Sensorless frequency-converter-based methods for LCC efficient pump and

fan systems

Tero AhonenLappeenranta University of

TechnologyFinland

tero.ahonen@lut.fi

Outline of the presentation

I. Role of a frequency converter in pump and fan systems

II. Sensorless estimation of the system operational state

III. Identification of system characteristics

IV. Energy efficient system control with variable-speed usage

V. Identification of operational states that reduce the system lifetime or cause an immediate failure

Part I: Role of a frequency converter in pump and fan systems

0 10 20 30 40 500

5

10

15

20

25

30

35

Flow rate (l/s)

Hea

d (m

)

20

40

60

80

10015% 44%

60%

71%73%

68%

58%

Es

(Wh/m3)

1160 rpm870 rpm

1450 rpm

High pump efficiency & poor system efficiency

Poor pump efficiency & high system efficiency

Frequency converter allows variable-speed system operation

Frequency converter as a sensorlessmeasurement unit

– Converters with internal current and voltage measurements can provide estimates for• Rotational speed, shaft torque and

power• Motor current and temperature• System energy consumption• System run-time

– These can be used to determine• System operational state (flow rate,

head, efficiency)• System energy efficiency Es (kWh/m3)• Distribution of flow rate, Es etc.

Estimation accuracy of a DTC frequency converter

Ref.: T. Ahonen et al., “Accuracy study of frequency converter estimates used in the sensorless diagnostics of induction-motor-driven systems,” in Proc. EPE 2011

Conf., pp. 1–10.

Speed estimation errors within 3.1 rpm (0.2 %) Power estimation errors within 0.77 kW (2.1 %)

37 kW, 1480 rpm induction motor Torque estimation errors within 4.9 Nm (2.1 %)

Investment7 %

Energy75 %

Maintenance 4 %

Production losses 14 %

Life-cycle costs in pump and fan systems dominated by energy, bounded by design

Investment 5 %

Energy58 %

Maintenance6 %

Production losses 31 %

Role of a frequency converter in LCC efficient pump and fan systems

.

IV.

II.

V.

III.

Part II: Sensorless estimation of the system operational state

Sensorless system state estimation by a frequency converter

QP-curve-basedpump modelnest

Pest

Qest

Es,est

Hest

The model provides estimates for the system operational state and energy efficiency that can be further used for process identification and energy efficiency optimization.

A parameter-adjustable pump or fan model can be implemented into the converter control scheme. QP and QH curves

at nnom

QP-curve-based pump model

QP-curve-basedpump modelnest

Pest

Qest

Es,est

HestQP and QH curvesat nnom

0 10 20 30 400

10

20

30

Flow rate (l/s)

Hea

d (m

)

QH curve

Hest

Qest0 10 20 30 40

0

2

4

6

8

Flow rate (l/s)

Pow

er (

kW)

QP curve

nnom

nestQest

Pest

Ref.: T. Ahonen et al., “Estimation of pump operational state with model-based methods,” Energy Conversion and Management, vol. 51, no. 6, pp. 1319–1325,

June 2010.

Operation of a QP-curve-based pump model

Affinity transform (1) for the shaft power estimate

nest

Pest

Es,est

Flow rate interpolation from

the QP curve

Qest,n Head interpolation from

the QH curve

Affinity transform (3) for the head

estimate

Affinity transform (2) for the flow rate estimate

Pest,n

Qest,n Hest,n

Qest Hest

Estimation (4) of specific energy consumption

Pest

Qestest

estests,

est

2

est

nomnest,

estest

nomnest,

est

3

est

nomnest,

:)4(

:)3(

:)2(

:(1)

QPE

HnnH

QnnQ

PnnP

Estimation accuracy of the QP-curve-based pump model

50% flow rate 100% flow rate 140% flow rate| Q/Q| < 8% | Q/Q| < 6% | Q/Q| < 20% at 1320/1560 rpm

Pilot test results for an industrial pulp pump system

30-40 50-60 70-80 90-100 110-1200

10

20

30

40

Relative flow rate (%)

Tim

e (%

)

• Sulzer ARP54-400 centrifugal pump• Typical pump eff. 76%, max. eff. 85%• Typical pump power cons. 240 kW• 9% efficiency improvement equals

25kW lower power consumption

Part III: Identification of system characteristics

0 10 20 30 400

5

10

15

20

25

Flow rate (l/s)

Hea

d (m

)

nnomnestHprocess

Hst

k Q2

Q, H

Surrounding system characteristics

2st QkHH

pdt

tots

HgQ

PE

stmin,s HgEMinimum level of energy usage:

– Pump/fan operating point lies in the intersection of the device and surrounding process characteristic curves

Ref.: T. Ahonen et al., “Generic unit process functions set for pumping systems,” in Proc. EEMODS 2011 Conf.

0 10 20 30 400

5

10

15

20

25

Flow rate (l/s)

Hea

d (m

)

nnomn

estH

process

Hst

k Q2

Hest

Qest

State estimation with the process-curve-based pump model

Process-curve-basedpump modelnest

PestQH curve at nnom,

Hst and k

Qest

Hest

Ref.: T. Ahonen et al., “Estimation of pump operational state with model-based methods,” Energy Conversion and Management, vol. 51, no. 6, pp. 1319–1325,

June 2010.

0 10 20 30 400

5

10

15

20

25

30

Flow rate (l/s)

Hea

d (m

)

QP estimatesEst. system curve

Identification of the surrounding system

– Hst and k can be determined with applicable QP curve model estimates (Qest, Hest) using the LMS method

2

1

2est,stest,

2stprocess

)(m

iii QkHHS

QkHHFind best-fit valuesfor Hst and k withthese estimates

Ref.: T. Ahonen et al., “Frequency-converter-based hybrid estimation method for the centrifugal pump operational state,” IEEE Trans. Ind. Electron., vol. 59, no. 12,

pp. 4803–4809, December 2012.

Combined (hybrid) usage of the pump models

– Process-curve-based model is used when operating on the flat part of the QP curve

QP curve modelis used

Process curvemodel is used

Process curvemodel is used

Estimation accuracy of the process-curve-based pump model

0 10 20 30 40 500

5

10

15

20

25

30

Flow rate (l/s)

Hea

d (m

)

35 %

65 %70 %

68 %

60 %

600 rpm

1400 rpmMeas.QP est.Hprocess

550 750 950 1150 1350 1550-1

0

1

2

3

4

5

6

7

Rotational speed (rpm)

Flow

rate

est

imat

ion

erro

r (l/s

)

QP curve estimationProcess curve est.

– Process curve parameters were determined with 11 QPcurve model estimates at 1160–1620 rpm

Part IV: Energy efficient system control with variable-speed usage

0 20 40 600

100

200

300

400

500

600

700

Head (m)

Sys

tem

Es (W

h/m

3 )

overall eff.0.3

0.4

0.7

1.0

0.5

How to achieve the minimum energy consumption?

1. Drive the system as energy efficiently as possible2. Minimize the hydraulic losses (Hst, k)3. Use high efficiency components in the system

800 1000 1200 140020

40

60

80

100

X: 815Y: 26.71

Rotational speed (rpm)

Spe

cific

ene

rgy

cons

umpt

ion

(Wh/

m3 ) Hst=5-10 m, k=0.0149

X: 1155Y: 53.41

6.5 m

5 m8 m

10 m

Ref.: T. Ahonen et al., “Generic unit process functions set for pumping systems,” in Proc. EEMODS 2011 Conf.

Energy efficient filling of a reservoir with a variable-speed pumping system

Hst,2

k

Hst,1

Hst,2

H

Q

k

Hst,1

LH

LL

k

– Variable-speed operation allows energy efficient filling of a reservoir, but at which rotational speeds?

800 1000 1200 140020

40

60

80

100

X: 815Y: 26.71

Rotational speed (rpm)

Spe

cific

ene

rgy

cons

umpt

ion

(Wh/

m3 ) Hst=5-10 m, k=0.0149

X: 1155Y : 53.41

6.5 m

5 m8 m

10 m

5 6.25 7.5 8.75 10800

900

1000

1100

1200

System static head (m)

Opt

imum

rota

tiona

l spe

ed (r

pm)

st

System identification and determination of an optimum rotational speed profile

Hst,1

Hst,2

H

Q

k

k

1.

2.

Test nest

• Surrounding system and the pumping task are identified during the first run

• Es charts (curves) are determined for Hst,1 and Hst,2 cases

• Rotational speed profile is formed with the Es chart information

Simulation results for a reservoir filling application

• System: Hst=5-10 m, k=0.0149, 3.75 m3 per reservoir filling

• Pump: Sulzer APP22-80, 1450 rpm• Minimum energy consumption is

achieved with the linear speed profile

Compensation of an oversized pump with variable-speed usage

• Pump: Sulzer APP22-80 with a larger impeller resulting in a 5-10 % higher output than needed

• Difference in Es is at smallest around1050 rpm

Part V: Identification of operational states that reduce the system lifetime

or cause an immediate failure

Pump cavitation and fluid recirculation, fan stalling

1557

1558

1559

1560

1561

1562

1563

0,0 1,0 2,0 3,0 4,0 5,0

n(rp

m)

Time (s)

-2,0

-1,0

0,0

1,0

2,0

0,0 1,0 2,0 3,0 4,0 5,0

Sca

led

T(%

of T

nom

)

Time (s)

No cavitation (BEP)Cavitation (max. flow)

Frequency-converter-based, sensorlessdetection of cavitation or stalling

1557

1558

1559

1560

1561

1562

1563

Cavitation (max. flow)Average speed at max. flow

)()(

)()()(

)()(

ACRMS

DCAC

DC

1

0

2

1

0

1

1

M

k

M

k

knxM

nx

nxnxnx

knxM

nx

Determine nRMS,N

and TRMS,N when the pump operates near

the BEP

Calculate the present nRMS

and TRMS

Is TRMS/TRMS,N over the threshold value?

Is nRMS/nRMS,N over the threshold value?

Pump is operating normally

Cavitation may occur in

the pump

Yes Yes

NoNo

Ref.: T. Ahonen et al., “Novel method for detecting cavitation in centrifugal pump with frequency converter,” Insight, vol. 53, no. 8, August 2011.

Test results

5 10 15 20 25 30 35 40012

45

Flow rate (l/s)

n RMS/n

RMS,

N

Serlachius 1500 rpm

5 10 15 20 25 30 35 40012468

Flow rate (l/s)

T RMS/T

RMS,

N

0 5 10 15 20 25 30 35 40 45012

45

Flow rate (l/s)

n RMS/n

RMS,

N

Sulzer 1450 rpm

0 5 10 15 20 25 30 35 40 45012

45

Flow rate (l/s)

T RMS/T

RMS,

N

Detection of contamination in a fan impeller

0 5 10 15 20 25 300

20

40

60

80

100

120

Time (s)

Torq

ue (%

)R

otat

iona

l spe

ed (%

)

TorqueClean impellerContaminated impeller

Ref.: J. Tamminen et al., “Detection of Mass Increase in a Fan Impeller with a Frequency Converter,” IEEE Trans. on Ind. Electron., Digital Object Identifier:

10.1109/TIE.2012.2207657, 2012.

– Impeller contamination is a root cause for several imbalance- and vibration-related faults in fans

Operation of the fan impeller contamination detection algorithm

Starting the fan with constant

torque ref.

Calculation of the fan acceleration

with (1)

est,1

Calculation of mass increase

with (2) and (3)

Baselinemeasurement

mest

Tref,1

Starting the fan with constant

torque ref.

Calculation of the fan acceleration

with (1)

est,n

nest,n

Tref,nnth detectionmeasurement

nest,1

Filtering and processing of

estimates

n1

Filtering and processing of

estimates

nn

22

21

Fanest

est,1

ref,1

nest,

nref,Fan

lowerlimit,upperlimit,est

2:)3(

:)2(

:(1)

rrJm

TTJ

tnn

Test results

Ref.: J. Tamminen et al., “Detection of mass increase in a fan impeller with a frequency converter,” IEEE Trans. on Ind. Electron., Digital Object Identifier:

10.1109/TIE.2012.2207657, 2012.

– Method has been successfully verified by laboratory measurements

1 2 30

40

80

120

160

Measurement set

Mas

s (g

)

Actual massEst. mass Tref=30%

Summary

Investment7 %

Energy75 %

Maintenance 4 %

Production losses 14 %

Energy efficiency optimization:

parallel pumping, compensation of

oversizing, optimal speed profiles, etc.

Role of the frequency converter in LCC optimization of pump and fan systemsDetection of operational

states degrading system lifetime:

cavitation, stalling,

recirculation, dry-running,

contamination build-up, etc.

System performance monitoring:

On-line specific energy consumption estimation, wear and

air filter contamination detection, etc.

Main points

I. Frequency converter is a versatile tool with monitoring and diagnostic abilities

II. Sensorless pump and fan system operation estimation is possible with dedicated models

III. Frequency converter by itself does not realize energy efficient system operation

IV. Identification of adverse operational states can effectively decrease the risk of system faults