ABSTRACT

This paper presents

modeling and operation strategy for a microgrid using wind and solar energy. A

current-source-interface multiple-input dc-dc converter is used to integrate

the renewable energy sources to the main dc bus. In this paper, a direct driven

PMSG is used with a variable speed control method whose strategy is to capture

the maximum wind energy below the rated wind speed. This analysis considers

both wind energy and solar irradiance changes in combination with load power

variations. Fuzzy-based incremental conductance has been implemented in case of

solar panels. As a case study a 30-KW wind/solar hybrid power system dynamic

model is explored. The examined dynamics shows that the proposed power system

is a feasible option for a sustainable microgrid application.

Index

Terms–Photovoltaic system, MI Cuk converter, MPPT, fuzzy logic

I.

INTRODUCTION

The

increasing demand for electricity, the high price of oil and growing concerns

for our environment are some of several factors that forcing us to enhance the

uses of alternative energy sources 1. Among a variety of renewable energy

sources, solar energy is a sustainable alternative option that can be utilized

in various ways and can be used for many applications 2.

Alone, wind energy is capable of supplying

large amounts of power but its presence is highly unpredictable as it can be

here one moment and gone in another. Similarly, solar energy is present

throughout the day but the solar irradiation levels vary due to sun intensity

and unpredictable shadows cast by clouds, birds, trees, etc. These energy

sources are preferred for being environmental-friendly.

The

common inherent drawback of wind and photovoltaic systems are their

intermittent natures that make them unreliable. In order to combine these

energy sources, a current source interface multi-input dc-dc converter 3 with

dc bus system, is used because they provide a cost-effective and flexible

method to interface various renewable energy sources.

The

proposed microgrid is also equipped with energy storage devices such as batteries

4. Fuzzy-based incremental conductance MPPT is proposed in this paper. The

maximum power point (MPP) is the point on the I-V curve at which the PV module operates with maximum output

power, the MPP varies with changing conditions such as irradiance levels and temperature

5. To make the best use of PV sources, it is essential to always operate at

the MPP. The main job for the MPPT is to control the PV system and run it near

its maximum power point.

II.

MODELING

OF SYSTEM

A.

Solar System

A

photovoltaic (PV) system directly converts sunlight into electricity. The basic

device of a PV system is the PV cell. A Cell may be grounded to form panels or

arrays. The voltage and current available at the terminals of a PV device may

directly feed small loads such as lighting systems and DC motors. The output

characteristic of PV module depends on the cell temperature, solar irradiation,

and output voltage of the module. The figure shows the equivalent circuit of a

PV array

Fig.

1. Model circuit of solar cell

The

equivalent circuit of a photovoltaic cell, as shown in Fig.1, is modeled by a

photocurrent source, parallel diode, shunt resistance (Rsh) and series resistance (Rs)

6.

A

single solar cell can only produce a small amount of power. To increase the

output power of a system, solar cells are generally connected in series or

parallel to form PV modules.

=- (1)

where

I0 is the PV

array output current, v is the PV output voltage, Iph is the cell photocurrent that is proportional to

solar irradiation, Irs is

the cell reverse saturation current that mainly depends on temperature, k0 is a constant, ns

represents the number of PV cells connected in series, and np represents the number of such strings connected in

parallel. The cell photocurrent is calculated from

= (2)

where

Iscr cell short-circuit

current at a reference temperature and radiation; ki short-circuit current temperature coefficient; Tr cell reference

temperature; S solar irradiation in

mill watts per square centimeter. Moreover, the cell reverse saturation current

is computed from

=exp (3)

where

Tr cell

reference temperature; Irr reverse

saturation at Tr; EG band-gap energy of the

semiconductor used in the cell.

B.

Wind Turbine Modeling

The

wind turbine(WT) converts wind energy to mechanical energy by means of a torque

applied to a drive train. A model of the WT is necessary to evaluate the torque

and power production for a given wind speed and the effect of wind speed

variations on the produced torque7. The torque T and power produced by the WT within the interval Vmin, Vmax, where

Vmin is minimum wind speed

and Vmax is maximum wind speed,

are functions of the WT blade radius R,

air pressure, wind speed and coefficients CP

and Cq

= (4)

CP

is known as the power coefficient and characterizes the ability of the WT to

extract energy from the wind. Cq

is the torque coefficient and is related to according to

=

(5)

(6)

(7)

where

Cp=Coefficient of

performance,

Pm=Mechanical

output power (watt),

=blade pitch angle

=Air density (kg/m3),

Vwind=wind

speed (m/s)

A=Turbine

swept area,

=Tip speed ratio

R=Radius

of turbine blades (m),

T=Torque

of wind turbine,

=Angular frequency of

rotational turbine (rad/sec).

The performance coefficient Cp (, ), which depends on tip speed ratio and blade pitch angle , determines how much

of the wind kinetic energy can be captured by the wind turbine system. A

nonlinear model describes Cp

(, ) as

( , )= + (8)

C.

MI Cuk DC-DC

A

multiple-input (MI) converter allows a variety of energy sources to combine

their inputs using a single common converter. 8

Multiple

input (MI) energy sources have the capability of diversifying different energy

sources and increasing the reliability of the system. In a steady state

condition, the energy stored in the inductors has to remain the same at the

beginning and at the end of a commutation cycle. This indicates that the

current through the inductors has to be the same at the beginning and the end

of the commutation cycle. As the evolution of the current through an inductor

is related to the voltage across it

Fig.

2. MI Cuk converter

Switching

state 1 (0 < t

< Right of MPP (11) This algorithm is based on the fact that the gradient of the P-V curve is equal to zero at MPP dP/dV=0 since, = (12) (13) (14) However, condition in (14) is difficult to Fig. 3. Flowchart of an incremental conductance method obtained and therefore, there is a small permitted error. Equation (14) can be rewritten as = (15) B. Fuzzy Logic Controller A Fuzzy logic tool is a mathematical tool for dealing with uncertainty. Fuzzy Logic Controller (FLC) is one branch of the intelligent control in which the concept of FLC is achieved by mimicking and adopting the behavior of human being 13. FLC comprises fuzzification process, inference system, rule, and defuzzification. 1.Fuzzification It converts crisp inputs into fuzzy inputs. The values of membership function are assigned to the linguistic variables using three fuzzy subsets called upper negative (UN), upper zero (uz), upper positive (up). The input variable of fuzzy logic (FL) control includes (E), change of error (CE) and double change in error (DE).These variables are processed through inference system and through some rules. These conditions are done to generate the output of FL. The next process is defuzzification. Here, the output of fuzzy is a change in duty cycle (dD). 2. Inference engine Inference engine mainly consists of two-sub blocks namely, fuzzy rule base and fuzzy implication. The inputs which are now fuzzified are fed to the inference engine and the rule base is then applied. The output fuzzy sets are them identified using fuzzy implication method. The commonly used fuzzy implication method is Min-Max. The consequent fuzzy region is restricted to the minimum (min) of the predicate truth while selecting output fuzzy set. The output fuzzy region is updated by taking the maximum (max) of these minimized. Fuzzy sets during shaping of output fuzzy space. 3. Defuzzification Defuzzification is a method where fuzzy sets values are transformed into crisp values. The Output part of the fuzzy controller is change in duty cycle (dD). The method chosen here is a center of gravity as it is simple and fast for calculation. The formula for center of gravity method is given in equation. (16) From the above formula duty cycle (D) is calculated and is given to PWM in order to control switch of the dc-dc Cuk converter. Fig. 4. Proposed algorithm C. Proposed Algorithm The proposed hybrid algorithm between INC algorithm 14 and fuzzy logic control algorithm 15 is described in fig.4. As already stated the output of INC algorithm is (E) applied to fuzzy logic control (FLC). The error, coming from INC algorithm is processed to obtain change error (CE), and double change in error (DE) which can be obtained as follows (17) (18) (19) Here error (E) is taken such that describes incremental conductance condition to zero where MPP is reached in equation (17). E (k) and E (k-1) is the present and the past error values respectively, whose difference gives us the change of error in equation (18). IV. Results and Discussion The proposed method employs the INC and the FL based INC controller to adjust a duty cycle of Cuk converter to achieve MPP condition. Once the MPP is reached, the MIC controller regulates PV modules output voltage towards the obtained reference voltage by adjusting the MIC's duty ratios. The detailed flow chart of this control method is provided in fig.3. As indicated in fig.3 a tolerance e which equals zero is used for this criterion in the simulation study because this tolerance allows PV modules to remain at their MPP, thus producing steady state error at the operating points of the PV system. A simulation model is established in Matlab/Simulink to validate the performance of the proposed method for MPPT in a photovoltaic system under variable climate condition. In order to evaluate of the proposed system, the results are compared with conventional INC it clearly describes that the conventional INC alone is not able to decrease fluctuation occurring around MPP. The simulation results of grid-connected PV system voltage and current given in fig.5 and fig.6. THD analysis of the output voltage of grid-connected PV system is given in fig.7 from the THD analysis it is clear that the total harmonic distortion is 6.16%. Fuzzy-based Inc controlled MPPT strategy is used for PV output voltage it can smoothly and quickly track the maximum power point of PV array. Grid-connected PV/wind systems are modeled and simulated. Using the fuzzy-based Incremental control strategy the PV system generated a sinusoidal voltage having THD 0.40%. V. Conclusion In this paper, a hybrid of INC algorithm and FLC is employed to achieve MPPT of photovoltaic systems. Moreover, the circuitry of the proposed MPPT system is simulated in Mat lab/Simulink environment. Based on comparison between conventional Fig. 5. Output voltage waveforms of the inverter Fig. 6. Output current waveforms of the inverter Fig. 7. THD analysis of the output voltage of grid-connected hybrid system Fig. 8. THD analysis of output voltage of grid-connected hybrid system using fuzzy-based incremental conductance INC and Proposed hybrid MPPT, the performance of the proposed system proved better results, compared to those of conventional INC. In addition, there is no fluctuation exists around MPP when the hybrid algorithm is integrated with a dc-dc converter. References 1 Litos Strategic Communication, "The Smart grid: An introduction 2008, pp.1-43, prepared for the U.S. Department of Energy. 2 C.Yaow-Ming, L. Yuan-Chung-sheng, "Multi-input inverter for grid-connected hybrid PV/Wind power system," IEEE Trans. Power Electron., vol. 22, no. 3, pp. 1070-1077, May 2007. 3 B. G. Dobbs and P. L. 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