Rev. Roum. Sci. Techn.– Électrotechn. et Énerg. Vol. 64, 3, pp. 235–240, Bucarest, 2019

1 Indian Institute of Technology (Indian School of Mines), Dhanbad, India. 2 Department of Electrical Engineering, University Institute of Technology, Burdwan, India; E-mail: soumya.sd1984@gmail.com.

DESIGN AND IMPLEMENTATION OF A PHOTOVOLTAIC WIND HYBRID SYSTEM WITH THE ASSESSMENT OF FUZZY LOGIC

MAXIMUM POWER POINT TECHNIQUE SHIV PRAKASH BIHARI1, PRADIP KUMAR SADHU1, SOUMYA DAS2, P. ARVIND2, ANAGH GUPTA2

Key words: Photovoltaic, Maximum power point technique (PPT), Wind hybrid system, Boost converter, Fuzzy logic controller.

Wind and sunlight are the most vital and significant sources of renewable energy for harnessing electricity to meet the rapid increase in energy demand all around the world. Off-grid hybrid systems prove to be more convenient and beneficial in distributing power to the rural areas and grid isolated system as compared to an grid connected system. The conventional system coupled with the hybrid system can prove to improve the system stability and is therefore economically viable in terms of the device and storage system cost. This paper deals with the design and experimental implementation of a hybrid wind /photovoltaic (PV) system along with fuzzy logic (FL) based MPPT controller. The hybrid system is essentially based on the nature of the source, environmental condition and the number of consumers being supplied. The control scheme has been justified with both simulations as well as hardware results. The validation results have been provided, which clearly show that the proposed scheme is expected to offer an impeccable solution for application in remote areas where utility grid is neither economical nor feasible.

1. INTRODUCTION

The fossil fuel based electricity production systems pose a serious threat on the international scale for the past two decades due to their harmful environmental impacts such as pollution, global warming, etc. Researchers are making an effort to meet the rapid increase in energy demands with inexhaustible, environment-friendly renewable energy sources (RES) [1–3]. These resources are clean, pollution free and abundant in nature. Presently, solar PV and wind energy sources have proved to be more promising, technically sound, and economical energy sources. They are being used in several places all over the world as a single source, jointly known as hybrid power system (HPS). These sources are employed in stand-alone or grid-connected mode. PV-wind hybrid power system with storage device can supply reliable power for stand-alone loads [4–7]. They produce energy using natural resources such as solar irradiation, natural wind, tides or waves etc. Since their nature changes with time, the power production of such renewable energy sources also varies from time to time. Maximum power from the sources can be generated using maximum power point tracking (MPPT) controller [8–12]. In order to obtain maximum power from these sources, many MPPT can be named from the literature, as in from solar panel, power can be generated using perturb and observe (hill climbing), incremental conductance, current control, voltage control, pilot cell, current compensated voltage control, fuzzy logic control, neural network control etc. [3,4, 8, 9, 12]. For wind system, many techniques such as tip speed ratio control, hill climbing search, fuzzy logic control, neural network control etc. have been proposed in the literature [3, 4, 13, 14].

2. THE PROPOSED METHOD

The physical model of a PV/wind/battery hybrid power system with fuzzy logic based MPPT is dealt in this paper for the HPS, which can run a small load of 550 W peak. The solar PV system output voltage is about 300 V and is based on temperature and level of solar irradiation.

Depending upon the wind speed, the wind generator provides an output of 24 V.

Fig. 1 – Schematic diagram of PV/wind/battery.

The variation of the duty cycle of the boost converter in each case provides the required boost in the voltage levels. The block diagram of a solar wind hybrid system incorporating the solar panels and wind turbines for power generation is given in Fig. 1. The duty cycles are derived from the respective fuzzy logic controllers.

2.1. EXPERIMENTAL SET-UP This system consists of four parts viz.

Artificial rotation unit Wind turbine Control unit SPV modules

Rotating generation unit consists of an induction motor and a voltage regulator. This unit produces variable rotation when the motor input voltage is varied with a regulator. The wind turbine unit incorporates a hub and a small permanent magnet synchronous generator (PMSG) which generates 3 phase electrical power of variable frequency. The control unit comprises of different meters viz. voltmeters, ammeters, power analyzers, tachometer along with an

236 Design and implementation of a photovoltaic wind system 2

MPPT controller and battery. This unit can, therefore, measure voltages, currents, wind speed, turbine speed (in rpm) and other power parameters at different stages. The solar photovoltaic (SPV) modules along with structure are fixed on roof and power of the array is supplied to the control unit. The rotating generation unit and wind turbine are fixed on a board. Tachometer sensors are placed near the turbine to measure the rotor rpm. The control unit can be attached and detached from the turbine unit as it is connected with the turbine components (generator) with the help of banana connectors. This control unit is portable, consisting of different components viz. battery, charge controller and generator. The wind turbine generator generates 3 phase power in delta connection which is connected to MPPT controller. Here, Arduino microcontroller is employed as MPPT controller. Arduino is a combination of both physical programmable circuit board and software. It is similar to that of an integrated development environment (IDE) that is used in computer. Hence, Arduino is selected in this paper to get a more precise result. The current sensor senses the current, the voltage sensor senses the voltage and supplies the output into Arduino which further compares with the preset instruction and varies as per requirement. The Arduino board comes with an inbuilt 16 MHz crystal oscillator and a voltage regulator. The pins 11, 10, 9 on the Arduino board (in Fig. 2) can be employed as a pulse width modulator, to trigger the gate of the boost circuit.

Fig. 2 – Hardware configuration of microcontroller ATMEGA-328P.

The three pins on the boost circuit are connected to ground, voltage and gate respectively. The boost circuit is further connected to load from pin given on the board. The battery will supply the power to load when there is a shortage of power at generation point. The specifications of experimental setup are given in Table 1.

Table 1

Specification of single PV module and wind turbine

Parameter Value Solar PV module

No. of PV modules 2 Maximum power 150 W

Open circuit voltage (Voc) 22.12 V Short circuit current (Isc) 8.68 A Maximum power voltage 18.03 V Maximum power current 8.32 A

Wind turbine

Type Horizontal axis type; coupled

with PMSG (3 phase) Voltage 24 V Power 250 W

3. MATHEMATICAL ANALYSIS OF PROPOSED MODEL

The mathematical analysis of the solar PV cells and boost converter are given in [15, 16].

3.1. MATHEMATICAL MODELLING OF WIND TURBINE

The energy stored in the wind is in the low-quality form. The nature of wind speed is dependent on several factors and therefore its modelling is done as a discontinuous and variable energy source illustrated as arbitrary variations in size and course. The mechanical power in the nonappearance of mechanical effects, on the physical structure of a wind turbine, that affect the energy conversion, is computed by

pttt cuAP 3

2

1 , (1)

where ttP is the mechanical power associated with the wind

energy captured by the blades, ρ is the air density, tA is the

area covering the rotor blades, and pc is known as the

power coefficient that is a function of the pitch angle θ of rotor blades and of the tip speed ratio λ which is the ratio between blade tip speed and wind speed value upstream of the rotor, given by

u

Rtt , (2)

where t is the rotor angular speed of the wind turbine,

tR being the radius of the area encompassed by the blades.

On the basis of blade element theory and taking into consideration the knowledge of the geometry of the blade, the power coefficient by numerical approximation is expressed as

i

ipC

4.18

14.2

e

2.13002.058.0151

73.0

, (3)

1

003.0

02.0

11

3

i . (4)

The maximum power coefficient is at a null pitch angle and for this numerical approximation is equal to

4412.00,0max optpC , (5)

with the optimal tip speed ratio at null pitch angle equal to 0572.70 opt . (6)

In order to obtain maximum power, the value of the tip speed ratio at each pitch angle should correspond to the global maximum for the power coefficient [17, 18]. Hence, the rotor angular speed of the wind turbine is a function of the maximum mechanical power maxttP and is given by

0,0

2

max5

max3

optp

ttoptt

CR

P

. (7)

The various forces acting on the blades and on the wind turbine tower (e.g. centrifugal, gravity and varying aerodynamic forces acting on the blades, gyroscopic forces acting on the tower) affect the conversion of wind energy into mechanical energy over the rotor of a wind turbine, thus introducing mechanical effects that influence energy

3 Shiv Prakash Bihari et al. 237

conversion [19]. Taking into consideration the physical forces, the mechanical power over the rotor is given by

3

1

2

1

)(1k

kkmm

kmkttt thtgaAPP , (8)

where

km

t

kkm trmg dsin(0

). (9)

The frequency of the wind turbine model is in the range of 0.1 to 10 kHz.

3.2. MATHEMATICAL ANALYSIS OF FUZZY LOGIC CONTROLLER

The basic framework of fuzzy inference system is simply a model that maps input characteristics to input membership functions, input membership functions to rules, rules to a set of output characteristics, output characteristics to output membership functions, and the output membership functions to a single-valued output or a decision linked to the output. Such a system incorporates arbitrarily chosen fixed membership functions and a rule structure that is essentially decided on the basis of the user’s interpretation of the characteristics of the variables involved in the model [20–22].

Fig. 3 – Fuzzy logic control flowchart.

For this model, a new fuzzy logic controller as a maximum power point tracker using a single-ended primary inductor converter (SEPIC) is employed, as it promises optimal utilization of the PV array as well as the wind turbine system proving its efficiency in variable load conditions [23]. The two input variables are the error signal generated from the proportional integral (PI) controller and the time derivative of the error signal. The output is the duty cycle of perturbation. For the computational purpose, Mamdani’s max-min method has been employed due to its widespread acceptance [24]. Here, both the premises and consequents are fuzzy proportions.

The membership functions are termed as: NB (Negative Big zone), NM (Negative Medium zone), NS (Negative Small zone), Z (Zero zone), PS (Positive Small), PM (Positive Medium), PB (Positive Large).

The above membership functions are expressed as triangular functions and each is mathematically defined as

xccxbcxc

bxaabaxax

cbaxs

if ,0 b if ),/()(

if ,/ if 0

),,,( . (10)

The algorithm for fuzzification of inputs to evaluate the membership for the antecedents is given as degree of membership:

Compute Delta 1=x–point 1 Delta 2= Point 2– x

If (Delta 1 ≤ 0) or (Delta 2 ≤ 0) Then Degree of Membership = 0 else Degree of Membership=

Max

2 Slope*2 Delta

1 Slope*1 Delta

min

The associated rules that govern the fuzzification are provided in the table below.

Table 2

Fuzzy rule table

Input

1 Input 2

NB NM NS Z PS PM PB

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

PB Z PS PM PB PB PB PB

The surface plot signifying the relationship between the

outputs to the two inputs is shown in the figure below.

Fig.4–Surface plot of the employed fuzzy algorithm.

The point of discontinuous variation in each membership function will vary as a function of current since the measured current is usually different at different time instants. Its potential to take decisions is an attractive feature of the fuzzy logic controller in the gain scheduling scheme. However, this is offset by the requirement of expert level of knowledge in the system to enable the design of a controller with suitable performance [20].

The tracking time is reduced along with improvement in the system performance during its operation under the steady state condition is achieved by the fuzzy logic controller by evaluating in variable step sizes to increase or decrease the duty cycle. As a result, this method is far superior than the traditional methods like P&O, incremental conduction etc. Besides, any sort of divergence related problems is eradicated which can arise as the reversal of direction of the controller input in response to the climatic conditions for any variation in power.

The defuzzification is carried out by the centre of area

238 Design and implementation of a photovoltaic wind system 4

(COA) method where the centroids are computed for each of the competing output membership functions. The defuzzified output being duty cycle is expressed as

ni iD

iDiDn

iD

1

1μ

. (11)

4. RESULT AND DISCUSSIONS

As discussed earlier, the PV module and the wind generator output voltage are directly supplied to the boost converter. The PV/wind hybrid system focuses on generating maximum global efficiency based on fuzzy logic control, implementing a fuzzy logic controller that provides smoother output waveform. Figure 5 displays the solar irradiance profile (600 W/m2 to 1000 W/m2) at 25°C.

Fig. 5 – Solar irradiance profile in constant temperature (25°C)

Figures 6 to 8 illustrate the generated voltage, current and power respectively from the PV module under varying irradiance.

Fig. 6 – Generated PV voltage in varying solar irradiance.

Fig. 7 – Generated PV current at varying solar irradiance.

Fig. 8 – Generated PV power in varying solar irradiance.

Figures 9 to 11 demonstrate the boost voltage, current and power respectively under varying irradiance.

Fig. 9 – Boost converter output voltage for PV in varying solar

irradiance.

Fig. 10 – Boost converter output current of PV in varying solar irradiance.

Fig. 11 – Boost converter output power from the PV in varying

solar irradiance.

An irradiance of 1000 W/m2 on the PV panel produces an initial voltage of 36 V and a power of 300 W.

5 Shiv Prakash Bihari et al. 239

Subsequently, a boost circuit is employed to obtain a voltage of 230 V at an irradiance 1000 W/m2.

Figure 12, shown below, signifies the rotor speed of the wind generator. Generated voltage and current from the wind generator along with the corresponding distribution of its phase voltage and current are demonstrated in Figs 13 and 14, respectively. The boost circuit output voltage, current and power for wind generator are displayed in Figs. 15 to 17.

Fig. 12 – Rotor speed of wind turbine.

Fig. 13 – Generated 3-phase voltage from the wind generator.

Fig. 14 – Generated 3-phase current from the wind generator.

Fig. 15 – Boost converter output voltage from wind generator.

It is clearly evident from the above figures that maximum generated voltage from the wind generator is 24 V, which is boosted up to 230 V by boost converter circuit. The boost converter along with the fuzzy logic converter is used here

to synchronize the PV voltage and the wind generator voltage by the help of synchroscope. After synchronization, the output from the boost converter is fed to the dc bus. The obtained graphs illustrate the efficiency of hybrid systems i.e. wind and solar combined system to produce power.

Fig. 16 – Boost converter output current from the wind generator.

Fig. 17 – Boost converter output power for wind generator.

The experimental setup for proposed PV/wind hybrid system that is implemented here is shown in Fig 18.

Fig. 18 – (a) Installed PV panel on rooftop, (b) Experimental setup for

wind turbine generator, (c) Experimental setup of PV/wind hybrid system, (d) MPPT charge controller circuitry.

5. CONCLUSION

The design and implementation of a PV/wind hybrid system are presented in this paper. The proposed system combines the hybrid wind and solar system using solar panel and a wind turbine generator. The maximum voltage

240 Design and implementation of a photovoltaic wind system 6

rating of the solar panel is 18.03 V with the output current at 8.32 A which produces a maximum power of 150 W. Two such solar panels are connected in series. A boost circuit is used to raise the voltage to the desired range. MPPT controller is also developed utilizing Arduino. The wind generator constitutes the other system where a permanent magnet synchronous generator is coupled to the motor via a shaft to generate power. The rated value of wind voltage is 24 V. The obtained values, being in ac are fed to a rectifier whose output is directly provided to boost converter that boosts it up to 230 V. The obtained results imply that the variation in the output is in proportion to the rotor speed. The rated output of the wind turbine generator is 250 W. Both the systems are provided with an MPPT controller along with a boost circuit which is finally connected to the dc bus.

Received on January 23, 2018

REFERENCES 1. S. Das , P. K.Sadhu, S.Chakraborty, S.Banerjee, T.Saha , Design and

Implementation of an Intelligent Dual-Axis Automatic Solar Tracking System, Revue Roumaine des Sciences Techniques – Électrotechnique et Énergetique, 61, 4, pp.383-387 (2016).

2. A. H. Bellia, Y.Ramdani, F. Moulay, K. Medles, Irradiance and temperature impact on photovoltaic power by design of experiments, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., Bucarest, 58, 3, pp. 284–294 (2013).

3. T. Bogaraj, J. Kanakaraj, J. Chelladurai, Modeling and simulation of stand-alone hybrid power system with fuzzy MPPT for remote load application, Archives of Electrical Engineering, 64, 3, p. 487-504 (2015).

4. S.B.Skretas, D.P. Papadopoulos, Efficient design and simulation of an expandable hybrid (wind photovoltaic) power system with MPPT and inverter input voltage regulation features in compliance with electric grid requirements, Electric Power Systems Research, , 79, 9, p.1271-1285(2009).

5. M. Kalantar, S.M. Mousavi Gazafrudi, Dynamic behaviour of a stand-alone hybrid power generation system of wind turbine, microturbine, solar array and battery storage, Applied Energy, 87, 10, p. 3051-3064 (2010).

6. E. Dursun, O. Kilic, Comparative evaluation of different power management strategies of a standalone PV/Wind/PEMFC hybrid power system, Electrical Power and Energy Systems, 34, 1, pp. 81-89 (2012).

7. C.N. Bhende, S. Mishra, S.M. Malla, Permanent Magnet Synchronous Generator-Based Standalone Wind Energy Supply System, IEEE Transactions on Sustainable Energy, 2011, 2, 4, p. 361-373.

8. H. Mahamudul, M. Saad, M.I. Henk, Photovoltaic System Modeling with Fuzzy Logic Based Maximum Power Point Tracking Algorithm, International Journal of Photoenergy, 2013, pp. 1-10 ( 2013).

9. I.H.Altas, A.M. Sharaf, A novel maximum power fuzzy logic controller for photovoltaic solar energy system, Renewable Energy, 33, 3, pp. 388-399 (2008).

10 B. Bendib, F. Krim, H. Belmili et al., Advanced Fuzzy MPPT Controller for a stand-alone PV system, Proc. Int. Conf. Technologies and Materials for Renewable Energy, Environment and Sustainability, Beirut, Lebanon, Energy Procedia, 50, pp. 383-392 (2014).

11. M.M. Algazar, H. Al-monier, H.A. El-halim, M. Ezzat El Kotb Salem, Maximum power point tracking using fuzzy logic control, Electrical Power and Energy Systems, 39,1, pp. 21-28 (2012).

12. C.L. Liu, J.H. Chen, Y.H. Liu, Z.Z. Yang, An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems, Energies, 7, 4, pp. 2177-2193 (2014).

13. E. Kamal, M. Koutb, A.A. Sobaih, B Abozalam, An intelligent maximum power extraction algorithm for hybrid wind–diesel-storage system, Electrical Power and Energy Systems, 32, 3, pp. 170-177 (2010).

14. T. Bogaraj, J. Kanakaraj, Development of MATLAB/SIMULINK Models for PV and Wind Systems and Review on Control strategies for Hybrid Energy Systems, International Review on Modelling and Simulations, 5, 4, pp. 1701-1709 (2012).

15. M. L. Azad, P. K. Sadhu, S. Das, B. Satpati, A. Gupta, P. Arvind, R. Biswas, An Improved Approach o Design a Photovoltaic Panel, Indonesian Journal of Electrical Engineering and Computer Science, 5, 3, pp. 515 – 520 (2017).

16. S. Das, P. K. Sadhu, S. Chakraborty, N. Pal, G. Majumdar, New Generation Solar PV Powered Sailing Boat Using Boost Chopper, TELKOMNIKA Indonesian Journal of Electrical Engineering, 12, 12, pp. 8077-8084 (2014).

17. R. Melício, V. M. F. Mendes, J. P. S. Catalão, Computer simulations of a converter control malfunction on PMSG-based wind turbines, IEEE EUROCON - International Conference on Computer as a Tool, 2011.

18. J.A Baroudi, V. Dinavahi, A.M Knight, A review of power converter topologies for wind generators, Renew. Energy, 32, p. 2369-2385 (2007).

19. V. Akhmatov, H. Knudsen, A.H. Nielsen, Advanced simulation of windmills in the electric power supply, Int. J. Electr. Power Energy Syst,, 22, pp. 421-434 (2000).

20. B.N. Alajmi et al., Fuzzy- logic-control approach of a modified hill-climbing method for maximum power point in microgrid standalone photovoltaic system. IEEE Trans. Power Electron, 26, 4, pp. 1022–1030 (2011).

21. M.A. Cheikh et al., Maximum power point tracking using a fuzzy logic control scheme, Revue des Energies Renouvelables, 10, 3, pp. 387–395 (2007).

22. I Altas., A. Sharaf, A photovoltaic array simulation model for Matlab-Simulink GUI environment, International Conference on Clean Electrical Power, ICCEP’07. IEEE (2007).

23. A.El Khateb, N.A Rahim, J. Selvaraj, M.N. Uddin, Fuzzy-logic-controller-based SEPIC converter for maximum power point tracking. IEEE Transactions on Industry Applications, 50, 4, pp. 2349-2358 (2014).

24. E.H. Mamdani, S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, 7, 1, pp. 1-13 (1975).