Condition Monitoring of Power Transformer: A Practical Approach

Condition Monitoring of Power Transformer: A Practical Approach

Amol Nikam1

Electrical Engineering Dept.

RIT, Rajaramnagar Islampur, India

Arun Thorat2

Electrical Engineering Dept.

RIT, Rajaramnagar Islampur, India

Abstract- Power transformer is important and expensive component in the electric power system. At electricity utilities still maintenance approach is time and age based only. This paper describes how various observed, measured, testing conditions used for calculation of health indices to support reference for asset management programmes to management and asset cares. Power transformers are important assets in electrical network, considering cost and reliability. The conditions of these assets have to be known, in order to avoid any possible outages and to choose the appropriate maintenance operation that could be done. The health index of a power transformer is one single overall indicator that represents its condition and is derived by a weighting process of all available indicators.

This paper presents a case study on several power transformers having different capacity and discussing the benefits of using health index and failure probability as overall diagnostic tools. Moreover, a remaining lifetime calculation based on the transformer failure probability is defined.

Keywords- Condition monitoring, Aging rate, Health index, Probability of Failure (PoF), End of Life (EoL)

1. INTRODUCTION

   Indian power grid is one of the major power networks across the world. After independence there was large investment and development in power sector. Many assets are very old and at the end of life considering their original design. Still many assets are providing functional duties without any major problem.

   The transformer is a static device so the efficiency will be more. The natural failure rate is less therefore replacement rate is also very slow. This is another reason for use of asset for longer life span. From last decade regularity bodies and asset management policies emphasised on to reduce asset maintenance cost, simultaneously improve reliability and efficiency. In this situation understand the present condition and future performance of assets is important. Time frame of assets to be replace and which are the different consequences, financial as well as operational, are the paramount questions.

   But after 2014 all over the world Asset management standard ISO 55001:2014 provides the guidelines to power utilities. Asset owners are already focusing on asset optimum use, efficiency, operational parameters, maintenance strategy etc. but asset management

   emphasizes scientific and systematic approach to take care of assets. In any high voltage substation transformer cost will be 60% that of total substation cost [4]. During normal operation of transformer it will experiences various stresses like, thermal, chemical, environmental, operational etc. [5].Consequently transformer age approaches to end of life and probability of failure increases.

   Additionally regular overloading and short-circuit incidents on aged transformers may lead to unexpected premature failures, resulting in damage to customer relationships due to interruption of power supply.

   The major consequences of failure of power transformer are,

   1. Loss of cost and remaining life of existing

      asset.

   2. Unexpected financial burden for replacement of power transformer.

   3. Environmental effect due to oil leakage and Safety policy violation due to fire /flashover.

   The existing process of asset condition monitoring techniques involves monitoring dissolved gas analysis, oil screening test, FFA of oil, various testing results such as, thermal image, partial discharge, sweep frequency response analysis, dielectric frequency response analysis etc.[4] [6]. Each individual parameter data stated above have a different effect on the transformer. This effect does not have a linear effect on transformer age. It may be exponential. So this evaluation method for deciding present health status of the transformer is not enough. Conventional methods are not compatible to calculate health condition of transformer combining all available inputs.

   This limitation has been overcome and expressed in this paper to calculate the condition assessment of power transformers using Health Index (HI) technique. DNO common network asset indices methodology is adopted for calculation of health index in this paper. Weighting factor is defined for each input parameter considering the physical, environmental, operational, location conditions of transformers used for HI calculation in this paper.

   Health Index (HI) is a way of combining complex condition information to give a single numerical value as a comparative indication of overall condition.

   This health index calculation technique gives a health score

   of existing transformers along with Probability of Failure (PoF) and End of Life (EoL) data.

   it is an exponential function which, for a given asset, can be written as follows: [1]

2. DEVELOPING HEALTH INDEX

   With the available theoretical available data ideal health score is calculated. In actual practice the transformer duty and location factors affects the expected life. Normal theoretical life and expected life are important for calculation of aging rate as age is depends on functional

   Where,

   HIt = Health score at time t = Initial aging rate

   (1)

   and operational parameters. Initial health score is function of aging rate and defined health score for new asset i.e. 0.5 as per methodology adopted. Additional information such as measured and observed conditions are collected for final calculation of health index. These conditions are needed to be fixed by weighting method, generally called as calibration. This approach to development of health indices summarised in the schematic diagram below [3].

   To determine the rate of change of the health Score, the value of 1 must be determined. The initial aging rate is a function of health score of new asset( i.e. 0.5) and the end of an assts normal expected life (i.e. 5.5). Therefore, a different value of 1 must be calculated for each single asset based on its duty (load) and operating environment (e.g. indoor, outdoor, proximity to the coast etc.) as follows:[2]

   (2)

   Where,

   1 = Initial ageing rate

   1. Health scores-

      Fig.1. Flow chart for HI

      Hnew = Health Score of a new asset = 0.5

      Hexpected life = Health Score at the end of the expected life = 5.5

      Health score of each asset defined here is representated in a numeric presentation. The health score is calculated from combining complex input data such as transformer age, duty, environmental, functional, and operational parameters. The concept is illustrated in schematically in figure 2.[3]

      Fig.2. Concept of HI

   2. Initial Aging Rate ()-

      The rate of change of the health Score is not linear. For distribution assets, the degradation processes involved are all accelerated by the products of the process. Hence the rate of degradation increases as the processes proceed, i.e.

   3. Probability of Failure (PoF)-

      The probability of failure is depends on the HI of asset. For high health score the probability of failure of asst is very high and asset is near to its end of life. It involves the degradation parameters and illustrated as,

      (3)

      Where,

      HS = Health score

      PoF = Probability of Failure per annum k & c = Constants

   4. Initial Helth Score-

      The Initial Health Score is obtained from new asset health and aging rate as mentioned in bellow expression,[2]

      (4)

      Where,

      Hnew = New asset health score (0.5) 1 = Initial Ageing Rate

      Age = Current age of the asset in year

   5. Normal Expected Life-

      The Normal Expected Life is defined depending on asset manufacturing year and OLTC type. It will be finalised after discussion among asset care individuals and not fixed as per methodology.

   6. Expected Life-

      Expected Life is derived from Normal Expected Life, stated above and assets location factor (environmental conditions where asst is installed) and duty factor (functional duties of asset).[2]

      (5)

   7. Current Health Score-

   Having calculated an initial Heath Score for all assets, we can now consider the available condition information in order to improve on, or override, the initial Health Score value.

   In order to calculate the Current Health Score, the Initial Health Score is multiplied by a Health Score Modifier. The Health Score Modifier is derived from a number of Health Score Factors which are derived from condition information. For each condition measure, a Minimum and Maximum Health Score is also calculated, with the final Health Score being within these minimum and maximum boundaries.

   Fig.4 Health Score modifier schematic diagram

   1. Reliability Modifier-

      The reliability modifier is a direct input for each transformer and is used to reflect any reliability issues that exist with the unit. These may be reliability issues due to the make/model of the asset, or may reflect a particular asset that has a history of reliability issues.[2]

      Fig.5 Reliability modifier schematic diagram

   2. Future Health Score-

   The ageing rate is first recalculated for calculation of future health score. The initial Health Score was calculated using an initial ageing rate based on the asset expected life, duty and location. The Current Health Score also takes into account the condition information and, as such, recalculating the ageing rate using the asset age and the Current Health Score gives a more accurate indication of the expected future ageing of an asset.

   The Forecast Ageing Rate (2) is calculated as follows:

   Fig.3 Current Health Score schematic diagram

   H. Health Score modifier-

   Where,

   2 = Forecast Ageing Rate

   Hnew = Health Score of a new asset = 0.5 Hcurrent = Current Health Score

   (6)

   Where,

   Hfuture = Future Health Score.

   (7)

   Duty Factor 1
   Re f	Average % Utilisation	Factor
   1	70%	1
   2	> 70% and 85%	1.05
   3	> 85% and 100%	1.1
   4	> 100%	1.4
   5	Default	1

   Duty Factor 1
   Re f	Average % Utilisation	Factor
   1	70%	1
   2	> 70% and 85%	1.05
   3	> 85% and 100%	1.1
   4	> 100%	1.4
   5	Default	1

   Hcurrent = Current Health Score. 2 = Forecast Ageing Rate.

   t2 = Future year

   t1 = Current year

   K. End of Life-

   Duty Factor 2
   Re f	Maximum % Utilisation	Factor
   1	100%	1
   2	> 100% and 110%	1.05
   3	> 110% and 120%	1.2
   4	> 120%	1.4
   5	Default	1

   Duty Factor 2
   Re f	Maximum % Utilisation	Factor
   1	100%	1
   2	> 100% and 110%	1.05
   3	> 110% and 120%	1.2
   4	> 120%	1.4
   5	Default	1

   The number of years to end of life is calculated based on a defined Health Score relating to end of asset life. In this case end of life does not relate to the point at which an asset fails, but to the point at which the probability of failure becomes unacceptable.

   The number of years to end of life is calculated using the following equation.[1]

   Tapchanger Duty
   > From	<= To	Avg. No Daily Tap Operations
   0	7	7
   7	14	> 7 and 14
   14	21	> 14 and 21
   21	100	> 21
   None		Default

   Tapchanger Duty
   > From	<= To	Avg. No Daily Tap Operations
   0	7	7
   7	14	> 7 and 14
   14	21	> 14 and 21
   21	100	> 21
   None		Default

   (8)

   Where ,

   Hfuture =Future Health Score i.e. the end of life health index

   Hcurrent =Current Health Score 2 = Forecast Ageing Rate

   t2 =Years to end of life

   t1 =Current year

3. CALIBRATION SELECTION Calibration factors are decided for different inputs to

   calculate required output. Reference for this health score calculation is DNO common network asset indices methodology [1].

   TABLE I. LOCATION FACTOR

   Altitude Factor
   Ref	Distance	Factor
   1	100m	1
   2	> 100m and 200m	1
   3	> 200m and 300m	1.05
   4	> 300m	1.1
   5	Default	1

   Distance from Coast Factor
   Ref	Distance	Factor
   1	1km	1.1
   2	> 1km and 5km	1.05
   3	> 5km and 10km	1
   4	> 10km and 20km	1
   5	>20km	1
   6	Default	1

   Corrosion Factor
   Ref	Proximity with	Factor
   1	DUMPING GROUND	1.05
   2	CHEMICAL PLANT	1.05
   3	RAILWAY YARD	1
   4	QUARY	1
   5	SEWARAGE PLANT	1.1
   6	INDUSTRIAL BELT	1
   7	CHEMICAL PLANT + SEWARAGE PLANT	1.15
   8	DEFAULT	1

   TABLE II. DUTY FACTOR

   TABLE III. OBSERVED CONDITIONS

   Main Tank Condition
   Ref	Observed Condition	Description	Factor
   1	No Wear	The asset is as new	0.9 5
   2	Normal Wear	The asset component is fit for continued service. There is little deterioration	1
   3	Some Deterioration	e.g Minor corrosion but no leakages	1
   4	Substantial Deterioration	e.g. major corrosion or Welding defects leading to Oil leakage from main tank	1.1
   5	Default	No data available	1

   Cooler Condition
   R ef	Observed Condition	Description	Fac tor
   1	No Wear	The asset is as new	0.9 5
   2	Normal Wear	All Fans working	1
   3	Some Deterioration	All Fans working but noisy	1
   4	Substantia l Deterioration	one or more fans not working	1.0 5
   5	Default	No data available	1

   Oil Ave Temp
   Ref	Measured Condition	Description	Factor
   1	Normal	upto 65 degrees	1
   2	Moderate	65 to 75 degrees	1.05
   3	High	Above 75 degrees	1.1

   Oil Ave Temp
   Ref	Measured Condition	Description	Factor
   1	Normal	upto 65 degrees	1
   2	Moderate	65 to 75 degrees	1.05
   3	High	Above 75 degrees	1.1

   TABLE IV. MEASURED CONDITIONS

   4	Default	No data available	1
   Oil Max Temp
   Ref	Meas ured Conditi on	Description	Factor
   1	Nor mal	Less than 75 degrees	1
   2	Mod erate	75 to 85 Degrees	1.05
   3	High	Greater than 85 degrees	1.1
   4	Defa ult	No data available	1
   Winding Ave Temp
   Ref	Meas ured Conditi on	Description	Factor
   1	Nor mal	Less than 75 degrees	1
   2	Mod erate	75 to 90 degrees	1.05
   3	High	Greater than 90 degrees	1.1
   4	Defa ult	No data available	1

   TABLE V. OIL TEST MODIFIER

   Moisture Condition State Calibration (Mineral Oil)
   > Moisture (ppm)	<= Moisture (ppm)	Moisture Score
   -0.01	15	0
   15	30	2
   30	40	4
   40	50	8
   50	10000	10

   Acidity Condition State Calibration (Mineral Oil)
   > Acidity (mg KOH/g)	<= Acidity (mg KOH/g)	Acidity Score
   -0.01	0.1	0
   0.1	0.15	2
   0.15	0.2	4
   0.2	0.3	8
   0.3	10000	10

   Breakdown Strength Condition State Calibration (Mineral Oil)
   > BD Strength (kV)	<= BD Strength (kV)	BD Strength Score
   -0.01	30	10
   0	40	4
   0	50	2
   0	0000

   FFA Test Factor
   > FFA value (ppm)	<= FFA value (ppm)	FFA Test Factor
   -0.01	2	1
   2	3.3	1.1
   3.3	6.2	1.25
   6.2	7	1.4
   7		1.6

   FFA Test Factor
   > FFA value (ppm)	<= FFA value (ppm)	FFA Test Factor
   -0.01	2	1
   2	3.3	1.1
   3.3	6.2	1.25
   6.2	7	1.4
   7		1.6

   TABLE VI. FFA TEST MODIFIER

   TABLE VII. DGA TEST MODIFIER

   Hydrogen Condition State Calibration
   > Hydrogen (ppm)	<= Hydrogen (ppm)	Hydrogen Condition State
   -0.01	20	0
   20	40	2
   40	150	4
   150	200	10
   200	10000	16

   Methane Condition State Calibration
   > Methane (ppm)	<= Methane (ppm)	Methane Condition State
   -0.01	10	0
   10	20	2
   20	130	4
   130	250	10
   250	10000	16

   Ethylene Condition State Calibration
   > Ethylene (ppm)	<= Ethylene (ppm)	Ethylene Condition State
   -0.01	10	0
   10	20	2
   20	180	4
   180	300	10
   300	10000	16

   Ethane Condition State Calibration
   > Ethane (ppm)	<= Ethane (ppm)	Ethane Condition State
   -0.01	10	0
   10	20	2
   20	90	4
   90	150	10
   150	10000	16

   Acetylene Condition State Calibration
   > Acetylene (ppm)	<= Acetylene (ppm)	Acetylene Condition State
   -0.01	1	0
   1	5	2
   5	20	4
   20	100	8
   100	10000	10

   TABLE VIII. HEALTH SCORE MODIFIERS

   Health Score Modifier Tapchanger

   Setting Item	Value
   Health Score Factor 1 Divider	1.5
   Health Score Factor 2 Divider	1.5
   Health Score Max. No Factors	2
   Max Boundary	1

   Health Score Modifier Tapchanger

   Setting Item	Value
   Health Score Factor 1 Divider	1.5
   Health Score Factor 2 Divider	1.5
   Health Score Max. No Factors	2
   Max Boundary	1

   	Reliability Modifier
   6.58		(6- 7)	7.3 0	0.005 87	0.007 69	4.28
   3.49		(3- 4)	3.9 0	0.001 77	0.001 77	15.9
   3.14		(3- 4)	3.5 3	0.001 77	0.001 77	17.1
   4.98		(4- 5)	5.4 6	0.002 95	0.003 68	11.6
   10.0 0		(9- 10)	11. 11	0.017 68	0.023 53	0.0

   	Reliability Modifier
   6.58		(6- 7)	7.3 0	0.005 87	0.007 69	4.28
   3.49		(3- 4)	3.9 0	0.001 77	0.001 77	15.9
   3.14		(3- 4)	3.5 3	0.001 77	0.001 77	17.1
   4.98		(4- 5)	5.4 6	0.002 95	0.003 68	11.6
   10.0 0		(9- 10)	11. 11	0.017 68	0.023 53	0.0

   TABLE IX. FUTURE HEALTH SCORE

   Setting Item	Value
   Recalculated Ageing Rate Ratio Limit (B)	1.25
   As New HI	0.5
   Future Year	2
   Future Year Health Score Max HI	15
   Recalculated Ageing Rate Ratio Start Year (B)	10

   Setting Item	Value
   Factor Default	1
   Min HI Default	0.5

   Setting Item	Value
   Recalculated Ageing Rate Ratio Limit (B)	1.25
   As New HI	0.5
   Future Year	2
   Future Year Health Score Max HI	15
   Recalculated Ageing Rate Ratio Start Year (B)	10

   Setting Item	Value
   Factor Default	1
   Min HI Default	0.5

4. PRACTICAL APPROACH_CASE STUDY

   Total 211 no. of transformers having various MVA capacity, voltage level, different insulation medium, different types of OLTC included in this model. A health Score is calculated for each transformer and its associated tapchanger. The spreadsheet model contains a detailed calculation sheet which provides the values for each step in the calculation.

   TABLE X. DETAILS OF TRANSFORMER IDs

   TX Asset ID	Health Index Asset Category	Asset Register Category
   Transformer ID-1	33/11 kV	20MVA
   Transformer ID-2	33/11 kV	20MVA
   Transformer ID-3	33/11 kV	20MVA
   Transformer ID-4	33/11 kV	20MVA
   Transformer ID-5	33/11 kV	20MVA
   Transformer ID-6	33/11 kV	20MVA
   Transformer ID-7	33/11 kV	20MVA
   Transformer ID-8	33/11 kV	20MVA
   Transformer ID-9	33/11 kV	20MVA
   Transformer ID-10	33/11 kV	20MVA
   Transformer ID-11	33/11 kV	20MVA
   Transformer ID-12	33/11 kV	20MVA
   Transformer ID-13	33/11 kV	20MVA
   Transformer ID-14	22/11 kV	10MVA
   Transformer ID-15	22/11 kV	10MVA

   Probability of failure
   Cur rent Hea lth Sco re	Cur rent Hea lth Sco re Ban d	Fut ure Health Score	Curre nt PoF	Future POF (Y2)	Year s to End of Life
   1.93	(1- 2)	2.2 6	0.001 77	0.001 77	18.7
   5.50	(5- 6)	6.0 8	0.003 75	0.004 81	8.71
   2.38	(2- 3)	2.7 1	0.001 77	0.001 77	19.7
   1.65	(1- 2)	1.9 1	0.001 77	0.001 77	22.0
   4.50	(4- 5)	5.1 0	0.002 32	0.003 13	10.0
   3.51	(3- 4)	4.1 5	0.001 77	0.001 92	10.5
   3.73	(3- 4)	4.1 7	0.001 77	0.001 94	14.8
   4.03	(4- 5)	4.4 8	0.001 80	0.002 29	14.0
   3.49	(3- 4)	3.7 1	0.001 77	0.001 77	10.7
   1.64	(1- 2)	1.9 4	0.001 77	0.001 77	19.4

   Probability of failure
   Cur rent Hea lth Sco re	Cur rent Hea lth Sco re Ban d	Fut ure Health Score	Curre nt PoF	Future POF (Y2)	Year s to End of Life
   1.93	(1- 2)	2.2 6	0.001 77	0.001 77	18.7
   5.50	(5- 6)	6.0 8	0.003 75	0.004 81	8.71
   2.38	(2- 3)	2.7 1	0.001 77	0.001 77	19.7
   1.65	(1- 2)	1.9 1	0.001 77	0.001 77	22.0
   4.50	(4- 5)	5.1 0	0.002 32	0.003 13	10.0
   3.51	(3- 4)	4.1 5	0.001 77	0.001 92	10.5
   3.73	(3- 4)	4.1 7	0.001 77	0.001 94	14.8
   4.03	(4- 5)	4.4 8	0.001 80	0.002 29	14.0
   3.49	(3- 4)	3.7 1	0.001 77	0.001 77	10.7
   1.64	(1- 2)	1.9 4	0.001 77	0.001 77	19.4

   TABLE XI. RESULTS FOR SAME IDs

5. CURRENT AND FUTURE HELATH SCORE PROFILE

   Graph 1: Current and Future HI profile

   Note: Future health score considered for after 2 years

6. RESULT SUMMARY For top results summary is tabulated bellow:

   TABLE XII. CURRENT HEALTH SCORE

   Current Health Score
   TX Asset ID	Trans. Name	Transfor mer	Tap Chang er	All Com pone nts
   Transformer ID- 11	20MVA -1	10.00	3.14	10.00
   Transformer ID- 90	10MVA -1	10.00	5.82	10.00
   Transformer ID- 124	20MVA -2	10.00	3.75	10.00
   Transformer ID- 140	10MVA -1	10.00	5.50	10.00

   TABLE XIII. HI PROFILE

   Current Health Index Profile
   Category	Number of Assets
   (0-1)	35
   (1-2)	62
   (2-3)	24
   (3-4)	13
   (4-5)	17
   (5-6)	24
   (6-7)	21
   (7-8)	6
   (8-9)	1
   (9-10)	8
   (10+)	0
   No Result	0
   Total	211

   Graph 2: Current HI summary

   TABLE XIV. HI BANDS

   Detailed result for highest health score i.e. 10 tabulated herewith for Transformer ID-11.

   TABLE XV. DETAILS FOR HIGH HI ASSET

   Asset ID	Transformer ID-11
   Transformer Name	20MVA-1
   Current Health Score (All Components)	10
   Current Health Score (Transformer)	10
   Current Health Score (Tapchanger)	3.14
   Years to End of Life (All Components)	0
   Transformer Initial HI
   Expected Life	38.10
   Duty Factor	1.00
   Location Factor	1.05
   Actual Age	27.00
   Initial Health Score	2.74
   Current Health Score	10.00
   POF
   Current PoF	0.02
   Future POF (Y2)	0.02
   Tapchanger Initial HI
   Expected Life	33.33
   Duty Factor	1.00
   Location Factor	1.05
   Actual Age	27.00
   Initial Health Score	3.49
   Tapchanger Health Score
   Observed Condition Modifier	1.00
   Measured Condition Modifier	1.00
   Oil Test Modifier	0.90
   Health Score Factor	0.90
   Health Score Max HI	10.00
   Current Health Score	3.14

   Transformer ID- 148	20MVA -1	10.00	5.25	10.00
   Transforme ID- 45	20MVA -1	9.75	3.37	9.75
   Transformer ID- 111	10MVA -2	9.23	4.95	9.23
   Transformer ID- 94	10MVA -1	9.11	5.78	9.11
   Transformer ID- 193	10MVA -1	8.43	5.78	8.43
   Transformer ID- 114	10MVA -1	7.76	5.78	7.76
   Transformer ID- 20	10MVA -1	7.52	6.05	7.52
   Transformer ID- 47	10MVA -1	7.49	5.50	7.49
   Transformer ID- 40	10MVA -1	7.24	5.50	7.24

   Transformer ID- 148	20MVA -1	10.00	5.25	10.00
   Transformer ID- 45	20MVA -1	9.75	3.37	9.75
   Transformer ID- 111	10MVA -2	9.23	4.95	9.23
   Transformer ID- 94	10MVA -1	9.11	5.78	9.11
   Transformer ID- 193	10MVA -1	8.43	5.78	8.43
   Transformer ID- 114	10MVA -1	7.76	5.78	7.76
   Transformer ID- 20	10MVA -1	7.52	6.05	7.52
   Transformer ID- 47	10MVA -1	7.49	5.50	7.49
   Transformer ID- 40	10MVA -1	7.24	5.50	7.24

7. MAJOR CONTRIBUTORS AND RECOMENDATIONS PRAPOSED

   Graph 3: Major contributors category wise

   Category	Health Index
   Bad	Above & including 8
   Poor	Above & including 5.5 & below 8
   Fair	Above 4 & below 5.5
   Good	4 or Below

   Category	Health Index
   Bad	Above & including 8
   Poor	Above & including 5.5 & below 8
   Fair	Above 4 & below 5.5
   Good	4 or Below

   From above graph it is clear that in Bad category HI major contributors are FFA and DGA. For this category having major contributor FFA there is no recovery of cellulose electrical and mechanical properties by any maintenance process. So the recommendation is only repair or replacement of transformer. For DGA, investigation of cause for gas generation by Duval triangle method and corrective action is recommended. If not possible to take corrective action on site then repair, rewinding or replacement is recommended.

   For Poor and Fair category HI the recommendations are summarised as bellow,

   	Recommendations to Management
   FFA
   DGA	1. Procurement of online transformer oil DGA monitoring set.
   Oil Condition
   Measured condition
   Observed Condition	1. Procurement of new radiators for heavy leakage cases on priority basis.
   OLTC	1. Procurement of Online filter (OFU) for OLTC unit. 2. Procurement of Dynamic resistance measurement (DRM) kit for OLTC contact healthiness check up.

   1. Procurement of transformer main tank online filtration portable machine.

   2. Attending conservator air cell leakage abnormalities. Provision of budget for new air cell purchase and service order to attend same.

   1. Procurement of transformer main tank online filtration portable machine.

   2. Attending conservator air cell leakage abnormalities. Provision of budget for new air cell purchase and service order to attend same.

   1. Procurement of smart breather, various online physical condition monitoring sensors.

   2. Procurement of tan delta measurement kit & inclusion of same test as routine test for transformer.

   1. CONCLUSION

      In this health score model we have formulated current health score, future health score (after 2 years), current and future PoF and end of life (EoL) for each asset. Now it is easy to take action on assets considering their health score banding. The spreadsheet is so designed that inputs can be changed as per change in observation status. The calculations are done automatically and results are available immediately. Also a number of asset additions are possible with this tool. This model will help as a reference to change maintenance strategy or replacement of any component / total asset from the network.

   2. REFERENCES

1. DNO common network asset indices methodology, version 2.0 dated 01.09.2020

2. J. Serra, A. de la Fuente, A. Crespo, A model for life cycle cost calculation based on asset health index, International Conference on Smart Infrastructure and Construction 2019 (ICSIC)

3. D.T. Hughes, The use of Health indices to determine end of life and estimate remnant life for distribution assets, CIRED, 17th International Conference on Electricity Distribution, Barcelona, 12-15 May 2003

4. Ali Naderian Jahromi, Ray Piercy, Stephen Cress, Jim R. R. Service, and Wang Fan,An Approach to Power Transformer Asset Management Using Health Index, IEEE Electrical Insulation Magazine, pp. 20-34,2009

5. D. Arvind, S. Khushdeep and K. Deepak, "Condition monitoring of power transformer: A review," 2008 IEEE/PES Transmission and Distribution Conference and Exposition, 2008, pp. 1-6, doi: 10.1109/TDC.2008.4517046.

6. Gabriel, Tanasescu & Dragomir, Oana & Voinescu, L & Gorgan, Bogdan & Notingher, Petru & Suru, T & Melinte, C. (2012). Assessment of Power Transformers Conditions Based on Health Index.