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Energy Procedia 90 (2016) 163 - 170

5th International Conference on Advances in Energy Research, ICAER 2015, 15-17 December

2015, Mumbai, India

Z-source DC-DC Converter with Fuzzy Logic MPPT Control for

Photovoltaic Applications

U.Shajith Ali *

Department of EEE, SSN College of Engineering, Chennai, India

Abstract

Z-source converters are recently premised DC-DC converters which can perform buck and boost operations, offer greater range of DC output voltage, high reliability and reduce in-rush and ripple currents. In photovoltaic (PV) applications, they provide better results compared to conventional DC-DC converters. These converters can be used as power conditioning units, so that the voltage boost and reception of maximum power from the PV array can be achieved. The maximum power point tracking (MPPT) is here achieved by controlling the duty cycle of the converter. A fuzzy logic control based MPPT is developed in this work to track the maximum power point of the PV array under variable solar irradiance and temperature conditions. MATLAB simulation and experimental results are given to establish the developed system. © 2016 The Authors.Publishedby ElsevierLtd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the organizing committee of ICAER 2015 Keywords: Photovoltaics; Z-source DC-DC Converter; Fuzzy logic; MPPT

1. Introduction

Sustainable energy usage is steeply increased because of the environmental awareness and continuous price growth of conventional fuels. PV arrays convert the energy from an essentially unlimited source -the sun-into useable electricity. In previous years, PV arrays have been directly connected to loads. Unfortunately, direct connection of PV cells to the utility almost never allows optimum power transfer when the load, solar irradiance or

* Corresponding author. Tel.: +91-44-27469700; fax: +91-44-27469772. E-mail address: shajithali@ssn.edu.in

1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the organizing committee of ICAER 2015 doi:10.1016/j.egypro.2016.11.181

temperature changes [1]. A PV module can produce the power at a point, called an operating point, anywhere on the current-voltage curve. But there is a particular point, called a maximum power point (MPP), at which the PV module develops the maximum output power. A maximum power point tracker (MPPT) is thus required to operate the PV array at this optimum point. To transfer energy from PV arrays into utility, power conditioning unit, which is normally a power electronic converter is required to convert the dc voltage obtained from PV array into desired voltage and to prevail the maximum power utilization of the PV array.

There are many dc-dc converter topologies intended in literature for solar photovoltaic application, some of them are derived from basic dc-dc converters, such as buck, boost, buck-boost, cuk converter, and others derived from isolated dc-dc converters, such as fly-back, forward converter. Also, there have topologies derived from half bridge and full bridge converters [2, 3, 4]. These topologies have many merits and can meet the needs of many applications. But they suffer two major limitations of short circuit effect and limited voltage gain. The high voltage gain can be achieved using isolated DC-DC converters by increasing the turns ratio of the transformer. But the input current is pulsed, which greatly affects the life of the PV array [5, 6].

New power converters like Z-source converters are developed to give better results and eliminate the above disadvantages of the conventional converters. Z-source converters have been recently studied by several researchers [7, 8, 9]. In this paper the operating principle of the Z-source DC-DC converter and its integration with PV array is explained for standalone photovoltaic applications. In this paper, a fuzzy logic based MPPT is proposed to track the maximum power point of the PV array under varying solar irradiance and temperature. Fuzzy is comparatively easy to design since it does not require any information about the exact model.

2. Z-Source DC-DC Converter

The topology of the Z-source DC-DC converter is shown in Fig.1. Unlike other buck-boost converters, Z-source converters consist of two inductors and two capacitors connected in X-shape to couple the converter to the input DC voltage. This converter can produce a desired output DC voltage irrespective of DC input voltage magnitude since the input may be the variable voltage obtained from solar panel, fuel cell or battery. Depending on switching positions of S1 and S2, the circuit of Z-source DC-DC converter has two operating modes: Non shoot-through mode and shoot-through mode [10].

Non shoot-through mode operation is obtained by closing the switch S1 and opening the switch S2. The equivalent circuit of this mode is shown in Fig.2. During this mode, the Z-network inductors L1 and L2 transfer the stored energies on them to the load. The capacitors C1 and C2 are charging using the input current Is. The output inductor L is energized during this operation.

Fig. 1. Topology of Z-source DC-DC converter.

Shoot-through mode is the short circuit mode and is achieved by closing the switch S2 and opening switch S1. Now the charges stored in Z-network capacitors C1 and C2 are transferred to inductors L1 and L2. The inductors and capacitors are now forming a parallel connection and the equivalent circuit is shown in Fig.3. So the total voltage across the Z-network is more than the input voltage. This makes the diode in parallel with switch S1 reverse biased. Hence the input voltage is disconnected and the input current is zero during this mode.

Fig. 2. Non shoot-through mode of operation.

Fig. 3. Shoot-through mode of operation.

Assume the Z-network inductors and capacitors are identical (i.e Lj=L2=Lz and C1=C2=Cz). So the following equations are valid.

Vci = Vc2 = VCZ

Vli = VL2 = VLZ

(1) (2)

When the converter is in non shoot-through operation for a duration Tn, the inductor voltage and the voltage across the switch S2 can be written as

Vlz = VS -VCZ

Vd = Vcz ~VLZ = 2FCZ -Vs

When the converter is in shoot-through operation for a duration Ts, the following equations can be written.

Vd = 0

Vlz = VCZ

The total switching time is T = Tn + Ts. The capacitor voltage VCz can be obtained by equating the average of the inductor voltage over one switching time T to zero and is derived as

Tn - Ts ) s 11 - 2D f S

where D = Ts /T is the shoot-through duty cycle. Similarly, the output voltage can be determined as

,,.1^, -M^f^-^, (8)

3. Fuzzy Logic MPPT Control

Many significant MPPT techniques have been formulated by many researchers [11, 12, 13, 14]. Recently, intelligence-based MPPT control techniques have been introduced [15, 16, 17, 18]. In this paper, a fuzzy logic based intelligent control technique associated with an MPPT controller is formulated in order to improve tracking efficiency. Fuzzy logic MPPT controller measures the values of the voltage and current at the output of the PV array and calculates the PV power to derive the inputs of the controller. The inputs to the fuzzy logic controller are taken as the error E and change in error CE. The error E is set as the change in power with respect to change in voltage and is expressed as:

£ = P(k ) - P(k-1) (9)

V (k) - V (k -1)

Hence CE can be written as:

CE = E(k) - E(k-1) (10)

where P(k) and V(k) are the instantaneous power and voltage of the PV array. Fig.4 and Fig.5 show the input membership functions. The crisp output of the controller is the shoot-through duty ratio of the converter and its membership function is shown in Fig.6. There are 49 inference rules applied and are summarized in Table 1 and shown in Fig.7.

Membership function plots

Fig. 4. Membership function plots for E.

Membership function plots

NB NM NS ZE PS PM PB

i f i i i i i i i

Fig. 5. Membership function plots for CE.

Membership function plots

Fig. 6. Membership function plots for D.

Table 1. Fuzzy rule base table.

E / CE NB NM NS ZE PS PM PB

NB M M M VVS VVS VVS VVS

NM M M M VS VS VS VS

NS S M M S S S S

ZE VS S M M M B VB

PS VB B B B M M B

PM VB VB VB VB M M M

PB VVB VVB VVB VVB M M M

input CE "5 ¡"P"1'E

Fig. 7. Input variables and output variable mapping.

4. Results and Discussion

For the purpose of modelling, simulation and hardware implementation, a photovoltaic panel from SOLKAR manufacturer is selected because this PV panel is commercially suited for all applications. This panel has 36 series connected PV cells and provides 37W of nominal maximum power, 21.24V of open circuit voltage and 2.55A of short circuit current. A PV array with two series connected PV modules each consists of 4 parallel SOLKAR PV panels is used as the DC input source.

Therefore, at standard conditions (1000 W/m2 and 25o C) the array produces the following maximum power and associated voltage and current.

Pm = 2 x 4 x 37 = 296W

Vmp = 2 x 16.43 = 32.86V Imp = 4 x 2.25 = 9A

The integration of Z-source converter with the PV array is developed in Matlab/Simulink. Simulation and experimental readings are obtained at the solar irradiation of 900 W/m2 and at the temperature of 38o C.

The inductors in the Z- network limit the current ripple through the devices during boost mode with shoot-through. The capacitors in the Z-network absorb the voltage ripple and maintain a reasonably constant voltage across the Z-network. The Z-network elements are designed with the following values: L1 = L2 = 3mH and C1 = C2 = 1000^F. The switching frequency is 10 kHz. The two semiconductor devices in the converter are selected based on the current through them and the maximum voltage across them. Here IGBTs are taken as the switching devices. The complete system is simulated and the output voltage, inductor current and capacitor voltage waveforms of converter are given in Fig.8. The tracking of the maximum power point of the PV array with this fuzzy controller is shown in Fig. 9 and is compared with that of conventional P&O MPPT controller.

It is very clear that the fuzzy controller based MPPT tracks the maximum power point quickly and is faster than the conventional controller. Laboratory hardware with the same parameter values is developed to validate the simulation results. Input and output powers are directly measured using power quality analyzer and these values are displayed in Fig.10. The values in watts and VA are rounded-off values by the meter. From the results, the output DC boosted voltage obtained from the Z-source converter is 102.2 V and the load current is 2.46A. The output power can be calculated based on the output voltage and load current. The total output power is 251W. Thus the maximum power output from the photovoltaic array is obtained at the available irradiation and temperature conditions. The tracking of PV power is shown in Fig.11.

Time (5)

Fig. 8. Simulation waveforms: Output voltage, inductor current and Capacitor voltage.

- P & O Controller - Fuzzy Controller

Time (s)

Fig. 9. Power Tracking with P&O and Fuzzy Controller.

IEIn Power 1 phase

0.0 Hz

w VA VAR

262 262 0

V avg A avg PF

32.17 8.14 OL

I Si Power 1 phase

0.0 Hz

w VA VAR

251 251 0

V avg A avg PF

102.2 2.46 OL

Input Power

Output Power

Fig. 10. Experimental measurement of Fuzzy logic MPPT.

Fig. 11. Experimental results of PV power tracking.

5. Conclusions

Z-source DC-DC converter is preferred in this work for boosting the PV voltage and for receiving the maximum power from the PV array. A fuzzy logic based control is developed for maximum power point tracking. From the results it is very clear that the fuzzy logic controller produces better performance than the basic P&O controller. It can be inferred that the fuzzy controller is faster in the transient part of maximum power point tracking. Also, very small oscillation in the steady state is obtained as compared to the P&O controller. Thus it is possible to get the

maximum power point of the PV array quickly and obtain the maximum power with much more accuracy with fuzzy controller. The complete system is modeled and simulated in the Matlab/Simulink environment. Also the proposed concepts are verified experimentally using a laboratory prototype.

References

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