THERMAL EVALUATION AND ENERGY SAVING WITH LOSS REDUCTION IN CORE AND WINDING OF POWER TRANSFORMERS Kourosh Mousavi Takami1 Jafar Mahmoudi2 1-TDI researcher and Ph.D. student in Malardalen University 2-professor in Malardalen University, Sweden Box 883,721 23, IST Dep., Mälardalen University, Västerås, Sweden P.O. Box: 13445686, Sharif institute of technology (TDI), Tehran, Iran [email protected] [email protected]

ABSTRACT Power loss, heat generation and heat distribution evaluations in a large-scale oil cooled power transformer are presented here, along with the details of computer implementation and experimental verification. In this paper, we consider that core power losses are approximately constant with temperature various or might decreased with that. Winding temperature and oil will increase with the load increasing and might create a hot spot and that is caused by degradation insulation and the loss of life in the power transformer. Therefore the authors tried to Asses these phenomena with use of electrical and thermal soft wares. On the results (with Iranian network data) of simulation showed that in case of oil spraying on the hotspot point or area, very low temperature with the best conditions would be obtained. Then it is best to provide a cooling system with the best insulation and with the minimum side effect on the magnetic and electrical field distribution. Finally by reduction in transformer losses, could savings potential of 22 TWh / year for EU.. Keywords: hot spot temperature, losses, ONAN, oil spraying system, nozzles, energy. INTRODUCTION Power transformers are important in power network; therefore, they are located between the two sides of the network that contain two different voltage levels. For example they act as changer in two different countries. Power transformer is exposed to the worst injures because if we draw an over load from the network, surely it would be damaged, or would cause loss of life insulations and other parts of power. Because in this facts, we have to analyze all the parts, that may have a temperature rise. For example with on line monitoring we can see the temperature of all the parts in oil, winding and cores. Then decide which fan should start working or which ones should rotate in twice RPM. In power transformer fans have two modes of turning. Originally heat generations in power transformers are resistance losses, eddy current and hysteresis losses that the two recent items varies less than resistance losses with load or temperature. Any way in transient analyses we have to note all of losses. This paper focus on the source of heat generation and distribution and with the help of MATLAB and FEM software’s will analyze those phenomena’s. There are two main categories of losses, no load losses and load losses. No load losses are basically core losses associated with energizing the transformer and driving flux through the core. Load losses are further subdivided into RI 2 losses and stray losses. Stray losses are the result of the stray flux from the winding or lead impinging on metal parts such as the tank walls , the clamps , and even the winding themselves , resulting in induced eddy currents. Our algorithms decide the number of coolers that are required to dissipate the calculated losses. Our program decides which coolers must be activated on data based in the actual and/or predicted load and ambient conditions. The cooling fans are under control and each permutated to run according to the number of hours in operation. This results in longer bearing life and reduced costs. In the following Table 1 Energy & emission savings potentials related to transformers & standby power is illustrated: Calculating of core losses (eddy current and hysteresis losses)

Figure1: thermo vision picture

Cores in power transformers are generally made of stacks of electrical steel laminations. These are usually in the range of 0.23- 0.46 mm (and for large scale power transformer may be up to 0.5 mm )in thickness and up to 1 meter wide or as much wide as it can be accommodated by the rolling mill.

The losses of a ferrite core or core set Pv is proportional to the area of the hysteresis loop in question. It consists of three components: Pv=Pv,basic+Pv,hysteresis+Pv, eddy current owing to the high specific resistance of ferrite materials, the eddy current losses in the common frequency range today (50Hz - 2 MHz) may be practically disregarded except in the case of core shapes having a large cross-sectional area. In principle the following applies: (1) Pv = U ..I . cos(ϕ ) The power loss PV is a function of the temperature T, the frequency f, the flux density B and is of course dependent on ferrite material and core shape. The temperature dependence can generally be approximated by means of a third-order polynomial, while Pv ( f ) ∝ f (1+ x ) 0〈 x〈1 (2) Applies for the frequency dependence and Pv ( B) ∝ B (1+ y ) 0〈 y 〈1 (3) for the flux density dependence. The coefficients x and y are dependent on core shape and material, and there is a mutual dependence between the coefficients of the defining quantity (e.g. T) and the relevant parameter set (e.g. f, B). A transformation program is available for EFD and ETD cores as a design tool which allows core losses to be converted to different operating conditions (specific to core shape / material). General relationship between B and H: (4) B = µ0 ⋅ µr (H ) ⋅ H

µ 0 = 1.257 * 10E-6 Wi = [k h .B 1.6 . f + k f .t.B 2 . f 2 ]vol

And total loss relation is:

(5)

Table 2: Constant total loss for f constant Power loss versus of T

1000

B=50

Pl[W/m³]

100

B=100 B=200

10

B=300

1

0

50

100

150

T[°C]

T °c

B=50

P (w/m3) B=100 B=200 B=300

25 30 40 50 60 70 80 90 100 110 120

11.62 10.73 9.04 7.49 6.1 4.9 3.91 3.15 2.64 2.4 2.45

56.43 52.66 45.29 38.33 31.94 26.33 21.67 18.14 15.95 15.26 16.28

251.23 237.05 209.51 183.76 160.64 140.98 125.62 115.4 111.14 113.68 123.86

609.69 581.8 527.97 478.01 433.49 395.97 367.01 348.17 341.01 347.09 367.97

Figure2: core power loss total for f constant hysteresis loss at T=25 c

hysteresis loss at T=100 c

500

500 400

300

300

200

200

100

100

B[mT]

B[mT]

400

0 -100

0 -100

-200

-200

-300

-300

-400

-400

-500 -298.0 -238.4 -178.8 -119.2 -59.6

0.0

59.6

H[A/m]

Figure3: hysteresis loss at T=25 c

119.2 178.8 238.4 298.0

-500 -300

-240

-180

-120

-60

0

60

120

180

240

300

H[A/m]

Figure4: hysteresis loss at T=100 c

Simulation shows that, with the increase of temperature from 25°C to 50°C hysteresis loss will decrease and after that from 50°C to 75°C it will raise as the same way it decreased before, and from 75°C until 100°C it will increase but with less slope and finally after 100°C approximately slope will decrease but like the last case. For all simulations PT decreased linearly with increasing temperature. The Ph values tended to be constant or increased slightly between room temperature and 75- 100°C and then decreased slightly from 100°C to 120°C.

The eddy current component of loss Pe which was defined as PT - Ph, followed dependence with increasing temperature. (See figure 2) Hysteresis loops shows in Figures3 and4, for the temperature rang of 25 °C to 100 °C. Table 2 shows how power loss varies with temperature rise in the core. 3- Temperature calculation: In most of the heat and mass transfers the following equation can be seen. In this paper, convection and conduction consider for all the calculations. The following are the assumptions considered for simulation: z Oil and air initially are in steady state. z Oil is a Newtonian and incompressible fluid. z Fluid flow is laminar, unsteady and two-dimensional. z There is internal heat generation. z Data base provided from appendix 1 The thickness of the copper-insulation layers, as shown in Fig. 2(a), can be calculated from the design data of the winding log kr = (

r log 0 r1 kr1

+

r log 3 r2 kr 2

rn r1

(6)

+ ... +

r log n rn −1 kr1

)

Similarly, the t.c. of a disc or layer in -direction can be calculated as in (7) [Fig. 2(b)]

k cu k kp k pb (t cu + t kp + t pb )

kz =

k cu t kp k pb + t cu k kp k pb + k cu k kp t pb

(7)

The calculated effective thermal conductivity of a typical power transformer Windings in axial and radial direction has been given in Table 1. These values have been used in the computation of HST. Table 1: typical value of k for winding kr

kz

K

w / moc

w / moc

w / m oc

Winding type

Transformer rating(MVA)

2.5 7.2 7.35

4.4 4.46 4.46

3.32 5.67 5.72

Hv disc Lv disc Lv disc

250 250 250

The winding is a thermally inhomogeneous structure for this, the thermal conductivity should be treated as a tensor. The thermal conductivity takes the following form: k ten =

k rr

k rz

k zr

k zz

Where, k rr ....k zz are called conductivity Coefficients. Considering the transformer winding (simplified as above), the insulation structure closely satisfies the orthotropic structure in the orthogonal coordinate system and the above equation becomes k rr

0

k ten =

= 0

The terms

k zz

kr 0

0 kz

=

k1 = k 2 0

0 k3 = k 4

k r and k z are called as principal thermal conductivity.

Heat Flow Equations [5]: With the thermal conductivity, the system of no homogeneous HCE under a no homogeneous boundary condition in cylindrical coordinate system is written as

kr

1 ∂T ∂  ∂T  ∂ 2T G + = r  + kz r∂r  ∂r  ∂z 2 K α~d ∂t

In the region , a ≤ r ≤ b

,0 ≤ z ≤ l

(8)

l ≥ 0.

At the inner cylindrical surface (r = a, t f 0) .

∂T (9) + h1T = f 1 ( z , t ) ∂r At the outer cylindrical surface r = b, t f 0 ∂T (10) k2 + h2T = f 2 ( z , t ) ∂r At the bottom flat surface z = 0, t f 0 ∂T (11) − k3 + h3T = f 3 ( z , t ) ∂z At the top flat surface z = l , t f 0 ∂T (12) k4 + h4T = f 4 ( z , t ) ∂z In the region, a ≤ r ≤ b ,0 ≤ z ≤ l , and t = 0 − k1

(

)

(

(

)

)

T = F (r , z )

(13) ( T = T (r , z , t ) ). The term G is the heat source function, and has been modified here to take care of variation of resistivity of copper with temperature. The heat source term G can be of the form: G = g 0 (1 + ρ t (T − T0 )) (14) = g 0 − g 0 ρ t T0 + g 0 ρ t T = G0 + g 0 ρ t T Where

ρ t is the temperature coefficient of electrical resistance of copper wire in o c −1 . With this representation, the

function G becomes temperature dependent, distributed, heat source. For the sake of mathematical convenience and to provide a reference for the heat source function, the constant G0 is included to replace the constant part of (14). The term F (r , z ) represents the initial function for transient heat conduction problem. The term, ~  1  αd =  

where is the diffusivity. The time-dependent boundary functions

f i ( z, t ) = f i ( I ) ( z ) + f i ( F ≈ I ) ( z )[1 − e

−( t )

τ

]

f1 .... f 4

ρ eq Cp eq  = α d / k

, derived from Newton’s law of cooling, are of the following form:

(15)

The degenerate form in steady state, along the axial direction, is shown to be as in (16) (16) f i ( z ) = hi Tz i = hi × (Tb + msi z ) is the initial temperature at time (t = 0) , and f i ( F ≈ I ) ( z ) is the difference between final steady-state (t = ∞) and initial steady-state (t = 0) temperature. The term Tb is the temperature at the bottom of the disc or layer, as

Where:

f i( I ) ( z)

applicable. Similarly, functions f 3 (r ) and f 4 (r ) representing temperatures across the bottom and top surfaces, in steady state, are of the form of f j (r ) = h j (Tb or Ttop ) . Heat-transfer coefficients h1 ....h4 , are different across all four surfaces, the values of which can be calculated by using heat-transfer empirical relations given in [2]. RESULTS AND DISCUSSION We can solve that equation with MATLAB software based on genetic algorithm. Of course, assessment was done in the steady state mode. We give the second part of the equation (8) equal to zero and solve them. We consider that with out oil spraying[5] and with oil spraying and finally modeling of the transformer oil flow to obtain the flow pattern, and the velocity and temperature distributions by fluent software we are able. We find for the maximum internal temperature above the surrounding oil T (0, 0) - Toil=25°C. The surface temperature rise in the top of oil is approximately T (top)-T (oil) =17.8 °C and bottom oil is T (bottom) –T (oil) =12.2 °C This modeling shows that the hot spot point is located in 50% of the core and winding, height. According to the results of modelling with the fluent soft ware, it is seen that for the OFAF condition heat in 91% height of core, hot spot might be created On that point, in case of temperature increase due to over loading the transformer. If we spray oil to top of winding (in 91% height of core) we can see that in the top, without oil forced, oil temperature will be intensity low. For the maximum internal temperature we find above the surrounding oil we have T (0, 0)-Toil= -35.8°C.

But there is a problem in the power transformer for oil spraying and that is electrical and magnetic field problem. While spraying oil, non homogeneous field might occur, and this causes a break down before the manufacturer determinations may occur, and for this problem we have to assess and calculate field effects.

evaluation of temperature profile with ONAN and oil spraying to %91 of core height

130

125

90 100

120

120

115 110

115

105

temprature in oc

Temprature in oc

125

80

80

70

60

60

40 50

100 1

110 0.8

0.5 radius of core and winding in pu.

0

0

0.2

0.6 0.4 Height of core and winding in pu.

1

20 1

40 1

105

Figure5: evaluation of temperature profile with ONAN

0.5 radius of core and winding in per unit

0.5 0

0

30

per unit of core and winding height

Figure6: evaluation of temperature profile with ONAN and oil spraying to %91 of core height (location of hot spot).

CONCLUSIONS In this paper we consider that with the increasing temperature due to over load or another phenomena, core power loss is remains constant, but over loading copper loss and etc. will increase which causes a rise in the temperature and creates hot spot, as the solution of this problem with consider action of magnetic field aspects we offer the use of oil spraying devices, that sprays oil on hot spot point. The other way is installation of many spraying devices on the tank that can give order according to location of the hot spot after identifying it, and its nozzles can rotate to any angle until it can spray oil to hot spot point or area. See figure 6. Control system can be designed with genetic algorithm, neural network, fuzzy logic and any another algorithm that could be chose for spraying. It’s noted that there’s no needed to use piping inside tank. All the spraying will be done only from the tanks body. Ultimately, by loss reduction we can Savings potential of 22 TWh / year for EU. ACKNOWLEDGEMENT AND FUTURE WORKS In this research we had helping by Iranian energy ministry and Punel substation. In future: For decreasing of GHG that produce due to power transformers losses, we would like using of distributed intelligence, because it is the Solution to transformer monitoring: • Reduce amount of data transmitted to operator by using an intelligent system installed at transformer • Apply on-line, real-time data processing techniques with intelligent sensors and systems • Use on-line models to calculate and present only the useful information to the operator Schematic diagram of this method is presented in figure 8. We work on writing program for all of discussed ways. Bushing

3 4 2

2

2

5

Steel tank

Iron core behind the steel bar

2

To pump and control system Winding

Figure 7: schematic of power transformer with oil spraying system

Insulation Radiator

Where: 1: core and winding 2: nozzles 3: power transformer tank 4: hot spot area or point 5: oil flow direction APPENDIX1 [3]: Power transformer 250 MVA in IEEE loading guide 1995 Transformer Losses, W. No Load 78100 Pdc losses (I2 Rdc ) 411780 Eddy losses 41200 Stray losses 31660 Nominal voltage 118 KV 230KV Pdc at hot spot location 467 527 Eddy current losses at hot spot location 309 (0.65 pu) 157 (0.3 pu) Per unit height to winding hot spot 1 1 Temperature Rise °C . Rated top oil rise 38.3 Rated top duct oil rise 38.8 Rated hot spot rise 58.6 50.8 Rated average winding rise 41.7 39.7 Rated bottom oil rise 16 Initial top oil 38.3 Initial top duct oil 38.3 Initial average winding 33.2 Initial bottom oil 28 Initial hot spot 38.3 Transformer component weights, kg Mass of core and coil assembly 172200 Mass of tank 39700 Mass of oil 37887 Appendix 2[3]: Q GC+ Q LW+ Q GSL= Q AO+ Q LO Where, Q LW is the heat lost by the windings, W. Q GC is the heat generated by the core losses, W. Q GSL is the heat generated by stray losses, W. Q AO is the heat absorbed by the tank core oil, W. Q LO is the heat lost by the oil, W. Q GC=PCR are the rated core losses, W. NOMENCLATURE Greek Letters µ0 Magnetic field constant

t cu t kp

Total thickness of kraft paper in axial direction.

t pb

Total thickness of pressboard in axial direction.

m si

Axial temperature gradient.

τ µr h

Total thickness of copper in axial direction.

Time constant (TC) of the thermal process. Relative permeability Heat-transfer coefficients

Figure8:shematic diagram, for on line monitoring of power transformer

Top-oil temperature H winding current X winding current Y winding current

rules Winding hot-spot Temperature model

Sensors

Moisture sensor temperature

Moisture model

Moisture sensor

Cooling stage status

Fixed parameter Type of paper Reference water content for dry paper Oxygen content

Insulation Aging Model Insulation aging is a function of temperature Aging is calculated automatically on the hottest winding Aging can be calculated either as per IEEE rules Cooling control The cooling system can be initiated from either: - Top-oil temperature - Load current -Winding hot-spot temperature Cooling control can detect discrepancies and raise alarm in case of cooling malfunction It is assumed that under significant load, the absolute water content in oil is uniform in the transformer Moisture and bubbling The relative water content in the winding can be derived from the moisture sensor When thermal equilibrium conditions are achieved, paper-oil partition curves can be applied

output Insulation

Aging

Aging rate

Cumulative aging Display and trending

Cooling control Stage 2 ON/OFF control

Stage 3 ON/OFF control Order to nuzzle Control vs. status discrepancy alarm

Moisture and bubbling Water content insulating paper

in

Bubbling temperature Top oil temperature set pint Hot spot temperature set point Load current set point

Water condensation temp Absolute water content in oil

REFERENCES [1] Robert M. Del vecchio, Bertrand poulin, Pierre T. Feghali, Dilipkumar M. shah, and Rajendra Ahuja, `` Transformer Design Principles with application to core – form transformers ``, CRC press, 2002. [2] M. K. Pradhan and T. S. Ramu, `` Prediction of Hottest Spot Temperature (HST) in Power and Station Transformers``, IEEE Transaction on power delivery, vol.18, NO.4, October2003 [3] IEEE Loading Guide for Mineral Oil Immersed Transformer, C57.91, pp. 18–19, 46–53, 1995. [4] Jafar Mahmoudi, Mathematical modelling of fluid flow, heat transfer and solidification in a strip continuous casting process``, International Journal of Cast Metals Research IJC604.3d 20/1/06 13:22:12 ,The Charles worth Group, Wakefield +44(0)1924 369598- Rev 7.51n/W (Jan 20 2003). [5] Kourosh Mousavi Takami, Jafar Mahmoudi, A novel device (oil spraying system) for local cooling of hot spot and high temperature areas in power transformers, 3rd IGEC conference, Sweden, June 2007. [6] G.W. Swift, T.S. Molinski, W. Lehn , “A Fundamental Approach to Transformer Thermal Modelling–Part I: Theory and Equivalent Circuit,” IEEE Trans. on Power Delivery, vol.16, no.2 April 2001, pp.171-175.I

[7] G.W. Swift, T.S. Molinski, R. Bray and R. Menzies, “A Fundamental Approach to Transformer Thermal Modelling– Part II: Field Verification,” IEEE Trans, on power delivery, vol.16, no.2 April 2001, pp.176-180. [8] D. Susa, M. Lehtonen, H. Nordman, “Dynamic Thermal Modelling of Power Transformers,” IEEE Trans. on Power Delivery, vol.20, Iss.1 Jan 2005, pp.197- 204. [9] D. Susa, M. Lehtonen, H. Nordman, “Dynamic Thermal Modelling of distribution Transformers,” The paper has been approved for publication in the IEEE Trans. on Power Delivery [10] P.T. Staats, W.M. Grady, A. Arapostathis, R. S. Thallam, “A Procedure for Derating a Substation Transformer in the Presence of Widespread Electric Vehicle battery Charging,” IEEE Trans. on Power Delivery, vol.12, No.4 Oct 1997, pp.1562-1568. [11] Working Group 12-09, “Experimental Determination of Power Transformer Hot-Spot Factor” Electra, no.161, Aug 1995, pp.35-39. [12] IEC Publication 76-2:1993, Power Transformers, Part 2. Temperature rises. [13] Working Group 12-09, “Analytical Determination of Transformer Windings Hot-Spot Factor” Electra, no.161, Aug 1995, pp.29-33. [14] Working Group 12-09, “A survey of facts and opinions on the maximum safe operating temperature of power transformers under emergency conditions” Electra, no.129, pp.53-63. [15] J. Saitz, T. Holopainen and A. Arkkio, Modelling and Simulation in Electromechanics Field Problems, HUT , Electromechanics Lab. 2002. [16] K. Haymer and R. Belmans, Numerical Modelling and Design of Electric Machines and Devices, WIT-Press, 1999. [17] M. David and Others, Finite Element Method Magnetic FEMM. [18] A. Konard, “Inegrodifferential Finite Element Formulation of Two-Dimensional Steady-State Skin Effect Problems,” IEEE Trans. on Magnetics, vol. MAG-18, no.1, Jan 1982, pp.284-292. [19] Kourosh Mousavi Takami, Evaluation of oil in over 20 year’s old oil immersed power transformer, Mazandaran University, May 2001. [20]Kourosh mousavi Takami, A FFT technique for discrimination between faults and magnetizing inrush currents in power transformers, KAHROBA scientific magazine specialized in power electric engineering, Mazandaran, Iran [21] Kourosh Mousavi Takami, Hasan Gholnejad, Jafar Mahmoudi, April 2007, Numerical modelling on Thermal and hot spot evaluations of oil immersed power Transformers by FEMLAB and MATLAB software’s, eurosime conference, London [22] Kourosh Mousavi Takami, Advanced Transformer Monitoring & Diagnostic Systems and thermal assessment with robust software's, research presentation, Water and power University, March 2007, Tehran, Iran [23] Kourosh Mousavi Takami, Hot Spot identification and find a best thermal model for large scale power transformers, April 2006, KTH University, Stockholm, Sweden.