Manchester University Research on Transformer Thermal Management

Transformer Research at the University of Manchester, contact: zhongdong.wang@manchester.ac.uk; qiang.liu@manchester.ac.uk 1/43
Transformer Thermal Management
for Future Power Networks
20 June 2018
Dr Qiang Liu (PhD, CEng, SMIEEE, FHEA)
Senior Lecturer in Power System Plant
Director of The High Voltage Laboratories
School of Electrical and Electronic Engineering
The University of Manchester

MANCHESTER
Birthplace of Industrial Revolution

MANCHESTER

High Voltage Lab

Phase 4
2017-2021
Transformer Research Consortium
Phase 1, 2005-2007
3 PhD , 11 papers
Phase 2, 2008-2011
3 PhD, 22 papers
Phase 3, 2012-2016
6 PhD, 35 papers
*

Research Scope
Design &
Development
Operation &
Maintenance
Asset
Management
Post Failure
Analysis
v Application of Alternative Oils in Large Power Transformers
v Transformer Magnetic Core Modelling
v Transformer Thermal Modelling
v Frequency Response Analysis
v Online Monitoring of Partial
Discharges in Power
Transformers
v Dissolved Gas Analysis
v Power Transformer Ferroresonance
v End-of-life Modelling for Transformer Asset
v Transformer and System Reliability
v Forensic Investigation of
Scrapped Transformer
v Dielectric Performance of
Aged Transformer Insulation

Outline
v Background
v Steady-state Transformer Thermal Rating
v Dynamic Transformer Thermal Rating
v Cooling Performance of Alternative Liquids
v Summary

Transformers
Photo: Courtesy of GE Grid Solution, HVDC converter transformer

Transformer Voltage and Power
Updated based on R. Baehr, ‘Transformer technology state-of-the-art and trends of future development’, CIGRE ELECTRA, 2001

Transformers in Future Power Networks
v A large population of ageing transformers will exist.

Transformers in Future Power Networks
v Higher and more dynamic load profiles would appear.

Transformer Cooling Modes
• Oil Natural, Air Natural (ONAN)
• Oil forced and Directed, Air Forced (ODAF)
Oil Natural, Air Natural
Oil forced and Directed, Air Forced

Steady-state Transformer Thermal Rating

Transformer Thermal Diagram
v Illustration of temperature rise in a transformer
– Assumptions of linear winding and oil temperature rises along the height
– Hot-spot factor, H, to compensate the error introduced by the assumptions
(often used values H=1.1 distribution and H=1.3 transmission)
IEC 60076-7, "Loading guide for oil-immersed power transformers”, 2005. IEC 60076-2, "Temperature-rise for oil-immersed transformers”, 2011.

Simulation and Experimentation
Computational Fluid Dynamics (CFD)
Tin
Tout
Radiator
Main winding
model
External heating unit
to heating the oil
Flow meter
Pump
Experimental Setup

Experimental Verification of Simulation Results
v Experimental results show good agreement with simulation
results in term of flow distributions.
Case 4: Re=97
Case 2: Re=536
Case 5: Re=1402
X. Zhang, M. Daghrah, Z. Wang, Q. Liu, P. Jarman, and M. Negro, “Experimental Verification of Dimensional Analysis Results on Flow Distribution and Pressure Drop for Disc
Type Windings in OD Cooling Modes,” IEEE Transactions on Power Delivery, vol 33, issue 04, pp 1647 - 1656 , 2018

Experimental Verification of Simulation Results
v Experimental results show good agreement with simulation
results in term of disc temperatures.
1 2 3 4 5 6 7 8 9 10
78
79
80
81
82
83
84
85
86
Plates numbered from bottom to top
Temperature(o
C)
1 2 3 4 5 6 7 8 9 10 11
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Ducts named from bottom to top
Averagevelocityintheduct(m/s)
Measurement
CFD
PIV measurement
CFD
Pass inlet oil velocity
= 0.2 m/s

Case 1 – CFD Simulation OD
℃ ℃
Horizontal duct height "#$%&=3 mm Horizontal duct height "#$%&=4 mm
• 3-pass winding model
• 6 discs/7 ducts per pass
• 30 strands per disc
• Vertical duct width
Wduct = 8 mm
• Resistive power loss
corresponding to 4 A/''(
(3.4×10,W/'-)
• Inlet oil temperature 40 ºC
• OD Cooling mode
• Pass inlet velocity 0.4 m/s

Top Oil, Average Winding and Hotspot Temperatures
v With almost the same top oil and average winding
temperatures, slight winding geometric difference leads to
large variation of hotspot temperature.
Hduct = 3 mm Hduct = 4 mm
Top oil temperature 41°C 41°C
Bottom oil temperature (rise) 40°C 40°C
Average winding temperature (rise) 46.8°C 48.2°C
Hotspot Temperature (rise) 48.9°C 58.2°C

Winding Oil Flow Distribution
v The maldistribution (ratio of the maximum to the minimum
radial duct flow velocity) increases with Re (Reynold number)
and α (winding geometric parameter) monotonically.
X. Zhang, M. Daghrah, Z. Wang, Q. Liu, P. Jarman, and M. Negro, “Experimental Verification of Dimensional Analysis Results on Flow Distribution and Pressure Drop for Disc
Type Windings in OD Cooling Modes,” IEEE Transactions on Power Delivery, vol 33, issue 04, pp 1647 - 1656 , 2018

Hotspot Factor
v The hot-spot factor
remains approximately
constant in low Re region
and then increases
monotonically with Re
because of an increasingly
uneven flow distribution.
200 300 400 500 600 700 800 900 1000 1100 1200
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
The Reynolds number
Thehot-spotfactor
Pr=60
Pr=100
Pr=150
!"#$%=3 mm Wduct = 8 mm
X. Zhang, Z. D. Wang, and Q. Liu, “Interpretation of Hot Spot Factor for Transformers in OD Cooling Modes,” IEEE Transactions on Power Delivery, vol 33, issue 03, pp. 1071-
1080, 2018.

Case 2 – Temperature-rise Test ON
• LV winding, 66 MVA 225/26.4 kV
ONAN/ONAF transmission
transformer
• 4 passes with pass (19 discs, 20
horizontal ducts per pass)
• Mass flow rate 0.78 kg/s (inlet
velocity 0.051 m/s)
• Uniform power loss distribution
677 W/disc
Contours of the temperature distribution in the winding (Part 1
of pass 1 is excluded). (a) Pass 1 at the bottom of the winding.
(b) Pass 2. (c) Pass 3. (d) Pass 4 at the top of the winding
X. Zhang, Z.D. Wang, Q. Liu, P. Jarman and M. Negro, "Numerical investigation of oil flow and temperature distributions for ON transformer windings," Applied
Thermal Engineering, vol. 130, pp. 1-9, February 2018.

Simulated Flow and Temperature Distributions
v Oil flow stagnations correspond to high winding disc
temperatures
1 19 38 57 76
65
75
85
95
105
115
125
135
145
Pass 1 Pass 2 Pass 3 Pass 4
Disc number named from bottom to top
Discmaximumtemperature(
o
C)
1 10 20 30 40 50 60 70 80
-0.025
-0.02
-0.015
-0.01
-0.005
0
0.005
0.01
0.015
0.02
0.025
0.03
Pass 1 Pass 2 Pass 3 Pass 4
Duct number named from bottom to top
Ductaveragevelocity(m/s)
Flow distribution Temperature distribution

Comparison between Simulation and Temperature-rise Test
v The hotspot temperature from CFD simulation is 24.5 oC
higher than the conventional calculation based on
temperature rise test.
v The hotspot factor derived from the CFD simulation is 2.12.
CFD Temperature-rise test
Top oil temperature (rise) 82.8°C (52.6 K) 80.4°C (50.2 K)
Bottom oil temperature (rise) 46.7°C (16.5 K) 46.7°C (16.5 K)
Average winding temperature (rise) 92.5°C (62.3 K) 91.8°C (61.6 K)
Hotspot Temperature (rise) 141.6°C (111.4K)
117.1°C (86.9 K)
(assuming H=1.3)
Temperature-rise test at ambient temperature of 30.2 ℃.

Hotspot Factor
v The hot-spot factor is predominantly controlled by
Richardson number Ri in ON cooling mode.
0 0.2 0.4 0.6 0.8 1
1
1.2
1.4
1.6
1.8
2
2.2
2.4
Ri
Hot-spotfactor
Re=118
Re=138
Re=157
Re=177
Re=195

Dynamic Transformer Thermal Rating

IEC Loading Guide
IEC 60076-7, "Loading guide for oil-immersed power transformers”, 2005.
Typically, it would be
less than half-an-hour.
May persist for weeks
or even months
It is usually impracticable to control the
duration of emergency loading in this case

IEC Dynamic Thermal Model
v The IEC dynamic thermal model follows the rules laid down
in the thermal diagram.
v Exponential varying trends are described by !"($)and !&($).
'( stands for hot-spot temperature, ') for ambient temperature, ∆'+, for initial top oil temperature rise
over ambient temperature, ∆'+- for rated top oil temperature rise, . for the ratio of load loss to no-load
loss, / for load factor, ∆'(, for initial hot-spot temperature rise over top oil temperature, 0 for hot-spot
factor, 1- for rated average winding to average oil temperature gradient, 2+ for oil time constant, 23 for
winding time constant, x oil exponent, y winding exponent, 4"", 4&", 4&& for constants.
IEC 60076-7, "Loading guide for oil-immersed power transformers”, 2005.

Temperature-rise Tests
v Extended temperature-rise tests are needed to derive the
thermal parameters used in dynamic thermal model.
v Steady-state means that rate of change of top oil
temperature-rise has fallen below 1 K/h and has remained
there for a period of 3 h.
Extended Temperature-rise Tests
v Continuous recording
of temperature during
heat up period is
beneficial for deriving
oil time constant.

Case 1 – HST at Cyclic Load
• A 1000 kVA, 6.6/0.433 kV
Transformer
• ONAN cooling mode
• Layer type winding
• 12 fibre-optic sensors
installed
B phase HV top-oil
Installation of Fibre-optic sensors
Y. Gao, B. Patel, Q. Liu, Z.D. Wang and G. Bryson, “Methodology to assess distribution transformer thermal capacity for uptake of low carbon
technologies” IET Generation, Transmission & Distribution, vol. 11, issue 7, pp 1645 – 1651, June 2017.

HST Comparison at Cyclic Load
v Thermal parameters referred to the IEC standard (scenario one) tend to
overestimate the HST while thermal parameters derived from temp-rise
tests (scenario two) tend to underestimate the HST. The best estimation
of the HST was achieved by implementing thermal parameters derived
from fibre-optic sensors (scenario three).
Y. Gao, B. Patel, Q. Liu, Z.D. Wang and G. Bryson, “Methodology to assess distribution transformer thermal capacity for uptake of low carbon
technologies” IET Generation, Transmission & Distribution, vol. 11, issue 7, pp 1645 – 1651, June 2017.
Scenario one:
based on IEC parameters
Scenario two:
based on temp-rise tests
Scenario three:
based on fibre-optic sensors

Case 2 – HST at Over Load
Installation of Fibre-optic sensors
• A 200 kVA, 11/0.433 kV
Transformer
• ONAN cooling mode
• Layer type winding
• 16 fibre-optic sensors
installed
R. Villarroel, Q. Liu, and Z.D. Wang, “Experimental study of dynamic thermal behaviour of an 11 kV distribution transformer”, CIRED Open Access
Proceedings Journal, vol. 2017, issue 1, pp. 158–162, Glasgow, UK, 2017.

HST Comparison at Over Load
Case 1:
based on IEC parameters
Case 2:
based on temp-rise tests
Case 3:
based on fibre-optic
sensors
v Thermal parameters referred to the IEC standard (case 1) tend to
overestimate the HST while thermal parameters derived from temp-rise
tests (case 2) tend to underestimate the HST. The best estimation of the
HST was achieved by implementing thermal parameters derived from
fibre-optic sensors (case 3).
R. Villarroel, Q. Liu, and Z.D. Wang, “Experimental study of dynamic thermal behaviour of an 11 kV distribution transformer”, CIRED Open Access
Proceedings Journal, vol. 2017, issue 1, pp. 158–162, Glasgow, UK, 2017.

Cooling Performance of Alternative Liquids

Transformer Liquids
Gas-to-Liquid
Mineral Oil
Silicone Oil
Natural Ester
Synthetic Ester

Comparison under OD Cooling Mode
v The ester liquid provided more uniform oil flow distribution at the
same inlet oil flow velocity due to its higher viscosity compared to
other two liquids.
• Vin = 0.27 m/s,
• Ploss = 2010 W/m2
• TB = 70 ºC.
M. Daghrah, Z. Wang, Q. Liu, A. Hilker and A. Gyore, “Experimental Study of the Influence of Different Liquids on the Transformer Cooling Performance,” IEEE Transactions on
Power Delivery, under review, 2018

Comparison under OD Cooling Mode
v To achieve the same inlet oil flow velocity at a fixed winding
geometry, higher viscosity ester liquid would lead to higher pressure
losses in the winding.

Retro-filling under ON Cooling Mode
v In the investigated retro-filling scenario with a zig-zag disc type
winding geometry, the ester liquid increased the HST compared to
the mineral oil.
Liquid Type ! "#
[m/s]
$%&'
[ºC]
$()
[ºC]
*+$,"-.
[K]
Gemini X 0.028 77 78 26
MIDEL7131 0.017 83.5 85.2 52
• Ploss = 1200 W/m2
• TB = 60 ºC.

Summary
v Determination and optimisation of the winding hot-spot
temperature is essential for managing the loading capability
of transformers.
v Only measuring top oil and average winding temperatures
in the factory temperature-rise tests might not be adequate
to guarantee an acceptable hotspot temperature (HST).
v Computational Fluid Dynamic (CFD) based simulation can
predict the HST and its location especially for considering
variations of winding geometry and operating conditions.

Summary
v Thermal parameters obtained from IEC standard or
temperature-rise tests might not be accurate to predict HST
at dynamic loading especially under over loading
conditions.
v Extended temperature-rise tests supported by fibre-optic
sensors and CFD simulation are necessary for deriving
accurate thermal parameters in order to achieve dynamic
thermal rating of transformers.

Summary
v Under both cooling modes, the gas-to-liquid based
transformer oil behaved almost the same as the mineral oil
with comparable liquid flow and temperature distributions.
v Under liquid directed cooling modes, the synthetic ester
gave more uniform flow distribution and delayed the
occurrence of liquid reverse flow compared to the other oils.
v Under liquid natural cooling modes and using the zig-zag
disc type winding model, synthetic ester gave more
distorted liquid flow and temperature distributions due to its
higher viscosity which causes lower inlet flow rate to
develop under the specific tested retro-filling conditions.

Acknowledgement
University Researchers:
Prof. Zhongdong Wang
Dr. Xiang Zhang, Dr. Muhammad Daghrah
Dr. Yuan Gao, Dr. Rafael Villarroel
Industrial Partners:
Questions?

Manchester University Research on Transformer Thermal Management

Recommended

Recommended

More Related Content

Similar to Manchester University Research on Transformer Thermal Management

Similar to Manchester University Research on Transformer Thermal Management (20)

More from cwiemeexpo

More from cwiemeexpo (20)

Recently uploaded

Recently uploaded (20)

Manchester University Research on Transformer Thermal Management