IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 6, NOVEMBER Kerry D. McBee, Member, IEEE, and Marcelo G. Simões, Senior Member, IEEE

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1 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 6, NOVEMBER General Smart Meter Guidelines to Accurately Assess the Aging of Distribution Transformers Kerry D. McBee, Member, IEEE, and Marcelo G. Simões, Senior Member, IEEE Abstract Smart grid technology has enhanced the ability to monitor numerous aspects of the distribution system for improving system performance and reducing system losses. One benefit is the continuous assessment of insulation degradation within distribution transformers, which is commonly referred to as a loss of life assessment. Calculating the expended life of a transformer consists of determining its winding hotspot temperature, which fluctuates with customer demand, ambient temperature, and cooling characteristics. Most residential transformers in service today do not possess the ability to connect to a smart grid communication system for continuous monitoring; therefore, they rely upon fusing for protection against extreme loading conditions. Smart meters can provide utility companies with the information required to identify distribution transformers that are experiencing higher than rated losses that can ultimately reduce their expected life. The research presented in this paper defines the smart meter functions required to accurately assess the aging of distribution transformers according to IEEE Std C57.91 and C To establish general accuracy guidelines, transformer loading indices were developed to evaluate acute excessive loading, long-term excessive loading, and excessive loading due to harmonics. Metering functions evaluated included power factor, harmonic demand, and ambient temperature. Index Terms Distribution transformer, harmonics, loss of life, smart grid, smart meters, total harmonic distortion (THD). I. INTRODUCTION SMART grids consist of an extensive communication system that allows for continuous monitoring of most utility devices [1] [5]. Monitoring distribution transformer loads and environmental conditions provide distribution companies with the ability to continuously assess the health of distribution transformers by routinely calculating their aging rate. As we move further into the 21st century where global emphasis on carbon emissions is continually increasing, managing an asset that can comprise as much as 26% of overall system losses is vital to operating efficiently [6]. Many distribution companies within the U.S. do not manage or monitor the health of their distribution transformers sized 100 kva and below. Utility companies have relied upon fusing Manuscript received February 02, 2013; revised August 02, 2013, November 15, 2013; accepted January 19, Date of publication August 26, 2014; date of current version October 17, Paper no. TSG K. D. McBee is with Tesla Power Engineers, LLC, Golden, CO USA, and also with Xcel Energy, Denver, CO USA ( kerry.d.mcbee@teslapowerengineers.com). M. G. Simões is with the Department of Engineering, Colorado School of Mines, Golden, CO USA ( msimoes@mines.edu). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /TSG or internal circuit breakers to protect distribution transformers from excessive loading. Historically, the protection rating for these devices ranged between 125% and 150% of the transformer rating. However, research determined that the majority of transformer failures were due to lightning strikes, and not excessive loading, which ultimately led to an increased protection range of 200% 300% [7] [9]. This new protection philosophy, which focuses more on protecting the distribution system from a transformer failure rather than protecting the transformer itself, consequently leaves many transformers exposed to excessive loading and high losses [10]. In some cases, utility companies continually assess the expended insulation life of substation transformers based upon load data and/or measured winding temperatures. The amount of expended insulation life is referred to as the loss of insulation life and more commonly referred to as merely transformer loss of life. Unfortunately, residential distribution transformers have essentially been ignored due to their massive number, inexpensive cost, and lack of remote communication capabilities. Distribution companies that do employ distribution transformer management programs typically rely upon overload limits, historical load data, and load profile estimates to evaluate the loading of the units [11] [13]. Unfortunately, the loss of life calculations utilized for these evaluations are based upon an exponential decay rate that is sensitive to inaccurate input values [14]. Loss of life algorithms are directed at identifying a transformer s hotspot temperature [14], [15], which is utilized to access the level of polymerization of the device s insulation. Although utilizing these algorithms will not predict the date in which a transformer may fail, they do provide insight into how the transformer is aging based upon loading and environmental conditions. In the U.S., the most commonly utilized algorithms to estimate this hotspot temperature are described in IEEE Std C57.91 and IEEE Std C [14], [15]. IEEE Std C57.91 provides transformer thermal models for both steadystate (Clause 7) and transient load conditions (Annex G) for liquid immersed transformers. IEEE Std C focuses on identifying the hotspot temperature under harmonic load conditions for a two-winding transformer, which is the typical construction utilized in the U.S. to serve residential customers. In other parts of the world, IEC 354 is utilized to evaluate transformer life [16]. Transformer manufacturers are currently producing residential transformers that can interface directly with a smart grid communication system. Unfortunately, the majority of transformers in use today do not possess such communication capabilities. The lack of monitoring capabilities on distribution transformers make smart meters an attractive alternative for IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.

2 2968 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 6, NOVEMBER 2014 monitoring transformer demand, which can be utilized to evaluate insulation health. Smart meter functionality has increased significantlyinthe last decade, evolving from devices that only record peak energy demand to ones that can record kva, power factor, total harmonic distortion, total demand distortion, and the current and voltage harmonic spectrum [5]. For low voltage systems in the U.S., which typically consist of short lengths of cables/conductors to serve customer, smart meters can provide utilities with insight into which devices are being overloaded. In Europe, utilizing smart meters to evaluate transformer insulation polymerization may be more complex due to the extended lengths of low voltage conductor/cable lengths. In fact, the concern for overloading conductors/cables is equally as important to European utilities [17] [19]. Due to budgetary planning constraints and the limited features associated with currently installed smart meters, utility companies require guidelines to assist in developing a distribution transformer health assessment program that relies upon smart meters. These same guidelines can be utilized by smart meter manufacturers to improve device functionality to meet utility needs. Questions addressed in such guidelines should include: Which metering functionalities are required to accurately calculate the temperature of the winding hotspot? How important is it to measure the power factor or phase angle of each customer s demand so that load diversity is accounted for? What is the error associated with utilizing smart meters without harmonic measuring capacities? How correct should ambient temperature readings be to produce accurate results? Should ambient temperature readings be acquired from smart meters to produce as little error as possible? How does transformer loading affect the answers to any of these questions? The research presented in this paper answers these questions through the evaluation of six low-voltage models based upon actual distribution transformer load profiles, recorded temperature data from the National Oceanic and Atmospheric Administration (NOAA), and aging indices developed specifically for the purpose of assessing transformer aging. The results of the research establish general guidelines to assist in assessing the aging of distribution transformers through the utilization of smart meters. The guidelines presented include: aging indices to assess transformer loss of life based upon specific loading conditions; metering functions required to assess the life of transformers based upon: aging index; desired error percentage; ambient temperature variations; and harmonic distortion. II. LOSS OF LIFE APPLICATIONS Utilizing smart meter data to evaluate the expended insulation life of distribution transformers gives a utility company the ability to identify units that are experiencing excessive aging. Unlike the loss of life evaluations when applied to substation transformers, where the aging information is utilized to plan for maintenance and/or replacement expenditures, the goal is not to Fig. 1. Diagram illustrating the flow of information from smart meters and temperature meters to the loss of life algorithms. identify the end of life, but to identify those that are improperly sized due to new customers, harmonic distortion, or new increases in demand. Monitoring the expended life of distribution transformers provides utility companies the ability to: identify acute transformer loading for new loads, such as a cluster of electric vehicle (EV) chargers, which can demand up to 6 kw during typical peaking periods [20] [22]; identify transformers whose expected life is significantly reduced due to regular loading and ambient temperatures above their nameplate rating; identify transformers that require upsizing due to excessive harmonics produced by nonlinear loads; determine the most energy efficient transformer size based upon actual load profiles, transformer characteristics, and carbon emission penalties. Data required for the loss of life calculations is retrieved from residential smart meters and relayed through the smart grid communication system to software or a data historian that applies the loss of life calculations to each distribution transformer on the smart grid. The diagram in Fig. 1 illustrates the flow of information between metering devices and the loss of life algorithms. Whereas load data is supplied by smart meters, there are a number of locations and devices that may supply ambient temperature readings. Each demand and temperature measurement is assigned to a specific distribution transformer on the system. Utility personnel are notified once the loss of life algorithms

3 MCBEE AND SIMÕES: GENERAL SMART METER GUIDELINES TO ACCURATELY ASSESS THE AGING OF DISTRIBUTION TRANSFORMERS 2969 identify a transformer that is experiencing insulation degradation greater than a predetermined threshold limit, which is application specific and is addressed in the following sections. It should be noted that the most accurate method of determining the expended insulation life of a transformer is by directly monitoring its winding temperature in several locations; however, this capability is not available on most transformers rated 100 kva and below in service today. The excessive loading indices for evaluating distribution transformer health are divided into three categories: acute expended life; long-term expended life; excessive loss of life due to harmonics. The ability to access the amount of transformer life expended provides a utility company with a means to forecast the remaining useful life of the transformer based upon insulation degradation. The life of a transformer is estimated to be hours according to [14]. The excessive loading indices are compared to threshold limits that are based upon the operational life of the transformer. For example, consider a transformer with only a single year of service that has expended 4 years ( h) of insulation life during this period. Based upon the single year usage of the transformer, the utility company can forecast that the transformer will have a useful insulation life of 5.14 years instead of the 20.5 years ( hrs). The threshold limits that are utilized to evaluate the excessive loading indices (acute, long-term, and harmonics) are based upon forecasted values that indicate the transformer s insulation will not last the expected hours. Each index is based upon the calculation of an insulation aging rate, which is a function of transformer hotspot temperature as definedby(1)[14].ieeestdc57.91and C provide means for calculating under steady-state conditions, transient conditions, and nonsinusoidal load demand conditions. Because these standards are considered the accepted practice in the industry, no effort has been made to augment the defined approaches. It is the responsibility of the entity implementing the monitoring program to determine which methods are appropriate for evaluating the health of their distribution transformers based upon customer load profile information, environmental conditions, harmonic distortion, and smart meter functionality limitations. A. Acute Expended Life The acute expended life index (AELI) measures the expended insulation life of a transformer for a short period of time, such as a week or a month. When compared to an acute loading threshold limit, the AELI value can identify transformers that are experiencing excessive loading that will significantly reduce the life of the transformer if continued. This function is aimed at identifying transformers that serve new loads, such as EV chargers. The total amount of insulation aging, or AELI value, for the evaluation period is performed by summing the incremental expended life values and dividing them by the total time within the evaluation period as illustrated in (2). The total expended life for the evaluation period is compared to an acute loading threshold limit as defined in (3). The authors define as the per unitized amount of transformer life, which is normalized to the duration of the monitoring period, that a utility company is willing to allow before investigating transformer upsizing or adding an additional transformer to share the load. For instance, a weekly equal to 1.2 means that a utility company is willing to accept hrs ( ) of aging for the period. Essentially, is the threshold monitoring limit for notification and is valid for utilization with long-term excessive loading as well: where: AELI hotspot conductor temperature of transformer; acute loading threshold limit; aging acceleration factor for monitoring period ; acute expended life index; number of time intervals in evaluation period; time interval of monitoring increment. Accuracy limitations for AELI are based upon short-term calculation errors summed over the evaluation period, which can be a day, week, month, or several months. Accuracy is measured by how many transformers would be mistakenly upgraded or ignored as a result of loss of life calculation errors caused by inaccurate transformer demand (diversity and harmonics) and/or ambient temperature measurements. Because acute loading is associated with significant step increases in demand that impact loss of life, calculations performed for this analysis must possess little error associated with extreme ambient temperatures and/or load demands above 150% of the nameplate rating. B. Long-Term Excessive Loading Long-term excessive loading index (LELI) is directed at identifying transformers that experience excessive loading throughout the year, which includes peaking and non-peaking seasons. Violation of the long-term loading threshold limit indicates the transformer will not meet its expected life, which is typically estimated to be hr [14]. The algorithm for this function is the same as those described in (1) (3), with the only difference being a minimum analysis period of a year to account for on and off peak periods. The LELI and its threshold limit are illustrated in (4) and (5). (1) (2) (3) (4) (5)

4 2970 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 6, NOVEMBER 2014 where: LELI long-term loading threshold limit; long-term expended life index. Accuracy limitations for LELI are based upon long-term calculation error. As with AELI, the calculation results must be accurate over a given period of time, with the exception that for this application the monitoring period is a minimum of a year and may extend to multiple years. The accuracy is measured by how many distribution transformers are mistakenly upgraded or ignored due to calculation error over this extended period of time. C. Loss of Life due to Harmonics The transformer loss of life due to harmonics index (TLHI) is directed at accessing the transformer insulation life that has been expended due to harmonics. Violation of the harmonic loading threshold limit for this index may require the installation of a larger, but derated, transformer to operate for the entire expected life. The typical equation utilized for derating a transformer utilizes the rated or peak current drawn from the transformer in its determination and does not account for periods when current demand is well below this value [23]. Utilizing peak and rated values are sufficient when considering transformers with high load factors (0.9 and above), such as those utilized in industrial facilities. However, the increased implementation of nonlinear devices within residential homes, which have load factors between 0.4 and 0.8, has made distribution transformers susceptible to excessive loading due to harmonics. The approach for estimating loss of life due to harmonics is based upon the principles that a differential methodology can be applied using IEEE Std C57.91 and IEEE Std C by assuming these standards set arbitrary conditions for a realistic model behavior accepted for calculating the loss of life for two different conditions, i.e., the input data with i) harmonics and ii) without harmonics. Considering that loss of life is an endogenous variable, i.e., calculated with the IEEE Std C57.91 and IEEE Std C taken as a model, and outputs are determined accepted functional relationships in such model, it is plausible to assume that the differential determination of the loss of life for case i) from case ii) is the best likelihood for the loss of life considering only the effect of harmonics. To measure how transformer life is expended due to harmonics, the authors have augmented (1), which accounts for harmonics in calculating the transformer hotspot conductor temperature. Harmonic current increases eddy current losses and the harmonic loss factor for other stray losses, which manifest as heat and increase the internal temperature of the transformer. Ignoring harmonics in calculating will result in a calculated loss of life that is lower than actual due to the ignored heat generated at harmonic frequencies. To determine how much life is lost due to harmonics, the authors calculate hotspot conductor temperature with harmonics and without harmonics, and apply each temperature separately to (1). As a result, the difference between the values calculated by each equation is the amount of aging that is caused by the harmonic content of the load. This comparison is illustrated in (6), while the index and threshold limit are illustrated as follows: where: TLHI hotspot conductor of transformer without harmonic contribution; harmonic loading threshold limit; aging acceleration factor due to harmonics; transformer loss of life due to harmonics index. It is obvious that smart meters that provide this function must have the capability of measuring the harmonic spectrum of customer demand. Because these calculations rely upon determining the combined magnitude of harmonic currents, error can be introduced into the results if harmonic phase angles are not acquired by the smart meters to account for diversity. Research has proven that harmonic diversity increases as more nonlinear devices are connected [24], [25]. Therefore, depending upon the number of homes connected to a distribution transformer and the number and type of devices connected to each home, the error associated with calculating the loss of life due to harmonics can be significant. Currently it is unknown how the loss of life calculation error is related to the lack of harmonic diversity information. The authors will examine the effects of ignoringharmonicattenuationinsectioniv. III. ACCURACY OF STEADY-STATE CALCULATIONS The measuring capabilities of smart meters in the last decade have increased significantly. The initial smart meters were essentially automated revenue meters with functionalities limited to retrieving the magnitude of kwh. Currently, smart meters are being produced with the capability of measuring power factor, the harmonic spectrum including phase angle, voltage and current total harmonic distortion ( and ), and total demand distortion (TDD) [5]. Since most smart meters in use today have been installed over the last decade, many of the devices lack the capabilities of measuring fundamental phase angle (directly or indirectly with power factor and VArs), magnitude of harmonic currents, and phase angles of harmonic content, all of which are required to accurately calculate the loss of life of distribution transformers utilizing customer load information. With so many varying functionality options available on new and legacy smart meters, it is imperative to understand the limitations associated with utilizing data retrieved from each of these types of devices to calculate the loss of life of distribution transformers. The research presented in this paper attempts to identify these limitations. (6) (7) (8)

5 MCBEE AND SIMÕES: GENERAL SMART METER GUIDELINES TO ACCURATELY ASSESS THE AGING OF DISTRIBUTION TRANSFORMERS 2971 Ambient temperature is another attribute that is required to accurately calculate the loss of life of a distribution transformer. Although the ability to measure ambient temperature is usually not an available option on smart meters, it is an attribute that is required to perform the loss of life calculations. Ambient temperature readings from substations or local weather agencies are examples of non-smart meter options that can provide information to a distribution transformer health assessment program. However, these local temperatures may not match the actual ambient temperature at each transformer, which can thereby introduce errors into the calculation results. Adding temperature monitoring capabilities to a smart meter should be a simple implementation considering the existing complex functions of such devices. To utilize such readings from smart meters, which could be located in sun protected locations unlike the transformers, would require a probabilistic approach so that outlying temperatures could be discarded. However, before such probabilistic research efforts are initiated, one must answer the question of How accurate do ambient temperatures need to be to provide loss of life calculation results that accurately identify transformers that are losing life faster than expected? The answer to these questions will dictate whether ambient temperature readings are acquired from local weather agencies, local substations, or from smart meters. The remainder of this section evaluates the error associated with utilizing inaccurate ambient temperature information and inaccurate harmonic demand information. The loss of life equations utilized throughout this paper are based upon the steady-state thermal models identified in IEEE Std C57.91 and IEEE Std C [14], [15]. Although the transient model described in IEEE Std C57.91 Annex G provides a more accurate means of calculating the loss of insulation life of power transformers when considering their thermal dynamic characteristics, cooling system, windings, and oil viscosity, the steady-state method is more applicable to evaluating distribution transformers that serve loads with a harmonic content. The model described in Annex G does not accurately account for the heating effects caused by harmonics. As described in IEEE Std C57.110, which is the IEEE recommended practices for loading transformers under harmonic load conditions, harmonic currents affect the losses, eddy-current losses, and stray losses within the transformer. Although the IEEE Std C57.91 transient model indirectly accounts for harmonics when considering winding losses, it completely ignores the frequency dependent relationship between eddy-current losses and harmonic demand current [15]. Also, studies performed by manufacturers of two-winding transformers have shown that the harmonic effects on stray losses increase by an exponential factor of 0.8, which is also ignored in Annex G. The lack of consideration for harmonics in Annex G is not surprising since it is based upon research performed prior to 1992, when harmonic distortion on distribution systems was at a minimum [26]. With utility companies already witnessing an increase in residential customer harmonic demand, it is imperative to utilize a method of evaluating insulation health that accounts for the harmonic contribution [27] [29]. Although outside the scope of this document, Annex G should be updated to accurately reflect the aging effects created by harmonics. Another deciding factor in utilizing the steady models in lieu of the transient model is related to the ultimate goal of a distribution transformer management program, which is to identify transformers that are experiencing loading that will result in a significantly reduced operational life. As stated in both IEEE Std C57.91 and C57.110, the goal is not to predict the actual end of life date, but to identify the risk associated with overload or nonsinusodal loading conditions. Both standards, which are accepted in the industry as means to identify adverse transformer loading conditions, identify the steady-state model as a means to estimate insulation aging. The authors agree that for loss of life evaluations that affect large monetary expenditures, such as those associated with substation power transformers, utilities should utilize the transient method described in Annex G. A. Accounting for Harmonic Contents Smart meters typically measure the aggregate or peak load of a customer for a given period of time at the fundamental frequency. Because many of the smart meters in use today do not have harmonic measuring capabilities, it is imperative to understand the error that is introduced into the loss of life calculations by neglecting harmonic content. The total amount of current drawn by the transformer is determined by aggregating the current demanded by each smart meter at all frequencies. The relationship between the smart meter current demand and the current demanded by the distribution transformer that serves them is illustrated as follows: where: total transformer current; number of smart meters; number of harmonic frequencies represented; current magnitude of smart meter frequency ; phase angle associated with. at harmonic To determine the importance of acquiring harmonic current readings, the authors built a model and subjected it to over 2500 Monte Carlo simulations. The model consisted of two 50-kVA liquid immersed distribution transformers, each serving 20 homes. The authors utilized 58 different nonlinear devices, which included multiple types of compact fluorescent lighting, fluorescent tube lights, microwaves, high-efficiency refrigerators, and personal computers. To account for the multiple harmonic spectrum of these nonlinear devices, that are ratings and manufacturer specific, the authors utilized the harmonic spectrums found in [23], [30] [45]. Utilizing MATLab, each home was randomly assigned a number of linear devices and nonlinear devices. Of the 40 homes that were randomly generated, none of them consisted of the same load configuration. The MATLab function also randomly assigned a demand factor between 10% and 80% to each device. A percentage of the lighting loads were tied together and considered a single load for each simulation so as to represent peaking periods. (9)

6 2972 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 6, NOVEMBER 2014 TABLE I ATTRIBUTES OF THE 50 kva OVERHEAD LIQUID IMMERSED DISTRIBUTION TRANSFORMER UTILIZED THROUGHOUT THE PAPER Fig. 3. Percent error of loss of life calculation results as a function of while ignoring harmonic content for Transformer 2. Fig. 2. Percent error of the loss of life calculation results as a function of while ignoring harmonic content for Transformer 1. Simulations performed in this section consisted of randomly turning off and on devices in each home based upon their demand factor so as to analyze the most possible load combinations for the two transformer system. Utilizing the attributes of the 50-kVA transformer listed in Table I, the loss of life of each transformer was calculated for every simulation for two cases: i) one that accounted for the harmonic content of each load connected to each transformer; and ii) one that completely ignored harmonic content. The percent error was calculated as follows: where: % (10) error percentage associated with calculating loss of life with and without correct demand and ambient temperature information; loss of life calculation results utilizing accurate values; loss of life calculation results utilizing inaccurate values. The results indicate that loss of life calculation solutions are at least 10% accurate for demands with values below approximately 10%. Figs. 2 and 3 illustrate the percent error as a function of for Transformers 1 and 2. It was also shown that for this system, accuracy of the loss of life calculations was a function of distortion magnitude and not current magnitude. Fig. 4 illustrates the percent error as a function of current magnitude. Fig. 4. Percent error of the loss of life calculation results as a function of the fundamental current while ignoring harmonic content for Transformer 1. B. Accounting for Accurate Ambient Temperature The simplest method to retrieve ambient temperature readings is to acquire them from a local agency that continuously records such data. The disadvantage to this approach is that the recorded temperature may not reflect the actual temperature at the transformer location because of distance or geographical conditions. Due to their proximity to the distribution transformer serving them, smart meters with thermal metering capabilities can provide data representative of what is experienced by the transformer that serves them. Analysis of local temperature readings versus regional temperature readings was performed on the 50-kVA liquid immersed transformer that is listed in Table I. The analysis consisted of evaluating the differences between loss of life calculations utilizing correct ambient temperature and those derived with temperatures that varied C to simulate incorrect measurements. The authors considered local temperature readings those that are acquired within 100 ft of the transformer. Conversely, regional temperatures are considered those acquired from a central location and utilized for all distribution transformers. The current magnitude was varied from per unit for this analysis. The results indicate that at loads below 1.0 per unit, inaccurate ambient temperature has a significant effect on the accuracy of loss of life calculations. This occurs because at loads below 1.0 per unit the internal temperature of the transformer is driven

7 MCBEE AND SIMÕES: GENERAL SMART METER GUIDELINES TO ACCURATELY ASSESS THE AGING OF DISTRIBUTION TRANSFORMERS 2973 Fig. 5. Loss of life difference between actual and calculated aging rate in harmonic environment utilizing incorrect ambient temperature. Fig. 6. Error associated with calculating loss of life utilizing incorrect ambient temperature. more by ambient temperature than by the heat generated from core and winding losses. However, at loads below 0.9 per unit, the amount of life expended in a single hour is well below one hour if the ambient temperature is below the rated temperature. The expedited life for each temperature variation is illustrated in Fig. 5. Overestimating the temperature has more of an impact than underestimating the temperature by the same amount. At 1.0 per unit, the percent error range for a Cdifference is %to %, which indicates that the actual loss of life for the period is 46% lower than calculated. Fig. 6 illustrates the percent error associated with each temperature variation. In summary, these results indicate that correct ambient temperature is more important for smart meters utilized to monitor long-term transformer health than it is for monitoring for acute excessive loading, where demand is the dominant heat generation source. IV. ACCURACY ASSOCIATED WITH THE SUMMATION OF INDIVIDUAL CALCULATIONS A. Analysis Description The analysis results in the previous section indicate that ignoring harmonics and utilizing inaccurate temperature information introduces errors into individual loss of life calculations. Because customer demand and ambient temperature fluctuate throughout the day, the error associated with summing individual loss of life calculations will also fluctuate. Therefore, it is important to understand how these calculation errors will affect the evaluations for AELI, LELI, and TLHI, which are dependent upon summing the results of individual loss of life calculations. A system that calculates the loss of life hourly would result in 720 calculations per month, 8760 calculations for a year, and calculations for a 10-year period, all of which are subjected to errors based upon monitoring accuracy. The acceptable amount of error is dependent upon which excessive aging index is utilized for the evaluation. The authors calculated the loss of life of three 50-kVA transformers installed in Boulder, Colorado, to determine the required smart meter functions to evaluate each loss of life index accurately. Each transformer was installed on a smart grid with transformer load monitoring capabilities. Ambient temperature information in 15-minute increments was retrieved from NOAA, which is also located in Boulder. The expended life of each transformer was calculated for a month utilizing accurate ambient temperature and demand information, which was considered the benchmark condition for the analysis. The benchmark was then compared to calculations that utilized varying ambient temperatures, fundamental demand, and harmonic content, all of which represented different smart meter monitoring capabilities. The authors utilized demand information from the months of January and July, because they are the coldest and hottest months of the year for this region. Analyzing the two most extreme environmental and load conditions provides a means to extrapolate annual and long-term error for any geographical region. The authors believed that utilizing an entire years worth of data would only be representative of the same analysis performed within Colorado. Analyzing monthly data allows for the analysis results to be compared to similar environmental conditions on a monthly basis in other regions. For example, determining the annual error in Florida may consist of applying the Colorado July results for several months of the year. Smart meter monitoring functionalities are represented by the accuracy of the input information utilized in the loss of life calculations. To represent smart meters that can only measure demand at the fundamental frequency, harmonic demand is ignored in the calculations. To represent smart meters that cannot measure phase angles directly or indirectly with power factor or VArs, which allows for the accountability of diversity, the authors multiplied the measured values by a diversity factor of 1.2, which resulted in a larger transformer demand. Depending upon the number of customers connected to a transformer, the diversity factor can range between 1 2 [46], [47]. The ambient temperature was varied by C, C, Cto determine the importance of acquiring accurate ambient temperature measurements. The distribution transformers utilized in the analysis were selected because of their varying load factors and peak demand, which were near or above their 50-kVA ratings. The load factors of the transformers varied between 44.8% and 73.5% depending upon the season. These load factor variations allow for the analysis results to be applicable for most typical demand profiles. A single day load profile for each transformer is illustrated in Figs To analyze loading conditions above 150% loading, the authors added 20 kva to the summer loading of Transformers B and to the winter loading of Transformer C to form simulation examples Transformer D and E. The resulting daily load profile of Transformer D averaged 49.5 kva and had

8 2974 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 6, NOVEMBER 2014 Fig. 7. Daily load profiles and ambient temperature for Transformer A. The typical July profile with load factor of 54.4% is illustrated in (a), while the typical January profile with a load factor of 50.3% is illustrated in (b). Fig. 9. Daily load profiles and ambient temperature for Transformer C. The typical July profile with load factor of 68.7% is illustrated in (a), while the typical January profile with a load factor of 73.5% is illustrated in (b). Fig. 10. Ambient temperature and typical daily load profile in July of Transformer D, which results in a load factor of 57.6%. Fig. 11. Ambient temperature and typical daily load profile in January of Transformer E, which results in a load factor of 80.3%. TABLE II SUMMARY OF TRANSFORMER LOAD PROFILES Fig. 8. Daily load profiles and ambient temperature for Transformer B. The typical July profile with load factor of 44.8% is illustrated in (a), while the typical January profile with a load factor of 55.6% is illustrated in (b). a peak demand of 85.8 kva, which resulted in a load factor of 57.6%. The resulting daily load profile of Transformer E averaged 61 kva and had a peak demand of 76.5 kva, which resulted in a load factor of 80.4%. The profiles of Transformer D and E are illustrated in Figs. 10 and 11. Loading profile information for each transformer is listed in Table II. The harmonic content of the energy demanded of each transformer was less than 8%; however, it was increased to 20.3% to reflect extreme harmonic conditions. The increased is above the 10% identified as being the threshold limit where harmonics should be considered to limit calculation error to 10%. This value of was selected because it represents a maximum TDD of 20.3%, which will fluctuate throughout the day as the fundamental current demand fluctuates. IEEE Std limits the TDD of residential customers to 20% [48]. Therefore,

9 MCBEE AND SIMÕES: GENERAL SMART METER GUIDELINES TO ACCURATELY ASSESS THE AGING OF DISTRIBUTION TRANSFORMERS 2975 TABLE III HARMONIC SPECTRUM FOR ANALYSIS Fig. 13. Comparing the loss of life calculation results with correct ambient temperatures to those that utilize ambient temperatures that vary C during the 6-hour period where loss of life is greater than 1 hour per calculation for Transformer D. Fig. 14. Comparing the loss of life calculation results including harmonics to those that ignore harmonics during the 6-hour period where loss of life is greater than 1 hour per calculation for Transformer D. Fig. 12. Fifteen-minute loss of life calculation results throughout the day for Transformer D. utilizing a of 20% in the analysis will allow for the evaluation of loss of life calculations during the most extreme distortion conditions based upon accepted practices. The harmonic spectrum utilized for the analysis is listed in Table III. B. Observation of the Daily Expended Life The results of analyzing Transformer D, with loading at 172% ofrateddemandduringjuly,indicated that calculation errors are only significant when the combination of load and ambient temperature result in transformer aging that is greater than 1.0 per unit. Fig. 12 illustrates Transformer D aging in 15-minute increments on a semi-log scale, which illustrates the nonlinear characteristics of the loss of life calculations. The -axis of the graph measures the exhausted insulation life for each 15-minute increment. The daily loss of life for this extreme condition is continuously above 1 hour for the 15-minute intervals between the hours of 15:00 and 23:00. Ambient temperature only becomes significant during periods of loading above 150%, where the error in hours ranges between 200 and 300. Fig. 13 illustrates the loss of life calculation results for ambient temperature inaccuracies ranging between C for Transformer D. As with Fig. 12, the -axis measures the exhausted transformer life of each 15-minute increment. Ignoring harmonics during these extreme environmental and loading conditions results in errors between 400 and 650 hours for a 15-minute calculation period. Fig. 14, which measures the life exhausted for each 15-minute increment, illustrates the difference between accounting for harmonics and ignoring harmonics when the transformer is subjected to 172% loading while ambient temperatures are above the transformer s rated temperature. The largest error observed during this excessive load conditions occurred when phase angles were not measured so as to account for the diversity of loads between residential customers. The error observed ranged between 6400 and hours depending on whether harmonics are accounted for. Fig. 15, which measures the expended life for each 15-minute increment, illustrates the difference between accounting for and ignoring load diversity for the 6-hour peaking period. C. Requirements for Acute Expended Life TheexpendedlifeofTransformersA,B,C,D,andEwerecalculated for an entire month. The results indicated that the load and environmental conditions for Transformers D and E would result in complete transformer expended life after two summer

10 2976 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 6, NOVEMBER 2014 TABLE IV SUMMARY OF EVALUATION OF EXPENDED TRANSFORMER LIFE IN JULY Fig. 15. Comparing the loss of life calculation results including harmonics and diversity to those that ignore diversity during the 6-hour period where loss of life is greater than 1 hour per calculation for Transformer D. months (Transformer D) or seven winter months (Transformer E). These two transformers are representations of acute excessive loading. For these months, ambient temperature had little effect upon the evaluation. The reason for this occurrence is that the internal temperature of the transformer is driven more by heat generated from the demand current than it is by the ambient temperature during extreme load conditions. Calculations utilizing ambient temperatures varying C only resulted in errors between %and %. This error would change the expected failure dates, which would still be less than 2 years. Therefore, these temperature variations would have little effect upon identifying transformers with acute excessive loading. Ignoring harmonics in the calculations had a significant impact on the ability to identify acute loading. For 172% loading, ignoring the harmonic content resulted in loss of insulation life that was 33% lower than actual, which is illustrated in Fig. 14. Depending upon the magnitude of the distortion and the magnitude of fundamental demand on the transformer, ignoring harmonics may only result in a small number of transformers being neglected due to excessive loading.

11 MCBEE AND SIMÕES: GENERAL SMART METER GUIDELINES TO ACCURATELY ASSESS THE AGING OF DISTRIBUTION TRANSFORMERS 2977 TABLE V SUMMARY OF EVALUATION OF EXPENDED TRANSFORMER LIFE IN JANUARY The simulations that did not consider phase angle would have still identified transformers D and E as requiring upgrades; however, the error associated with these calculations was 7 to 10 times the actual value (calculated benchmark). As evident in Fig. 15, even if the harmonic magnitude is accounted for, the error in loss of life calculation is still 700%. This suggests that utilizing smart meters without phase angle measuring capabilities may identify transformers for replacement that are not experiencing excessive loading. D. Requirements for Long-Term Loading Evaluation Long-term Expended Life evaluation, which is considered greater than a year, accounts for on and off peak seasonal loading. Therefore, the results of the expended life calculations accounting for diversity and harmonics for Transformers A, B, and C, which represent adequately sized units, indicate that these transformers should have a life comparable to the expected hours. The calculated expended life for July ranged between 1038 and 1084 hours, whereas for January, it only ranged between 0.1 and 111 hour in the month. The results for the evaluation of each transformer profile are listed in Tables IV and V with their corresponding smart meter inaccuracies. As with the acute expended life evaluation, the attribute with the largest impact on accuracy was diversity. Ignoring diversity in the month of July resulted in an expended life 5.5 times greater than the correct value. However, unlike the results of the acute expended life loading evaluation, ambient temperature has a larger effect on the accuracy of the calculations in extreme temperature conditions. In the month of July, an ambient temperature error greater than 2 C results in errors %or % even if harmonics and diversity are accounted for. Ignoring harmonics in the calculations resulted in a 28% error forthemonthofjulyifdiversityand correct ambient temperatures are utilized.

12 2978 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 6, NOVEMBER 2014 In summary, it appears that to accurately perform long-term expended life evaluations with smart meters, the devices must have the capability of measuring phase angle to account for diversity. In regards to ambient temperature, devices that guarantee C accuracy are required. V. CONCLUSION Smart meters are becoming more prominent on electric distribution systems. The research presented in this paper reveals that it is possible to utilize smart meters to accurately identify excessive loading on liquid immersed distribution transformers and/or to forecast the remaining useful life of their internal insulation. The application of threshold limits and excessive loading indices can be applied to smart meter data to evaluate the health of distribution transformers based upon the expended insulation life. Smart meter accuracy requirements for such a program are dependent upon the level of harmonic distortion on the system, the loading on the transformer in regards to its rating, and the desired application of the results. For systems where the current demanded possesses a below 10%, utilizing smart meters without harmonic measuring capabilities will result in a calculation error no greater than 10%. If the desire is to perform acute expended life evaluations on transformers with at least 170% loading, correct ambient temperature becomes less of a factor and can be inaccurate as much as 4 C. However, to perform long-term expended life evaluation, the measured ambient temperature should be within 2 C to keep the error associated with a single loss of life calculation error within % and %. Depending upon temperature variations within a specific region, a utility company may have to utilize smart meters with ambient temperature metering capabilities to minimize calculation errors. 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13 MCBEE AND SIMÕES: GENERAL SMART METER GUIDELINES TO ACCURATELY ASSESS THE AGING OF DISTRIBUTION TRANSFORMERS 2979 [33] I.Chatzakis,G.A.Vokas,andF.V.Topalis, Theinfluence of replacement of incandescent lamps with compact fluorescents to the harmonic distortion in non-interconnected island grids, in Proc. 7th WSEAS Int. Conf. Electr. Power Syst., High Voltages, Electr. Mach., Nov [34] A. B. Baggini, Handbook on Power Quality. Chichester, U.K.: Wiley, p [35] Arrillaga and N. R. Watson, Power System Harmonics. Chichester, U.K.: Wiley, p. 82. [36] D. Chapman, Cooper Development Association, Power Quality Application Guide. Hemel Hemsptead, U.K., Mar [Online]. Available: [37] G. Masters, Renewable and Efficient Power Systems. Hoboken, NJ, USA: Wiley, p. 91. [38] J. S. Navamany and M. T. Au, The impact of small and medium power loads on distribution network efficiency and harmonic propagation, in 19th Int. Conf. Electr. Distrib., May [39] A. Jakoef, Impact assessment of energy-efficient lighting interventions, M.Sc. thesis, Stellenbosch Univ., Stellenbosch, South Africa, Dec [40] J. R. Macedo, A. G. Martins, M. J. V. Siqueira, and J. R. V. Carneiro, The impact of FIFA world cup 2006 on power quality in the electric distribution system, in Proc. 9th Int. Conf. Electr. Power Qual. Utilization, Barcelona, Spain, Oct [41] H. Farooq, C. Zhou, M. Allan, M. Farrag, R. A. Khan, and M. Junaid, Investigating the power quality of an electrical distribution system stressed by non linear domestic appliances, in Proc. Int. Conf. Renewable Energies Power Qual., Apr [42] S. Bhattacharya, T. Frank, D. Divan, and B. Banerjee, Parrell active filter system implementation and design issues for utility interface of adjustable speed drive systems, in Proc. Industry Applicat. Conf., 1996, vol. 2, pp [43] J. Yong, L. Chen, A. Nassif, and W. Xu, A frequency domain harmonic model for compact fluorescent lamps, IEEE Trans. Power Del., vol. 25, no. 2, pp , Apr [44] J. Yong, L. Chen, A. Nassif, and W. Xu, Modeling of home appliance for power distribution system harmonic analysis, IEEE Trans. Power Del., vol. 25, no. 4, pp , Oct [45] A. Nassif, Modeling, measurement and mitigation of power system harmonics, Doctoral dissertation, Univ. of Alberta, Alberta, BC, Canada, [46] V. P. Chatlani, D. J. Tylavsky, D. C. Montgomery, and M. Dyer, Statistical properties of diversity factors for probabilistic loading of distribution transformers, in Proc. 39th North Amer. Power Symp., 2007, pp [47] W. H. Kersting, Distribution System Modeling and Analysis. Boca Raton, FL, USA: CRC, [48] IEEE, New York, NY, USA, Recommended practices and requirements for harmonic control in electric power systems, IEEE 519, Kerry D. McBee (M 14) received the B.S. degree from Colorado School of Mines, Golden, CO, USA, in 1999, the M.S. degree in electric power engineering from Rensselaer Polytechnic Institute, Troy, NY, USA, in 2000, and the Ph.D. degree from Colorado School of Mines in During his career, he has focused on smart grid implementation, power quality, forensic engineering, system planning, and distribution design for companies such as NEI Power Engineers, Peak Power, Knott Laboratory, and Tesla Power Engineers, LLC. He is currently the Lead Forensic Engineer for Xcel Energy, Denver, CO. Marcelo G. Simões (SM 98) received the B.S. and M.S. degrees from the University of São Paulo, São Paulo, Brazil, in 1985 and 1990, the Ph.D. degree from the University of Tennessee, Knoxville, TN, USA, in 1995, and the D.Sc. degree (Livre-Docência) from the University of São Paulo in He is an Associate Professor with Colorado School of Mines, Boulder, CO, USA, where he has been establishing research and education activities in the development of intelligent control for high-power-electronics applications in renewable- and distributed energy systems. He recently released the book Power Electronics for Renewable and Distributed Energy Systems: A Sourcebook of Topologies, Control and Integration (Springer-Verlag, 2013). Dr. Simões is currently the Chair for the IEEE IES Smartgrid Committee. He has been involved in activities related to the control and management of smart grid applications since 2002 with his NSF CAREER award Intelligent Based Performance Enhancement Control of Micropower Energy Systems.