fuzzy logic controller based wind energy conversion...

FUZZY LOGIC CONTROLLER BASED WIND ENERGY CONVERSION SYSTEM

G. BALAJI1, I. GNANAMBAL

2 & P. GAJENDRAN

3

1Assistant Professor, Department of Electrical and Electronics Engineering, Paavai Engineering College, Namakkal,

Tamil Nadu, India 2Associate Professor, Department of Electrical and Electronics Engineering, Govt. College of Engineering. Salem,

Tamil Nadu, India 3PG Student ME Power Systems Engineering, Department of Electrical and Electronics Engineering, Paavai Engineering

College, Namakkal, Tamil Nadu, India

ABSTRACT

Renewable energy resources (RES) are being increasingly connected in distribution systems utilizing power

electronic converters. This paper presents a fuzzy logic controller based wind energy conversion system. A fuzzy control

strategy for achieving maximum benefits from these grid-interfacing inverters when installed in single phase load

connection. The inverter is controlled to perform as a multi-function device by incorporating active power filter

functionality. A novel control of an existing grid interfacing inverter to improve the quality of power at three phase

distributed generating systems has been presented, the current harmonics and nonlinear load connected to the point of

common coupling (PCC), are compensated effectively such that the gird side currents always maintained as balanced and

sinusoidal at unity power factor. Moreover, when the power generated from RES is more than the total load demand, the

grid interfacing with the proposed control approach not only fulfills the total load active and reactive power demand but

also delivers the excess generated sinusoidal active power to the grid at unity power factor.

KEYWORDS: Maximum Power Point Tracking(MPPT), Wind Energy Conversion Systems(WECS), Permanent

Magnet Synchronous Generator(PMSG), Digital Signal Processors(DSP), Renewable Energy Resources(RES), Voltage

Source Inverter(VSI)

INTRODUCTION

Electric utilities and consumer of electric power are becoming increasingly concerned about growing energy

demand. Eighty percent of total global energy demand is supplied by the burning of fossil fuels. So increasing air pollution,

water pollution, global warming concerns, diminishing fossil fuels and their increasing cost had made it necessary to look

towards renewable sources [1].Therefore, significant efforts have been made to develop wind energy conversion system as

alternative renewable resource.There has been an enormous interest in many countries on renewable energy for power

generation.This is a feasible power level to be interfaced to the power grid. Wind power growth with 20% annual rate has

experienced the fastest growth among all renewable energy sources [2], [3].The market liberalization and government’s

incentives have further accelerated the renewable energy sector growth .Renewable energy source integrated at distribution

level is termed as distributed generation. The utility is concerned due to the high penetration level of intermittent resin

distribution systems as it may pose a threat to network in terms of stability, voltage regulation and power quality issues.

Wind energy generation has brought about many challenges to electrical power system engineers. The problems

encountered in the electrical network comprising wind energy systems are due to the continuous variations in the wind

regime. These Variations may inflect undesirable fluctuation in the network and thus has limited the capacity of the wind

International Journal of Electrical and

Electronics Engineering Research (IJEEER)

ISSN 2250-155X

Vol. 3, Issue 1, Mar 2013, 287-296

© TJPRC Pvt. Ltd.

288 G. Balaji, I. Gnanambal & P. Gajendran

energy systems which can be integrated with the network to a modest penetration factor. Various techniques have been

proposed to cope with the variations in the wind speed to ensure high performance and steady output for the wind energy

systems and hence contribute to allow for higher penetration factor[4]-[6].

The system operation with improved power quality at point common coupling .However, the extensive use of

power electronics based equipment and non-linear loads at point of common coupling generate harmonic currents, which

may deteriorate the quality of power. Generally, current controlled voltage source inverters are used to interface the

intermittent res in distributed system. Recently, a few control strategies for grid connected inverter incorporating power

quality solution have been proposed. In [7] an inverter operates as active inductor at a certain frequency to absorb the

harmonic current. But the exact calculation of network inductance in real-time is difficult and may deteriorate the control

performance. a similar approach in which a shunt active filter acts as active conductance to damp out the harmonic sin

distribution network is proposed in [8]. In [9], a control strategy for renewable interfacing inverter based on – theory is

proposed. In this strategy both load and inverter current sensing is required to compensate the load current harmonics.

The inverters must also be able to detect an islanding situation, and take appropriate measures in order to protect

persons and equipment [10]. Islanding is the continued operation of the inverter when the grid has been removed on

purpose, by accident, or by damage. In other words, the grid has been removed from the inverter, which then only supplies

local loads. The available detection schemes are normally divided into two groups active and passive. The passive methods

do not have any influence on the power quality, since they just monitor grid parameters. The active schemes introduce a

disturbance into the grid and monitor the effect. This may affect the power quality, and problems with multiple inverters in

parallel with the grid are also known to exist [11-12].

In order to explore the effects, grid connected wind turbine system was modeled using Mat lab Sim Power System

Toolbox. Selected wind generator structure is PMSG generator, connected directly to the grid. Many of the wind power

plants installed today have such configuration [13]. This type of the generator cannot perform voltage control and it

absorbs reactive power from the grid. Phase compensating capacitors are usually directly connected. That type of wind

turbine is cheap and robust and therefore popular, but from the system analysis point of view it has some drawbacks [14-

15].

In this paper, we are presenting the work carried out in designing the Fuzzy logic controller for switching

operation of inverter. A simple control strategy of inverter is adopted where the measurement. Then the performances of

conventional fuzzy logic controller are investigated. Simulation results show that Total Harmonic Distortion in source

current is drastically reduced fuzzy controller is included in the inverter control circuit. Simulation work has been done

using MATLAB/SIMULINK software.

WIND GENERATION SYSTEMS

The PMSG generates a voltage whose frequency and amplitude are proportional to its rotor speed and wind

atmosphere. The experimental generator currents are the rectified voltage and current is fed to a inverter which produces a

constant frequency voltage waveform that is injected to the grid using a step-up transformer. Assuming pure sinusoidal

waveforms, the analysis of an inverter connected to the grid made by using an equivalent linear circuit, The single phase

difference between the two voltage sources is represented by, and the current flowing through the inductor can be obtained

by where is the voltage generated by the voltage inverter and is the single phase AC grid voltage. The reactive power

delivered Experimental current waveforms of the generator. To the grid is then obtained by multiplying the current by the

voltage of the grid.

Fuzzy Logic Controller based Wind Energy Conversion System 289

Figure 1: Schematic of Proposed Renewable Based Distributed Generation System

Harmonic distortion can be generated several factors in the gird voltage, but two of them are responsible for most

of it, one is the pollution present on the grid and the second is related to the inverter itself. Although, SVPWM is an

advanced and efficient technique the use of gate signal given to the inverter, there are some hardware related issues that

cause harmonic distortion. The switching action in power IGBTs results in unavoidable losses that are related to the

capacitances of the switching device. Moreover, as a consequence of these capacitances low loess to the network, IGBTs

do not have an instantaneous turn-off and take longer to turn off than to turn on. Thus, it becomes necessary to insert a

dead-time between the transistors’ switching to prevent shoot-through failures on the inverter legs.

A result of these dead times

(1)

Where is the mutual inductance between armature and fieldwinding, and are the armature and field

resistances respectively, is the field voltage, is the armature self-inductanceand is the armature voltage. The power

versus speed curves for different armature voltages for the DC Generator. The behavior of the DC generator is similar to a

wind speed, where is analog to the wind speed. In PMSG’swith surface mountedmagnets, the rotor can be considered

magneticallyround and the stator and axis flux linkages can be expressed as

(2)

(3)

Where is the stator inductance and it represents both leakage and mutual inductances, the voltage equations of the

machine are given by

(4)


(5)

Where is the stator resistance and are theflux linkages respectively.The electromagnetic torque for a

Generator is expressed as follows

(6)

The simulation includes the Generator, and the grid connected SVPWM inverter along with its correspondent

controls. The system was simulated using different armature voltages on the DC motor to represent random weather

conditions. The active and reactive powers for these conditions as it can be seen, the system shows stable dynamic

response. The current and voltage waveforms delivered to the electric grid are illustrated in; the zero-crossing distortion

observed in the current is obtained as a consequence of the dead time present on the transistor switching and is further

discussed on the next section.

PHASE-LOCKED-LOOP

All PLL algorithms have three main sections, namely a phase detector, filter and voltage controlled oscillator.

Most PLL structures usually differ on the phase detector. The instantaneous reactive power theory was originally proposed

for three-phase applications. Nevertheless, it is possible to implement it in a single-phase PLL structure with some

modifications. This approach is based on a simple transport delay in order to create a fictitious two phase system than can

be studied under the stationary reference frame

Figure 2: Phase-Locked-Loop Structure

This PLL structure is based on keeping the fictitious power of the two phase system equal to zero. The

instantaneous power of the fictitious two-phase system is given by

= (7)

Where , , , and are the fictitious voltage and currents of the two-phase system and are defined as

(8)

(9)


(10)

(11)

(12)

(13)

(14)

Once the output signal has reached the same frequency as the input signal, will become a DC value. Thus, an

orthogonal signal with the electric grid voltage is effectively achieved. However, the effect of a fixed time transport delay

will cause that the PLL lacks of capacity to lock under frequency changes because the delay will not correspond to radians

anymore, and will no longer be valid. For this reason, a modified lag structure based on an integral and a low pass filter is

proposed. This scheme is proposed as an alternative to the Hilbert transform for low computational cost. The delay

structure keeps a fixed phase of 90 degrees for frequencies over the PLL central frequency. The magnitude for the PLL

central frequency will be equal to 0, therefore the system will have lock capacity for changes in frequency.

FUZZY CONTROL SYSTEM

The fuzzy controller has four main components (i) the “rule-base” holds the knowledge, in the form of a set of

rules, of how best to control the system (ii) the inference mechanism evaluates which control rules are relevant at the

current time and then decides what the input to the plant should be (iii) the fuzzification interface simply modifies the

inputs so that they can be interpreted and compared to the rules in the rule-base and (iv) the defuzzification interface

converts the conclusions reached by the inference mechanism into the inputs to the plant . In our MPPT controller, we use

the error between reference speed and the real rotor speed and the change of this error as inputs. Output is the duty cycle of

the first boost converter.

Fuzzy logic is widely used in machine control. The logic involved can deal with concepts that cannot be expressed

as "true" or "false" but rather as "partially true". Although genetic algorithms and neural networks can perform just as well

as fuzzy logic in many cases, fuzzy logic has the advantage that the solution to the problem can be cast in terms that human

operators can understand, so that their experience can be used in the design of the controller. This makes it easier to

mechanize tasks that are already successfully performed by humans. A fuzzy control system is a control system based on

logical mathematical system that analyzes analog input values in terms of logical variables that take on continuous values

between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0.

Figure 3: Block Diagram of Fuzzy Logic Controller

This paper focuses on fuzzy logic control based on sugeno system. This system has four main parts. First, using

input membership functions, inputs are fuzzified then based on rule bases and inference system, outputs are produced and

finally the fuzzy outputs are defuzzified and applied to the main control system. Error of inputs from is chosen as input. To

avoid miscalculations due to fluctuations in wind speed and the effects of noise on data, triangular membership functions


are chosen to have smooth and constant region in the main points. The expressive power and interpretability of Mamdani

output is lost in the Sugeno FIS since the consequents of the rules are not fuzzy. But Sugeno has better processing time

since the weighted average replace the time consuming defuzzification process. Due to the interpretable and intuitive

nature of the rule base, Mamdani-type FIS is widely used in particular for decision support application. Other differences

are that Mamdani FIS has output membership functions whereas Sugeno FIS has no output membership functions.

Mamdani FIS is less flexible in system design in comparison to Sugeno FIS as latter can be integrated with ANFIS tool to

optimize the outputs.

PARAMETER SELECTION THIS PAPER

Table 1: Parameter of Wind Turbine

Parameter Value

Nominal mechanical output power(W) 8.5 Base power of the electrical generator (VA) 9.44 Wind speed (M/S) 12 Maximum power at base wind speed (Per unit of nominal mechanical power) 0.8

Table 2: Parameter of PMSG

Parameter Value

Stator phase resistance Rs (OHM) 0.425 Inductances [ Ld(H) Lq(H) ] [0.0082 0.0082] Flux linkage established by magnets (V.s) 0.433 Voltage Constant (V_peak L-L / krpm) 392.68 Torque Constant (N.m / A_peak) 3.2475 Inertia, friction factor and pole pairs [ J(kg.m^2) F(N.m.s) p() ] [0.01197 0.001189 5]

SIMULATION RESULTS

The full-power handling WECS topology emulated in this paper is depicted in Fig.4 In this system, a DC motor

emulates the wind by driving a PMSG in a similar way a wind turbine would do. In order to have a similar characteristic of

the power versus rotational speed to a wind generator, the dc motor Fig. 4. Topology of the WECS.

Figure 4: Output Characteristics of Wind Generator


Figure 5: Output Waveform of Wind Generator

Theoretical power curves for different armature voltages. Field windings are fed independently; the steady-state

mechanical power curve for such a machine is given by. The waveforms of Fig. 5. Simulation results (a) Line Current, (b)

Line Voltage, (c) RMS Line Voltage, (d) RMS Line Current, (e) Average Power, (f) Rotor Speed.

Figure 6: Torque Fluctuation

The waveforms of Fig. 6 Simulation result Turbine Generator shaft is shown in the following graph, by

applications of fuzzy controller the torque fluctuation is reduced 1sec. A RES with variable output power is connected on

the dc-link of grid-interfacing inverter. An unbalanced single phase nonlinear load, whose unbalance, harmonics, and

reactive power need to be compensated, is connected on PCC. The waveforms of Fig. 7. Simulation results (a) Grid

voltages, (b) Grid Currents. Grid voltage, grid currents, unbalanced load current and inverter current.

Figure 7: Output Wave Form of the Generator Side


The corresponding active-reactive powers of grid load and inverter Positive values of grid active-reactive powers

and inverter active-reactive powers imply that these powers flow from grid side towards PCC and from inverter towards

PCC, respectively. The active and reactive powers absorbed by the load are denoted by positive signs

Figure 8: Output Wave Form of the Load Side

The voltage and current waveforms between the converter and transformer are shown in Fig. 4. The dynamic

behavior under speed change. The output of the converter is shown in Fig. 8, the generated signal has a THD lower than

0.52%.

Figure 9: Source Current Waveform of the Test System (Without Controller)

Figure 10: FFT Analysis of Source Current Waveform of the Test System (Without Controller)


The Fig. 7 shows the corresponding FFT analysis waveform. From FFT analysis, it is observed that the Total

Harmonic Distortion (THD) of the source current waveform of the test system without inverter is 36.62%. Fig. 8 shows the

source current waveform of the test system with fuzzy logic controller based inverter and the Fig. 8 shows the

corresponding FFT analysis waveform. From FFT analysis, it is observed that the THD of the source current waveform of

the test system with fuzzy logic controller based Inverter is 0.52 %.

Figure 11: Source Current Waveform of the Test System with Fuzzy Logic Controller

Figure 12: FFT Analysis Source Current Waveform of the Test System with Fuzzy Logic Controller

CONCLUSIONS

This paper has presented a novel control of an existing grid interfacing inverter to improve the quality of power at

single phase load connection. The grid-interfacing inverter can be effectively utilized for power conditioning without

affecting its normal operation of real power transfer. The design simulation and implementation of WECS based on a low

cost fuzzy logic controller and a modified PLL have been presented on this paper. In this paper fuzzy logic controller based

inverter (IGBT) is presented for grid connected Wind Energy Generating System. The proposed FLC have improved the

power quality of source current significantly by reducing the THD from 36.62% to0.52 %. It is clearly presented with FLC

gives better performance than DSC controller.


REFERENCES

1. P. Mukund, “Wind power,” in Wind, Solar Power Systems Design, Analysis and Operation, 2nd ed. Boca Raton,

FL: Taylor & Francis, 2006.

2. Mukhtiar Singh “Grid Interconnection of Renewable Energy Sources at the Distribution Level with Power-

Quality Improvement Features,” IEEE transactions on power delivery, vol. 26, no. 1, January 2011.

3. F. A. Ramirez,” Development of a Grid-Connected Wind GenerationSystem with a Modified PLL Structure”

IEEE transactions on sustainable energy, vol. 3, no. 3, July 2012.

4. SoerenBaekhoejKjaer, “A Review of Single-Phase Grid-Connected Inverters for Photovoltaic Modules,” IEEE

transactions on industry applications, vol. 41, no. 5, September/October 2005.

5. Remus Teodorescu, “Flexible Control of Small Wind Turbines With Grid Failure Detection Operating in Stand-

Alone and Grid-Connected Mode,” IEEE transactions on power electronics, vol. 19, no. 5, September 2004.

6. AnshumanShukla, L. G. Tranquillo, J. T. Bialasiewicz, E. Galvan, R. C. Portillo Guisado, M. Á. Martín Prats, J. I.

León, and N. Moreno Alfonso, “Power-electronic systems for the grid integration of renewable energy sources: A

survey,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1002–1016, Feb. 2006,

7. Shuhui Li “Optimal and Direct-Current Vector Control of Direct- Driven PMSG Wind Turbines,” IEEE

transactions on power electronics, vol. 27, no. 5, may 2012.

8. Y. Mishra, “Coordinated Tuning of DFIG-Based Wind Turbinesand Batteries Using Bacteria Foraging

Techniquefor Maintaining Constant Grid Power Output” IEEE systems journal, vol. 6, no. 1, March 2012,

9. B. K. Bose, Modern Power Electronics and AC Drives, 1st ed. EnglewoodCliffs, NJ: Prentice Hall, 2002.

10. Florin Iov and M. R. Iravani, “A method for synchronization of power electronic converters in polluted and

variable-frequency environments,” IEEE Trans. Power Syst., vol. 19, no. 3, pp. 1 1263–1270, Aug. 2004,

11. Eftichioskoutroulis and Kostas kalaitzakis, “Design of a maximum power tracking systems for wind –energy

conversion Applications” IEEE transactions on sustainable energy, vol. 53, no. 2, April 2006.

12. Thiringer T.: “Power Quality Measurments Performed on a Low-Voltage Grid Equipped with Two Wind

Turbines”. IEEE Trans. on Energy Conversion, vol. 11, September 1996, pp. 601-606.

13. Chompoo-inwai Ch., Wei-Jen L., Fuangfoo P., Williams M., Liao J. R.: “System Impact Study for the

Interconnection of Wind Generation and Utility System”. IEEE Trnas.on Industry Applications, vol. 41, January

2005, pp 163-168.

14. Chen Z., Spooner E.: “Grid Power Quality with Variable Speed Wind Turbine”, IEEE Transaction on Energy

Conversion, vol. 16, No. 2, pp. 148-154, 2001.

15. M. Karimi-Ghartemani and M. R. Iravani, “A method for synchronization of power electronic converters in

polluted and variable-frequency environments,” IEEE Trans. Power Syst., vol. 19, no. 3, pp. 1263–1270, Aug.

2004.

fuzzy logic controller based wind energy conversion...

Documents