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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,[email protected]
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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