7 low-cost power tracking controller turbine

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A Low Cost Power-Tracking Controller for a Small Vertical Axis Wind Turbine

M.A. Parker(1)

, P.J. Tavner(1)

, L. Ran(1)

, A. Wilson(2)

(1) Durham University, UK (2) NaREC, UK

ABSTRACT

The paper presents a low cost power electronic solution for a system consisting of a savonius-type wind turbine

with a direct-drive permanent magnet generator, for battery charging applications. The converter increases the

energy capture at low wind speeds and features encoderless peak power tracking capabilities. To reduce cost the

converter only operates at the low speed range, where the generator EMF is less than the battery voltage, above

this the output of the rectifier is directly connected to the battery. The reduction in extracted energy compared to

a converter operating over the full range is small, especially when the average wind speed is low.

INTRODUCTION

In recent years significant attention has been paid to the

grid connection of small scale renewable energy

systems [1]. However battery connection still remains

important, particularly in remote areas where there is nogrid available. Typically, a permanent magnet

synchronous generator producing 3-phase AC is

connected to a rectifier, and the resulting DC voltage

used to charge the battery. At high wind speeds the

fixed battery voltage limits the generator speed, so the

wind turbine operates below the optimum speed for

peak power tracking. At relatively low wind speeds,which is often the case at many residential locations, the

need to generate sufficient voltage to charge the battery

means that the turbine has to operate above optimum

speed. Power electronics can be used to vary the load

voltage seen by the generator in order for the turbine torun at the optimum speed, but this can often be

expensive.

Current System

The wind turbine is shown in Figure 1. It will extract

166W in 9m/s wind, and rotating at the optimum speed

of 216RPM. It is a drag-based turbine, so it will

generate torque from rest and can self-start. Themaximum tip speed ratio is low at 1.1, so the turbine

speed does not need to be limited to prevent damage.

The turbine features a large flywheel with a total inertia

of 7.9Nms/rad.

The generator is of a direct-drive axial-flux design,

similar to [2] but air cored. There are 12 coils, in 3

phases, and for this application the 4 coils per phase are

connected in parallel. A summary is given in Table 1

below.

Table 1 Generator Characteristics

Number of coils 12

Number of pole pairs 8

Coil inductance 4mH

Coil resistance 1

Maximum current per coil 3A

Figure 1 The Savonius Wind Turbine

MODELLING THE SYSTEM

The purpose of modelling the system was to find the

most efficient operating point for different wind speeds,

and to calculate the power curve the curve of power

output against wind speed. For all modelling the

generator was represented as sinusoidal EMFs in series

with inductance and resistance for each phase, with theEMF being proportional to the rotation speed. The

Presented at the 40th UniversitiesPower Engineering Conference,Cork, 2005


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mechanical power extracted by the turbine is given by

equation 1.

2

3AU

CP wpmech

= (1)

where Pmech is the mechanical power extracted, Cp the

turbine coefficient of performance, Uw the wind speed,

A the turbine area and the air density. Cp is a functionof the ratio of the speed of the blade tips to the wind

speed, , and is shown for this turbine in Figure 2.

0

0.05

0.1

0.15

0.2

0.25

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

Tip speed ratio

Coefficientofperformance

Figure 2 Turbine Cp-Lambda Curve

Modelling was carried out using Simplorer. Thegenerator model was connected to a 3-phase diode

bridge, with the DC side of the rectifier connected to a

Thevenin-type voltage source to represent the DC load.

The simulation was run for a simulation time of 10s, to

allow the turbine speed to stabilise at the steady statevalue, and at the end of the time the power output and

other variables were recorded. This was carried out atDC voltages between 10V and 100V and wind speeds

between 1 and 20m/s, with a step of 1V and 1m/s

respectively. This required 1820 runs, which were set up

and performed automatically.

The results of the simulations were processed using

Matlab, to smooth out and interpolate the curves. For

each wind speed, the maximum electrical power was

found along with the other parameters at this power.

The electrical power was also calculated for a DC

voltage of 48V, to simulate the effects of connecting the

output of the rectifier directly to the battery.

Simulation Results

The simulated power curve is shown in Figure 3 below.

It can be seen that the battery connection produces a

similar power to the maximum, at wind speeds above

9m/s. Below this speed the turbine needs to turn much

faster than the optimum speed in order to generate

sufficient EMF to charge the batteries. At high windspeeds the fixed voltage limits the speed of the

generator, resulting in operation below the optimum

rotation speed.

0.0

50.0

100.0

150.0

200.0

250.0

2 3 4 5 6 7 8 9 10

Wind speed (m/s)

E

lectricalpower(W)

Maximum power Battery connection Figure 3 Simulated Power Curve

3. PROPOSED SOLUTION

It can be seen in Figure 3 that a direct battery

connection, as initially proposed, results in a good

tracking of the maximum power curve at wind speedsabove 8m/s. It is only below 8m/s where energy capture

efficiency is low. It is therefore proposed to use a boost-

type DC-DC converter to boost the DC output of the

rectifier at low wind speeds so that the generator will

operate at the optimum speed. Above 8-9m/s the

converter will not switch, and the current will be carriedby the boost diode, which will have to be rated

appropriately. This represents a cost saving over a

converter rated across the entire range as the switching

transistor and smoothing capacitor will have a much

smaller rating and can be significantly cheaper. A

further cost saving is achieved by using the generatorinductance as the inductance in the boost converter. The

system is shown in Figure 4.

Figure 4 Proposed System

Peak power tracking algorithm

The best control method would be to set the turbinerotation speed based on the wind speed, but this requires

measurement of both variables, increasing the cost of

the system. An alternative system, frequently used in

small scale systems, is to assume that the turbine isrotating at the ideal speed and extract power accordingly

[3]. If the turbine is rotating at less than the ideal speed

then it will speed up towards the ideal, the reverse

occurring if at greater than ideal speed. This is easiest to

implement by controlling the converter to operate at a

fixed I-V relationship. Current and Voltage

measurement points are shown in Figure 4 and the

relationship in Figure 5.

The controller was implemented using a PIC16F876

microcontroller. This is only 8bit and has no

IDC

VDC

48V

DC


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multiplication instructions, but it features a built in

ADC and PWM, and is significantly cheaper than more

powerful devices such as a DSP. The I-V curve is

implemented as a quadratic relationship between V and

I derived from simulations. A learning system could be

implemented to learn the maximum power

characteristic, which could vary over time. An

appropriate algorithm is described in [4].

4. TESTING AND EVALUATON

The generator and power converter were tested in the

laboratory to verify the operation of the I-V control

system. The generator was driven at different speeds bya motor, and for each speed the average current and

voltage at the DC side of the rectifier were recorded and

compared with the desired relationship. The results are

shown in Figure 5.

0

0.5

1

1.5

2

2.5

3

3.5

10 20 30 40 50

Smoothed DC Voltage

DCCurrent

Measured Idc Desired Idc

Figure 5 I-V control testing

Simulation of Complete System

Due to a savonius turbine being unavailable for testing,

the efficiency of the tracking algorithm had to be

simulated. This was carried out using Simulink, and the

SimPowerSystems block set To keep the simulation

time down, the DC-DC converter was not simulated,

and was instead represented as a thevenin source as in

the earlier simulation. The voltage of the source was

varied in the same way as the duty cycle of the

converter in the actual implementation. Wind speed data

was generated randomly in Matlab, using a recognised

technique, and read from a table in the Simulink model.

The model was simulated for a simulation time of 5

minutes, for average wind speeds of 5, 7 and 9m/s, and

for connection with and without the converter and

theoretical maximum power.

Results for the 5m/s average wind speed are shown in

Figure 6 below. Average powers for all three wind

speeds are summarised in Table 2. It can be seen that

the inertia of the turbine prevents it tracking the ideal

rotation speed as this varies too quickly. However it can

be seen in Table 2 that the extracted power is only

slightly less than the maximum power, and it is also

much more constant.

Table 2 Simulated Power Extraction

Average power (W)

5m/s

wind

7m/s

wind

9m/s

wind

Maximum 27.9 74.7 162.4

Connection throughconverter

25.6 71.5 157.3

Direct connection 8.2 64.2 157.2

Estimated Annual Energy Capture

The probability distribution of wind speeds can be

calculated using a Rayleigh distribution. The probabilitythat the wind speed is greater than a value U is given by

equation 2 below:

=

2

4exp)(

U

UUF

(2)

where U is the average wind speed. From this theprobability distribution can be calculated, and this is

multiplied by the power curves shown in Figure 3 in

order to obtain the power density. The power density for

an average wind speed of 5m/s (typical in the Durham

area [5]) is shown in Figure 7 below. The total annual

extraction for several wind speeds is shown in Table 3and a comparison of the partially rated converter and

battery connection in Table 4.The power curves assume

the turbine can react instantly to changes in the wind

speed.

Figure 6 Power Tracking at 5m/s Wind Speed


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Table 3 Annual Energy Capture

Annual energy capture (kWh)Average wind

speed (m/s) Battery Part Converter Full Converter Maximum

5 371 451 453 454

6 690 762 771 779

7 1083 1144 1168 1229

9 1848 1891 1954 2520

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5 6 7 8 9 1 0 11 12 13 14 15 16 17 18 19 20

Wind Speed

PowerDensity(W)

Maximum Converter Battery

Figure 7 Energy Capture at 5m/s Wind speed

It can be seen that energy capture is improved at lowaverage wind speeds, although the result is less

significant at higher speeds. At higher wind speeds there

is a significant difference between the extracted energy

with a converter and the theoretical maximum. This is

mostly due to the generator being cut out above 18m/s

wind speed. The fully rated converter improves on theenergy capture further, especially at higher average

wind speeds. However this will significantly increase

the cost.

Table 4 Converter vs. Battery Connection

Partially rated converterAverage

wind speed

(m/s)Increase over

battery (kWh)

Percentage

increase

5 80 21.6%

6 72 10.4%

7 61 5.6%

9 43 2.3%

CONCLUSIONS

This work has shown the following:

A low cost power tracking converter has beendesigned to operate with an axial flux permanent

magnet generator driven by a Savonius Wind

Turbine.

The converter allows more energy to be extractedfrom the wind than a simple passive rectifier.

The energy saving depends on the wind resourceand therefore the turbine site but is greater at lower

wind speeds.

A power tracking controller does not necessarilyincrease the cost of a wind turbine converter.

A partially rated converter can extract a similarlevel of energy to a fully rated converter at low

wind speeds, and is cheaper.

The generator design is significant as it affects thewind speed at which the converter cuts out. A

higher cut out speed will result in better

performance at high wind speeds but also a more

expensive converter.

REFERENCES

1. F. Blaabjerg, Z. Chen, S. Baekhoej Kjaer, PowerElectronics as Efficient Interface in Dispersed Power

Generation Systems, IEEE Trans. Power Electronics,

Vol.19, No.5, pp.1184-1194, 2004.

2. J.R. Bumby, R. Martin, M.A Mueller, E. Spooner,N.L. Brown, B.J. Chalmers, Electromagnetic design of

axial-flux permanent magnet machines, IEE Proc.-

Electr. Power Appl., Vol. 151, No. 2, pp. 151-160,

March 2004.

3. H. Polinder, G.J.W. van Bussel, M.R. Dubois,Design of a PM generator for the Turby, a wind turbine

for the built environment, Int. Conf. ICEM, pp. 432

438, Cracow, Poland, Sept 2004.

4. Q. Wang, L. Chang, An Intelligent MaximumPower Extraction Algorithm for Inverter-Based

Variable Speed Wind Turbine Systems, IEEE Trans.

Power Electronics, Vol.19, No.5, pp.1242-1249, 2004.

5. DTI wind speed database, at:http://www4.dti.gov.uk/energy/renewables/technologies

/windspeed

AUTHORS ADDRESS

The first author can be contacted at

School of Engineering,

University of Durham,

South Road,Durham,

UK,

DH1 3LE

Email [email protected]

7 low-cost power tracking controller turbine

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