AbstractEnvironmental concerns have resulted in

distribution companies becoming more cognitive of the amount of carbon emissions they produce. Research has shown that distribution transformer losses comprise a significant amount of the overall losses on a distribution and transmission system. Although some of the losses are considered the cost of operations, it may be possible to reduce the total losses associated with overloaded transformers depending upon the amount of overload and the efficiency of the replacement transformer. Through the use of a Smart Grid monitoring system, overloaded distribution transformers can be identified for replacement as soon as loads become sufficient to shorten the expected life of a transformer. This paper examines how to utilize a Smart Grid monitoring system in conjunction with loss of life calculations to identify overloaded transformers. Also described in the paper are the necessary input requirements, algorithm requirements, notification threshold levels, and an economic analysis that is specific to a Smart Grid overloaded transformer replacement program.

Index TermsDistribution transformer, no-load losses, line- load losses, loss of life, Smart Grid

I. INTRODUCTION Smart Grid will consist of an extensive communication system that allows for continuous monitoring of most

utility devices [1][2][3]. Monitoring distribution transformer loads and environment conditions continuously will provide distribution companies with the ability to identify transformers that begin to routinely serve loads that are higher than their rating, which results in losses that are higher than those experienced at the rated full-load. The higher the losses produced, the more carbon emissions produced. As we move into the 21st century, utility companies are experiencing increasing pressures from governments and customers to become more environmental friendly.

Losses associated with distribution transformers can make up 26.6% of the average transmission and distribution losses of a system, which can make up 7.5% of the power generated [4]. Overloaded distribution transformers can have losses that are considerably higher than those at rated full-load since line-load losses are a function of the square of the load current and can be 5 times greater than no-load losses at rated load [5].

Manuscript received June 15, 2009 Kerry D. McBee is with Colorado School of Mines, Golden, Colorado, CO

80401 as a PhD student. He is also affiliated with Xcel Energy (email: power.engineer@ymail.com)

Many distribution companies presently utilize transformer overload limits or algorithms that are based on historical load data, load profile estimates, and/or transformer loss of life calculations to predict transformer failure [6][7]. Loss of life calculations utilizes transformer loading, ambient temperature, and transformer hot-spot temperature to predict the loss of life of a transformer [7][8]. The most commonly used algorithms, which distribution companies utilize to develop loading levels and overload limits, are founded in IEEE Standard C57.91, IEEE Std. C57.100, and/or IEC 354. Unfortunately, the loss of life prediction results are only as accurate as the load and temperature data applied to them, which may be based on peak load values or demand estimates from energy meters [6]. A Smart Grid can provide actual load data to improve the accuracy of the loss of life calculations and also provide immediate notification to utility personnel so that the overloaded transformers are replaced as soon as possible, thereby reducing losses immediately.

This paper examines how a Smart Grid monitoring system can be utilized to incorporate a transformer overload replacement program that will minimize losses, reduce carbon emissions, and lower operating costs. Although a Smart Grid program such as this may be merged with an existing company program that routinely calculates transformer loss of life, this paper describes a completely autonomous program with the understanding that variations in structure may occur depending upon budgetary restrictions, company goals, and existing equipment and programs.

Section II of the paper focuses on the advantages of identifying overloaded transformers, while Section III details the necessary structure of such a program and the required algorithm inputs. The required algorithms for a Smart Grid transformer overload identification procedure are detailed in Section IV. An example utilizing the described procedure is then performed in Section V.

II. OVERLOAD TRANSFORMER REPLACEMENT

Replacing a distribution transformer with the next higher size typically results in more operational losses. Although line-load losses are decreased due to the same load current flowing through a smaller winding resistance, no-load losses are increased due to increased core requirements. Since no-load losses occur 24 hours a day, 365 days a year, the majority of distribution transformer losses may occur due to these types of losses.

Reducing Distribution Transformer Losses Through the use of Smart Grid Monitoring

Kerry D McBee, Student Member, IEEE, Marcelo G. Simes, Senior Member, IEEE

A

Transformers that serve loads greater than rated capacity may experience line-load losses greater than the accumulative no-load losses depending upon ambient temperature and duration of overload conditions. Unfortunately, without an extensive monitoring system, utility companies find it difficult to identify these transformers [6].

Replacing overloaded transformers with larger and possibly more efficient transformers can be cost effective and environmentally friendly depending upon the duration of load conditions, ambient temperatures, and efficiency of existing transformer and replacement transformers. The Department of Energy (DOE) has recently established distribution transformer efficiencies that are higher than the average efficiencies of transformers that were installed in the past [9]. Existing inefficient 25kVA liquid-immersed distribution transformers may have efficiencies as low as 97%, whereas the same size DOE compliant transformer is 98.91% efficient [5][9].

Without an active monitoring system, many utilities

determine daily load profiles by utilizing load probability predictions that are derived from customer type, measured historical load data, or neural networks [6][7][10]. Although these approaches may be sufficient for an overall load management system, they may not possess enough accuracy to identify a specific distribution transformer as overloaded with a high level of confidence [4]. Even if companies do employ a proactive replacement program based on loss of life calculations, the confidence in which the program is implemented is low due to the lack of real physical evidence that loading issues or inherent failure conditions exist. Understanding that the use of estimated values may result in

predictions that are too conservative or extreme, many replacement programs fall short of being able to identify transformers before they reach the end of their life. The extensive monitoring system of a Smart Grid can improve the accuracy of a companys overloaded transformer identification program.

III. SMART GRID FUNCTIONS A Smart Grid system consists of a communication system

that allows for two-way communication between the distribution company and customer, while also providing the distribution company with a means to monitor system and device attributes [1][2][3]. Existing Smart Grid technology allows for recording of transformer voltage and load values [11]. Including environment and winding temperature sensors on transformers will allow for real data to be utilized in loss of life calculations.

Data provided by a Smart Grid monitoring system is input into a multi-agent or Smart Grid software to determine if system actions are required [2][3]. A multi-agent system is the description given to a system that works in conjunction with several agents to monitor and control a distribution system without human interaction. Multi-agent technologies can perform analysis based on monitored inputs to determine optimal system configuration, optimal distributed generation, active Voltage and VAR compensation requirements, and demand response requirements [3].

The multi-agent or Smart Grid software that can identify overloaded transformers and determine whether replacement is warranted would require the ability to perform transformer loss of life calculations and possibly economic evaluations [5][8]. These calculations would require actual load data and two types of information that must be manually input into the initial programming. Figure 1 illustrates the basic structure of a Smart Grid overloaded replacement program.

The first type of input data described consists of specific transformer information. How the information is applied is described in latter sections of the paper. Information required include:

KVA rating of the transformer Losses (no-load and line-load losses) Installation date Capital costs of next size greater transformer

Information regarding company costs and goals comprise the second type of information necessary for calculating loss of life and economic evaluations. How the information is applied is also described in a latter section. Information required includes: Loss of Life/Actual Life ratios (LOL/AT) Book life value of transformer Attributes necessary to calculate no-load costs (A)

and load-line losses (B) of the transformer

IV. IDENTIFYING OVERLOADED TRANSFORMERS

Transformer sensors that are connected through a Smart Grid communication system will provide real-time data that is utilized to evaluate the life expectancy of a transformer based on load and environmental conditions.

A. Smart Grid Calculation Requirements The Smart Grid algorithms must determine the following:

1) Calculate the hourly loss of life of a transformer. 2) Identify transformer as overloaded. 3) Determine the exhausted life of the transformer based on

load and environmental conditions. 4) Determine the remaining life of the transformer based on

load, environmental conditions, and installation date. 5) Perform economic evaluation that compares replacing

overloaded transformer to leaving the overloaded transformer installed.

B. Calculating Loss of life daily The loss of life calculations determine the rate at which the

internal insulation of a transformer deteriorates. The IEEE standard utilizes an ambient temperature of 20C (68F), hotspot temperature of 110C (230F), and rated load as the normal conditions in determining a life expectancy of 180,000 hrs [8]. Loading above rated capacity during ambient temperatures above 20C may have a significant effect on the life of the transformer. Table 1 illustrates the daily load cycle and temperatures of an overloaded transformer. Utilizing the loss of life equations described in [8], the daily loss of life for a 24-hour period is calculated to be 56.88 hrs.

Computation time and data storage may limit the amount of data that can be stored continuously. Deleting loss of life input information after the calculations are performed, which could be performed hourly, can minimize data storage.

C. Indentifying Overloaded Transformers Smart Grid software or a multi-agent should be set to flag

transformers that are expending more life than expected. This paper considers a transformer that looses more than 24

hours of life in a 24-hr period as overloaded for that specific period. However, just because a transformer is overloaded does not necessarily mean the transformer will not last its entire expected life. This paper utilizes a Loss of Life to

Actual Time ratio (LOL/AT) to measure the longevity of a transformer.

Having a LOL/AT ratio that is greater than 1 indicates the transformer is loosing life faster than the expected life. The higher the LOL/AT ratio, the more life is lost. Although a transformer may be overloaded for a week, the determination of LOL/AT should be calculated utilizing a minimum of one years worth of data to account for off and on-peak periods.

Consider a transformer that exhausts 36 hrs of loss every 24-hr period for three weeks (21 days) a year, which is referred to as the on-peak period. During the remainder of the year (344 days), the loss of life for a single 24-hour period never goes above 15 hrs. The highest ratio possible for the load conditions during that year would be 0.675, which is illustrated in equation 1. A ratio of 0.675 suggests that the transformer is expending life at a rate that is less than the expected life of a transformer. If the same transformer exhausted 36 hrs of life daily for 300 days of year, the LOL/AT ratio would be 1.34, which suggests the transformer is expending life faster than the expected life of the transformer.

( ) ( )[ ] 675.036524

344152136=

+=

dayshrsdayshrsdayshrs

ATLOL (1)

D. Determining the Exhausted Life of the Overloaded Transformers

Determining the exhausted life of the transformer may be the most difficult aspect of incorporating such a replacement procedure. If the overloaded transformer was installed after

TimeLoad (kVA)

ambient Temp (Cel)

Hotspot (Cel)

Loss of life (hrs) Total

0:00 7 18.6 74.6 0.0185 0.01851:00 9 17.5 70 0.0104 0.02892:00 9 16 66.1 0.0063 0.03523:00 9.2 16.1 67 0.0071 0.04234:00 10 15.5 58 0.0021 0.04445:00 10 16 55.7 0.0015 0.04596:00 11 17 54.6 0.0013 0.04737:00 20 18 80 0.0358 0.08318:00 28 19.7 85 0.0649 0.14809:00 22 20 76 0.0220 0.1700

10:00 19 22.4 89 0.1031 0.273111:00 18 24.5 104 0.5362 0.809312:00 23 26.6 121 2.9845 3.793813:00 32 27 122 3.2864 7.080214:00 35 28.5 128 5.8009 12.881115:00 37 29 130 6.9842 19.865316:00 42 31 131 7.6582 27.523517:00 43 29 132 8.3935 35.917018:00 46 29.3 130 6.9842 42.901119:00 43 28 132 8.3935 51.294620:00 41 26.3 120 2.7089 54.003521:00 37 24 116 1.8296 55.833122:00 22 23.4 107 0.7340 56.567123:00 15 20 99 0.3141 56.8812

TABLE 1DAILY LOSS OF LIFE CALCULATION RESULTS (CONDITION

- OVERLOADED) - 25kVA

the Smart Grid monitoring system was installed, determining the exhausted life will consist of utilizing actual data and will not be difficult to calculate. However if the transformer was installed prior to Smart Grid implementation, the distribution company will have to estimate the exhausted life based on current load and environmental readings.

One method for predicting the amount of life exhausted on a transformer installed prior to Smart Grid implementation utilizes the existing company methods of calculating loss of life without continuously monitored data. This method is probably the least time consuming since utility companies can merely utilize information from prior or existing transformer load programs; however, this approach suffers from the lack of accuracy that the previous program suffers from.

Another approach to determine the amount of life that has been exhausted on a pre-Smart Grid installed transformer is to apply recorded monitored load information to the period between transformer installation and Smart Grid implementation. The average load growth and engineering judgment should be applied backwards to the existing demand to account for load growth since installation. Once prior load history and corresponding hot spots are estimated, loss of life calculations are performed to determine the amount of life exhausted. Once the exhausted life is determined, it should be subtracted from the overall expected life of the transformer to calculate the remaining life of the transformer.

Due to the complexity of this procedure, many companies may choose to only calculate the exhausted life only after a transformer is identified as being overloaded. Only performing these calculations when necessary may greatly reduce calculation time, time spent researching transformer installation date, and data storage requirements.

E. Determining the Remaining Life of the Overloaded Transformers

The calculated or estimated exhausted life of the transformer is subtracted from the overall expected life to determine the remaining life of the overloaded transformer. IEEE defines the life of transformer as 180,000 hours, whereas IEC 354 doesnt specify a time but implies the life is 30 years [8]. A study performed by Oak Ridge National laboratory concluded that the typical distribution transformer life is 31.95 years [5]. Utility companies typically utilize a transformer book life of 30 35 years (262,800 306,600hr) [5].

The loss of life calculations in the previous section determined that the transformer described in Table 1 exhausted 56.88 hours of life a day. If this daily exhaustion of life occurred 365 days a year, which is unlikely, the amount of life exhausted in a year would be 20,761 hrs (56.88 hrs/day 365 day). If the transformer experiences this type of load from install to failure, and the expected life is 180,000, the transformers expected life would only be 8.67 years (180,000 hrs / 20,761 hrs/yr). Assuming the transformer was flagged in year 2, the remaining life would be 6.67 years.

F. Determining Replacement Costs Utility companies commonly utilize the Total Owning Cost

(TOC) equation to evaluate the economic value of a transformer [6]. The typical TOC calculations utilize no-load (NLL) and line-load losses (LL) at rated capacity, the costs of no-load (A) and line-load losses (B), salvage and refurbish costs, and transformer purchase cost to evaluate the cost of owning a transformer. With several changes made to reflect the utilization of actual load data, TOC calculations can be utilized to evaluate transformer replacement on a Smart Grid.

There are several methods for calculating no-load costs and line-load costs. This paper will not address the different methods, but acknowledges that most of them utilize the present worth value of energy costs, fixed costs, system capacity costs, annual loss factor, and other factors that are affected by losses [5]. A and B values, whose units are $/watt, do not change for typical transformer purchase evaluations. However, to compare transformer options for periods less than the typical life utilized for transformer purchases, the A and B quantities must reflect the shortened time frame and not the typical life of the transformer. Therefore, the Smart Grid software, or multi-agent, must be able to determine A and B values for the time period (k), which is equal to the remaining life of the transformer.

An additional change to the TOC calculation includes the utilization of actual load-line loss data instead of rated losses. The rated load-line losses are multiplied by the square of average hourly demand (Pd) in per unit to account for actual loading conditions. This change reflects the actual load profile of the transformer and improves the accuracy of the economic evaluation. The standard TOC and Smart Grid TOC equations for transformer replacement are illustrated in equations (2) and (3). Transformer purchase cost is added to the TOC equation, while salvage costs are subtracted.

LLBNNLATOC += (2)

2)( dSmartGrid PLLBNNLATOC += (3)

V. EXAMPLE

A 25kVA, 97.1% overhead liquid-immersed transformer is installed on a Smart Grid system with initial load conditions as illustrated in Table 2. Monitored data from the Smart Grid indicates that the load profile remains constant over the first 5 years of operation with no substantial load growth. The average LOL/AT ratio for the first 5 years is 0.3256, which indicates the transformer is properly sized and should last the remainder of its book life.

Air conditioning units are installed throughout the neighborhood in year 6. Monitored load data indicates that in the first year after air conditioning installation, the average daily profile during May through September is similar to the

profile in Table 1. The LOL/AT ratio for the sixth year of operation is 1.178, which indicates that the transformer may not last its entire book life. The transformer is flagged as being overloaded and Smart Grid software, or a multi-agent, performs replacement analysis.

Loss of life calculations indicate that in the first 5 years of operation, the daily loss of life for the transformer was 7.816 hrs in a 24 hour period, which results in a loss of life of 2,852 hrs for each year. Therefore, in the first six years of operation, the transformer has exhausted 24,585 hrs (14,264 hrs first 5 years plus 10,322 hrs in year six) of its 180,000 hrs of life, which leaves 155,415 hrs of remaining life, or 15.06 years. This duration is utilized as the time frame (k) in the economic evaluation.

The next decision to make is to determine whether or not the replacement of the transformer is warranted based on economic impact. The cost of replacing the transformer may show that suffering the losses for a longer period of time is warranted.

The Smart Grid TOC is calculated for the overloaded transformer and replacement transformer to determine if an upgrade is an acceptable economic decision. For the utility company in the example, the purchase cost of a 98.3% efficient 50kVA transformer is approximately $1,700. The company no longer utilizes 25kVA transformer, so the replacement transformer will be salvaged at a cost of $400. The A and B costs are calculated for period (k) and are determined to be 2.15 and 0.35 $/watt. The present worth value of no-load and line-load losses for the overloaded transformer are determined to be $1,699 and $2,079, while the present worth value of losses for the replacement transformer is $361 and $1,889.

The calculated Smart Grid TOC for leaving the transformer until failure is $3,779, while the owning costs of replacing the transformer is $3,550. These results indicate that upgrading the transformer is a better economic decision by $229. It should be noted that the results are very dependent upon A and B quantities. If both A and B are reduced by $0.10, the best economic decision by $163 would be to leave the existing transformers. This is the main reason that A and B quantities should be calculated for each individual case.

Distribution companies may have other considerations that do not necessarily translate to costs. One benefit of replacing the transformer proactively is the elimination of an unplanned outage due to transformer failure, which affects the companys overall reliability and customer service. Depending upon a companys position on preserving the environment, they may have a minimal acceptable loss when reducing carbon emissions, which are typically accounted for in the calculation of A and B.

The avoided losses by upgrading the transformer to a DOE recommended efficient transformer 15 years prior to failure is 60,432 kWh for the remaining life of the overloaded transformer. The losses avoided per transformer are small when viewed on its own. However, utility companies may experience several hundred overloaded transformers a year. The same utility company in the example averages 300

overloaded transformers a year. If 10% of the transformers identified as overloaded were experiencing similar conditions to the one in the example, in a single year the company would avoid 1,812,960 kWh of future generation.

VI. OBSTACLES TO PROGRAM IMPLEMENTATION

The Smart Grid will provide accurate load and possibly

temperature information, but other factors may reduce the accuracy of the results.

Overloaded transformer evaluations are heavily dependant upon determining the exhausted and remaining life of the transformer. Without existing monitoring equipment, it may be difficult to calculate these values for a distribution transformer that was installed prior to Smart Grid implementation.

To accurately predict loss of life of a distribution transformer, the hottest spot within the transformer must be known. Although this can be monitored with sensors, the implementation costs of said sensors may be significant. Relying upon ambient temperatures to calculate hot spot temperatures may reduce the accuracy of the results.

VII. CONCLUSION

A Smart Grid has the capability of actively monitoring distribution transformers, which if applied to Smart Grid software or a multi-agent, have the ability to identify overloaded transformers without human interaction. By utilizing actual demand data, unlike many existing programs, the accuracy of calculating transformer loss of life is improved. Once a transformer is identified, the software or a multi-agent performs economic evaluations based on utility

TimeLoad (kVA)

ambient Temp (Cel)

Hotspot (Cel)

Loss of life (hrs) Total

0:00 19.5 9.5 74.6 0.0185 0.01851:00 19.5 9.3 73.3 0.0158 0.03432:00 14.975 8.5 66.1 0.0063 0.04063:00 16.375 8.7 67.9 0.0079 0.04854:00 13.35 8.8 58 0.0021 0.05065:00 12.5 9.5 55.7 0.0015 0.05226:00 13.65 10.8 54.6 0.0013 0.05357:00 14 12.2 55.8 0.0016 0.05518:00 16.25 13.8 57 0.0019 0.05699:00 19.5 15.4 64.1 0.0048 0.0618

10:00 20.025 17 69.5 0.0097 0.071511:00 20.75 18.4 76.3 0.0229 0.094412:00 22.05 20 79.8 0.0350 0.129413:00 23.25 20.8 86.5 0.0773 0.206714:00 24 21.8 89.8 0.1130 0.319615:00 24.75 21.9 110.2 1.0207 1.340316:00 29.5 21.5 114.2 1.5293 2.869617:00 30 20.4 115.9 1.8115 4.681118:00 26.5 19.2 114.3 1.5447 6.225819:00 25.25 18.4 110.1 1.0103 7.236120:00 22.5 16 97.8 0.2757 7.511721:00 21.675 15.3 93.2 0.1658 7.677622:00 20 12.5 84.5 0.0612 7.738823:00 19.25 11.9 84.2 0.0591 7.7979

DAILY LOSS OF LIFE CALCULATION RESULTS (CONDITION-ADEQUATELY SIZED) - 25kVA

TABLE 2

economic goals to determine if a replacement is warranted. Only if the replacement is warranted will utility personnel be notified. Implementing such a program can be environmental friendly while also being economically sound.

VIII. REFERENCES [1] Department of Energy, Title XIII, [online]. Available:

http://www.oe.energy.gov/DocumentsandMedia/EISA_Title_XIII_Smart_Grid.pdf

[2] R.E. Brown, Impact of smart grid on distribution system design, Power and Energy Society General Meeting Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE, Jul 2008, pp. 1-4.

[3] M. Pipattanasomorn, H. Feroze, S. Rahman, Multi-agent systems in a distributed smart grid: design and implementation, Power System Conference and Exposition, 2009, (PES09), Mar 2009, pp. 1-5.

[4] J Olivares, Y Liu, J Canedo, R Escarela-Perez, J Driesen, P Moreno, Reducing losses in distribution transformers, IEEE Transaction on Power Delivery, vol 18, issue 3, Jul 2003, pp 821 826.

[5] B. Kennedy, Energy Efficient Transformers. New York: MacGraw-Hill, 1997 pages 63 65, 72-73, 135.

[6] A. Galindo, J. Jardini, L. Magrini, S. Ahn, Distribution Transformer Losses Evaluation: A New Analytical methodology and artificial Neural Network Approach, IEEE Transaction on Power System, vol 24, issue 2, May 2009, pp. 705712.

[7] J. Jardini, H. Tahan, H. Schmidt, C. De Oliveira, S. Ahn, Distribution transformer loss of life evaluation: a novel approach based on daily load profile, IEEE Transaction on Power Delivery, vol 15, issue 1, Jan 2000, pp. 361-366.

[8] K. Najdenkoski, G. Rafajlovski, V. Dimcev, Thermal Aging of Distribution Transformers According to IEEE and IEC Standards, IEEE Power Engineering Society General Meeting, 2007, Jun 2007, pages 1 5

[9] Department of Energy, Environmental Assessment for adopted energy conservation standards for distribution transformers, July 2007 [online]. Available: http://gc.energy.gov/NEPA/nepa_documents/ea/ea1565/EA-1565.pdf

[10] J. Jardini, C. Tahan, S. Ahn, E. Ferrari, Distribution Transformer Loading Evaluation Based on Load Profiles Measurements, IEEE Transaction on Power Delivery, vol 12, issue 4, Oct 1997, pp. 1766 1770.

[11] CURRENT Group Webpage. [Online]. Available: http://www.currentgroup.com/sensing.php

IX. BIOGRAPHIES Kerry D. McBee (BS99-MS00) is pursuing his Ph.D. degree in the Department of Engineering at Colorado School of Mines, which is where he received his B.Sc. degree in 1999. He received his M.Sc. degree in Electric Power Engineering at Rensselaer Polytechnic Institute, Troy, New York, in 2000. During his career he has focused on power quality, reliability, forensic engineering, and distribution design for companies such as NEI Power Engineers, Peak Power Engineering, Knott Laboratory, and Xcel Energy. His fields of interest include Smart Grid implementation affects upon distribution engineering and utility operations. Marcelo G. Simes (S89-MS95-SM98) received the B.Sc. and M.Sc. degrees in electrical engineering from the University of So Paulo, So Paulo, Brazil, in 1985 and 1990, respectively, the Ph.D. degree from The University of Tennessee, Nashville, in 1995, and the D.Sc. degree from the University of Sao Paul, So Paulo, Brazil, in 1998. He joined the faculty of the Colorado School of Mines, Golden, in 2000 and has been working to establish research and education activities in the development of intelligent control for high power electronics applications in renewable and distributed energy systems. Dr. Simes received the NSF Faculty Early Career Development (CAREER) in 2002. He served as the Program Chair for PESC05, Power Electronics Specialists Conference, and the Conference Chair for PEEW05, Power Electronics Education Workshop, both held in Brazil. He has been

actively involved in the Steering and Organization Committee of the IEEE/DOE/DOD 2005 International Future Energy Challenge.

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