Sensorless frequencyconverter-based methods for LCC efficient pump and fan systems Tero Ahonen Lappeenranta University of Technology Finland
[email protected]
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
Frequency converter allows variablespeed system operation High pump efficiency & poor system efficiency
35 30
15%
44%
100
60% 71%
Head (m)
25
80
73%
20
68% 58%
15
40
10
1450 rpm
20
1160 rpm
5 0 0
60
870 rpm
10
20 30 Flow rate (l/s)
Poor pump efficiency & high system efficiency
40
Es
50 (Wh/m3)
Frequency converter as a sensorless measurement 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 37 kW, 1480 rpm induction motor
Torque estimation errors within 4.9 Nm (2.1 %)
Speed estimation errors within 3.1 rpm (0.2 %)
Power estimation errors within 0.77 kW (2.1 %)
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.
Life-cycle costs in pump and fan systems dominated by energy, bounded by design Production losses 14 % Maintenance 4%
Investment 7%
Energy 75 % Production losses 31 %
Maintenance 6%
Investment 5%
Energy 58 %
Role of a frequency converter in LCC efficient pump and fan systems
V.
IV. .
III.
II.
Part II: Sensorless estimation of the system operational state
Sensorless system state estimation by a frequency converter
A parameter-adjustable pump or fan model can be implemented into the converter control scheme.
nest
QP-curve-based pump model
Pest
QP and QH curves at nnom
Qest Hest Es,est
The model provides estimates for the system operational state and energy efficiency that can be further used for process identification and energy efficiency optimization.
QP-curve-based pump model
Pest
QP and QH curves at nnom
QP curve
6 4P est
n
2 0 0
Qest 10
20 30 Flow rate (l/s)
Qest Hest Es,est
QH curve
30
n
nom est
40
Head (m)
8 Power (kW)
nest
QP-curve-based pump model
20 10
Hest
0 0
Qest 10
20 30 Flow rate (l/s)
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.
40
Operation of a QP-curve-based pump model nest Pest
Affinity transform (1) for the shaft power estimate
Pest,n
Flow rate interpolation from the QP curve
Qest,n
Head interpolation from the QH curve
Qest,n
(1) : Pest,n
nnom nest
(2) : Qest,n
nnom nest
(3) : Hest,n
nnom nest
(4) : Es,est
Pest Qest
3
Pest
Hest,n
Affinity transform (2) for the flow rate estimate
Affinity transform (3) for the head estimate
Qest
Hest
Qest 2
Hest
Pest Qest
Estimation (4) of specific energy consumption
Es,est
Estimation accuracy of the QP-curvebased pump model 50% flow rate Q/Q| < 8%
100% flow rate | Q/Q| < 6%
140% flow rate Q/Q| < 20% at 1320/1560 rpm
Pilot test results for an industrial pulp pump system
40
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
30 Time (%)
• • • •
20 10 0
30-40
50-60 70-80 90-100 110-120 Relative flow rate (%)
Part III: Identification of system characteristics
Surrounding system characteristics Pump/fan operating point lies in the intersection of the device and surrounding process characteristic curves 25
H
20 Head (m)
–
15
Es
Q, H
10
2
kQ
nest
H
st
0 0
k Q2
Ptot Q
g H dt
p
n
nom
5
H st
Hprocess
10
20 30 Flow rate (l/s)
Minimum level of energy usage:
E s,min
g H st
40
Ref.: T. Ahonen et al., “Generic unit process functions set for pumping systems,” in Proc. EEMODS 2011 Conf.
State estimation with the process-curvebased pump model nest
Process-curve-based pump model
Pest
QH curve at nnom, Hst and k
Qest Hest
25
Head (m)
20 15 H
est 2
10 k Q 5
H
0 0
n n
st
Q
est
10
20 30 Flow rate (l/s)
nom est
H
process
40
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.
Identification of the surrounding system Hst and k can be determined with applicable QP curve model estimates (Qest, Hest) using the LMS method 30
QP estimates
Est. system curve
25 Head (m)
–
20 15 10
Hprocess
Find best-fit values for Hst and k with these estimates
S
m i 1
10
20 Flow rate (l/s)
30
k Q2 2
(H est,i
5 0 0
H st
40
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.
H st
2 k Qest, i)
Combined (hybrid) usage of the pump models –
Process-curve-based model is used when operating on the flat part of the QP curve Process curve model is used
QP curve model is used
Process curve model is used
Estimation accuracy of the processcurve-based pump model Process curve parameters were determined with 11 QP curve model estimates at 1160–1620 rpm
30 35 %
Head (m)
25
65 % 70 %
20
68 %
15
60 % 1400 rpm
10
Meas. QP est.
5 0 0
600 rpm
10
20 30 Flow rate (l/s)
H
40
process
50
Flow rate estimation error (l/s)
–
7 6
QP curve estimation
Process curve est.
5 4 3 2 1 0 -1 550
750 950 1150 1350 Rotational speed (rpm)
1550
Part IV: Energy efficient system control with variable-speed usage
How to achieve the minimum energy consumption? Drive the system as energy efficiently as possible Minimize the hydraulic losses (Hst, k) Use high efficiency components in the system H =5-10 m, k=0.0149 st
100
700
10 m
overall eff.
600
80 60
6.5 m 5m
40 20
System E s (Wh/m 3)
Specific energy consumption (Wh/m3)
1. 2. 3.
8m X: 1155 Y : 53.41
X: 815 Y : 26.71
800
1000 1200 Rotational speed (rpm)
0.3
500 0.4
400
0.5
300
0.7
200 1.0
100
1400
0
0
20
40 Head (m)
Ref.: T. Ahonen et al., “Generic unit process functions set for pumping systems,” in Proc. EEMODS 2011 Conf.
60
Energy efficient filling of a reservoir with a variable-speed pumping system –
Variable-speed operation allows energy efficient filling of a reservoir, but at which rotational speeds?
H k k
Hst,1
Hst,2
LH
k
Hst,2 Hst,1 LL
Q
k 2. k
Hst,2 1. Hst,1
• • •
Test nest
Q
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
Optimum rotational speed (rpm)
H
Specific energy consumption (Wh/m3)
System identification and determination of an optimum rotational speed profile H st=5-10 m, k=0.0149
100
10 m
80 60
6.5 m 5m
40
8m X: 1155 Y : 53.41
X: 815 Y: 26.71
20
800
1200
1000 1200 Rotational speed (rpm) st
1400
1100 1000 900 800
5
6.25 7.5 8.75 System static head (m)
10
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 around 1050 rpm
Part V: Identification of operational states that reduce the system lifetime or cause an immediate failure
Pump cavitation and fluid recirculation, fan stalling 1563
1561 1560 1559 1558 1557 0,0
1,0
2,0
3,0
4,0
5,0
Time (s) 2,0
Scaled T (% of Tnom)
n (rpm)
1562
1,0 No cavitation (BEP)
0,0
Cavitation (max. flow)
-1,0 -2,0 0,0
1,0
2,0
3,0
Time (s)
4,0
5,0
Frequency-converter-based, sensorless detection of cavitation or stalling Determine nRMS,N and TRMS,N when the pump operates near the BEP
1563 1562 1561
Cavitation (max. flow)
1560
Calculate the present nRMS and TRMS
Average speed at max. flow
1559 1558 1557
Is nRMS/nRMS,N over the threshold value?
No
Yes
Is TRMS/TRMS,N over the threshold value?
Yes
Cavitation may occur in the pump
No
Pump is operating normally
x DC ( n )
1 M
M 1
x( n
k)
k 0
x AC ( n )
x (n )
x RMS ( n )
1 M
x DC ( n ) M 1
2 x AC (n
k 0
Ref.: T. Ahonen et al., “Novel method for detecting cavitation in centrifugal pump with frequency converter,” Insight, vol. 53, no. 8, August 2011.
k)
TRMS/TRMS,N
nRMS/n RMS,N
TRMS/TRMS,N
nRMS/nRMS,N
Test results Serlachius 1500 rpm
5 4 2 1 0
5
10
15
20 25 30 Flow rate (l/s)
35
40
5
10
15
20 25 30 Flow rate (l/s)
35
40
8 6 4 2 1 0
Sulzer 1450 rpm
5 4 2 1 0
0
5
10
15 20 25 30 Flow rate (l/s)
35
40
45
0
5
10
15 20 25 30 Flow rate (l/s)
35
40
45
5 4 2 1 0
Detection of contamination in a fan impeller –
Impeller contamination is a root cause for several imbalance- and vibration-related faults in fans 120
Torque (%) Rotational speed (%)
100 80 60 40 Torque Clean impeller Contaminated impeller
20 0
0
5
10 15 Time (s)
20
25
30
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.
Operation of the fan impeller contamination detection algorithm Baseline measurement
nest,1
Starting the fan with constant torque ref.
Filtering and processing of estimates
n1
Calculation of the fan acceleration with (1)
Tref,1
(1) :
est
(2) : JFan (3) : mest
nlimit,upper
nlimit,lower
est,1
t Tref,n
Tref,1
est,n
est,1
Calculation of mass increase with (2) and (3)
2 JFan r12 r22
Starting the fan with constant torque ref. nth detection measurement
est,n
nest,n
Tref,n
Filtering and processing of estimates
nn
Calculation of the fan acceleration with (1)
mest
Test results Method has been successfully verified by laboratory measurements 160
120 Mass (g)
–
80
40
0
Actual mass Est. mass Tref=30% 1
2 Measurement set
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.
3
Summary
Role of the frequency converter in LCC optimization of pump and fan systems Detection of operational states degrading system lifetime: cavitation, stalling, recirculation, dryrunning, contamination build-up, etc.
Production losses 14 % Maintenance 4%
Energy efficiency optimization: parallel pumping, compensation of oversizing, optimal speed profiles, etc.
Investment 7%
Energy 75 %
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
