Medium voltage in between 9 V to 27 V and high voltage in between 27 V to 35 V.

The input PV current membership function has three memberships such as low

current in between 0 A to 1.4 A, Average current in between 1.4 A to 3.2 A and high

current in between 3.2 A to 4.35 A. The duty cycle is an output membership function as

shown in Figure 5. The output converter duty cycle membership function has three

membership such as low duty cycle in between 0.4 to 0.6, Medium duty cycle in between

0.6 to 0.8 and high duty cycle in between 0.8 to 1. The rules are developed with respect

to the nature of changes in input and output variable to achieve the goal as shown in

Table 1. The above model has been simulated in MATLAB environment under various

irradiance conditions (0 to 1000 W / M2), and the PV output power. The boost converter

output voltage and current are analyzed under various conditions. The comparative

analysis of P&O and a Fuzzy controller for 150 W PV system is presented in Table 2.

Figure 1. Fuzzy-logic control based MPPT for 150 watts PV system

Figure 2. Fuzzy MPPT control model

Figure 3. Fuzzy input membership function (voltage)

Figure 4. Fuzzy input - membership function (current)

Figure 5. Fuzzy output membership function (duty cycle)

Table 1. Fuzzy rules for MPPT

Table 2. Comparative analysis of P&O and Fuzzy Controller

- Voltage Low - Voltage Medium - Voltage High

- Current Low - Duty Cycle High - Duty Cycle High - Duty Cycle Low

- Current Medium - Duty Cycle Medium - Duty Cycle Medium - Duty Cycle Low

- Current High - Duty Cycle Medium - Duty Cycle Medium - Duty Cycle Low

Solar Irradiance

Watts / M2

- P&O

Controller

- Fuzzy

Controller

- 250 - 11.9 W - 12.7 W

- 500 - 38.7 W - 39.68 W

- 750 - 84.7 W - 87.6 W

-1000 - 142.8 W -147.9 W

3. Proposed Multi Port Converter for PV and Battery Management System

The proposed multi-port converter is designed for PV with battery management system

as shown in Figure 6. The proposed system can operate in two modes of operation such

as boost and the buck converter. The proposed system consists of six MOSFET power

electronics switches for PV and Battery power flow control [13]. The MOSFET switches

namely: S1 to S3 are connected to the PV system as well as the switches S4 to S6 are

connected to the battery. The high switching frequency transformer is connected this

multi-port converter then used a diode rectifier as shown in Figure 6. The power

management system for the proposed system is used for Fuzzy logic control to improve

the system performance under variable conditions. There are three modes of operations

namely:

Mode 1: PV power is higher than load demand

Mode 2: PV power is lesser than load demand

Mode 3: PV power is equal to load demand

Figure 6. Proposed three port DC to DC converter

Figure 7. Power flow during the power of PV is higher than the load demand

When the power is higher than the load demand, the PV power is supply to load,

and the remaining power will be stored in the battery as shown in Figure 7. Therefore

two type of power flow is presented, namely, PV power flow to a load and electricity

storage to battery.

MODE 1: PV power is greater than the load demand

Switches are turned on: S1, S2, S3, S4, and S6

Switches are turned off: S5

The power flow direction to load: PV +ve L5 S2 T pry S3PV-ve

|| T sec D1 LoadD4T sec

Battery charging: PV +ve S1 S4 B+veB-veS6S3PV-ve

MODE 2: Power of PV is less than the load demand

If the power is less than the load demand, both PV power and battery will discharge

power to the load as shown in Figure 8.

Switches are turned on: S2, S3, S5, and S6

Switches are turned off: S1 and S4

The power flow direction

PV +ve L5 S2 T pry S3PV-ve || T sec D1 LoadD4T sec

B +ve L2 S5 T pry S3PV-ve || T sec D1 LoadD4T sec

Figure 8. Power flow during PV power is lesser than load demand

Mode 3: Power of PV is equal to load demand

When the power is equal to load demand, the PV power will supply to the load as

shown in Figure 9.

Switches are turned on: S2 and S3

Switches are turned off: S1, S4, S5, and S6

The power flow direction

PV +ve L5 S2 T pry S3PV-ve || T sec D1 LoadD4T

sec

Figure 9. Power flow during the power of PV is equal to the load demand

The proposed Fuzzy-logic controller based multiport DC-DC converter simulation

model has been developed in MATLAB environment as shown in Figure 10. In this

simulation model, two DC sources such as a solar PV system and energy storage device

are used. The proposed model is analyzed with different modes such as Mode 1: PV

power greater than load demand, Mode 2: PV power lesser than load demand, and Mode

3: PV power equal to load demand. In this simulation model, the maximum power

generated by PV is 150 Watts, battery has 48V / 30 AH, and three different rating load

are connected in this circuit such as 50 W, 150 W and 200 W. Based on the above

condition, the Fuzzy-logic controller is developed for energy management system for

this multiport DC-DC converter as shown in Figure 11. The Fuzzy-logic controller has

one input signal such as load demand and three output signal such as PV power, battery

charging and discharging (see Figure 12). Based on the above input and output

membership function, the fuzzy rules are formed and presented in Table 3 and Figure 13.

Figure 10. Fuzzy Logic control based Energy Management System (EMS) simulation, model

Figure 11.Fuzzy controller for EMS

Figure 12. Changing Power - Fuzzy - input membership function

Table 3. Fuzzy rules for Power management system

Figure 13. Fuzzy rules for PV equal to the load demand

4. Results and Discussion

The simulation results of the above multiport DC-DC converter is analyzed at a different

mode of operations. Figure 14 represents the operating stage of PV power, battery

charging and discharging conditions with respect to present load demand and Fuzzy-

logic controller. In the simulation results, a comparison can be seen in Figure 14 and 15.

The PV power increases gradually with respect to solar irradiance. In this condition, 50

Watts load was connected to the proposed circuit. In Figure 15, the PV output power

reached 50 Watts at 0.02 sec when the battery was discharged when connected to the

load. The PV power was increased when the battery was charged. After 0.05s, the 200

Watts load was attached to the proposed circuit. The respective multiport DC-DC

converter output voltage waveform is presented in Figure 16.

Error Power

(PV Power –

Load Power)

- Negative Power - Zero Power - Positive

Power

- PV Power - ON - ON - ON

- Battery Charging - OFF - OFF - ON

- Battery

Discharging

- ON - OFF - OFF

Figure 14. Fuzzy controller generated Pulse for MOSFET switches

Figure 15. Power Waveform

Figure 16. Voltage waveform

5. Conclusion

In this paper, the 150 Watts PV system with Fuzzy controlled based MPPT was simulated

in MATLAB environment in different weather conditions. The simulation results were

analyzed. The proposed three port DC to DC converter was designed and simulated with

PV and a battery energy management system using the Fuzzy-logic controller. The

proposed model was recommended for a standalone PV system with battery storage. For

future works, the fuzzy controlled based MPPT will be demonstrated on a real system.

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