Managing Aged Transformers
Utility develops repair/refurbish/replace strategy using
innovative risk-based methodologies.
By Chris Kurtz, Kansas City Power & Light; Gary Ford and Mark Vainberg, PowerNex
Associates Inc.; Mike Lebow, Coplanar Consulting Inc.; and Barry Ward, EPRI
U
tilities are increasingly operating transformers up to and beyond their expected life spans. Asset managers are diagnosing and monitoring the condition of critical and problematic units, and then ranking the transformers by condition to deal in the near-term with the units that affect utility operations the most. However, from regulatory and business planning perspectives, a longer-term view on operating and capital investment is necessary. Developing a repair/refurbish/replace management strategy for signifi cantly aged populations with a rational basis for the strategy is a critical need.This type of strategy requires that asset managers relate the effect of available options on projected failure/replacement rates and their associated costs and impacts. In response to this need, utilities have adopted the assumption that failure/ replacement rates experienced in the past will continue in the future. If utilities had a constant distribution of transformer ages, and if the age distribution was in the fl at portion of the “bathtub curve,” this would be a valid approach.
In practice, however, many utilities have demographic distri-butions displaying a bulge of units in the 40, 50 and older age categories, which are at the back end of the bathtub curve where
Fig. 1. Typical demographic data sorted by condition.
failures increase rapidly over time. As a result, new asset-man-agement approaches are needed for the effective manasset-man-agement of the “boomer” generation of aging transformers.
A new EPRI project surveyed utility practices and needs and identifi ed important asset-management case studies. The project formulated a new risk-based methodology that could be used for solutions in these types of business case studies.
Reviewing Existing Practices and Emerging Needs
Sample utility concerns and needs were addressed through a focused survey of leading utility managers. Of the surveyed managers, more than two-thirds were concerned with the adverse demographics of their transformer fl eets. In view of these concerns, a signifi cant portion of utilities are beginning to proactively replace transformers. Of the surveyed managers, two-thirds are increasingly using on-line diagnostic monitor-ing to assess transformer condition. The survey confi rmed that most utilities use historic failure rate data to project future fail-ure rates. As a result, utility managers are unanimous in their assessment that the development of improved methodologies for managing such transformer fl eets is necessary. While utility managers agree that improved methodologies are needed, the kinds of decisions and business case analyses vary depending on each utility’s specifi c needs, which can include:
●Spares/replacement projection, which takes into account
the specifi c demographics and condition of the population. This will enable utilities to evaluate options for more-proactive versus less-proactive replacement programs, to allow more accurate planning of capital investment needs and to secure better contractual arrangements with suppliers.
● Reassessment or evaluation of loading criteria versus
life expectancy.
●Effi cacy of and payback on insulating fl uid system
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TRANSMISSION & DISTRIBUTION WORLD/www.tdworld.com/July 2005date of manufacture and rating), operating data such as load-ing records, and maintenance history and diagnostic test re-sults. Typical data of this type are illustrated in Fig. 1, which is based on demographic data for a population of 50-MVA transformers in which condition data have been assessed and sorted by condition.
One of the critical needs in the development of improved methodologies is valid failure data. However, the availability and quality of failure data are highly variable. Unfortunately, postmortem investigation of transformer failures is frequently not performed because of the cost and resources involved. Nevertheless, we were able to obtain failure data from EPRI’s host utility, Kansas City Power & Light (KCP&L; Kansas City, Missouri, U.S.), that could be sorted by transformer type and location and that was adequate for generic failure modes
specifi c to the transformer populations being considered. A sample of failure data for one specifi c type of transformer is shown in Fig. 2.
Apart from a startlingly high infant-mortality peak in the fi rst year, the distribution appears to peak in the range of 11 to 14 years of service. This result is probably consistent with diffi culty in providing short-circuit withstand capability and/or heating effects caused by unequal loading of the dual second-ary windings, rather than a typical mode of thermal aging failure caused by insulation deterioration with age and load. An important observation from the perspective of the usefulness of these data for projecting future failure rates is the apparent consistency in patterns for each of the year groupings.
Fig. 4. Industry failure rate data from the United Kingdom. units can be rebuilt at independent shops or by transformer manufacturers. However, the overall benefi t of rebuilding such units is in question, as shown in Fig. 3. These results indicate that, although some differences in unit life are achieved by the various rebuild facilities, overall life expectancies for rebuilt units are signifi cantly less than (by about half) that of new units.
Sources of industry failure data were also investigated. Two consistent sets of industry failure data were obtained. Figure 4 shows data for a large population of U.K.-area supply transformers that agrees closely with another credible source of similar information.
For the purpose of failure rate projection, the requisite curve is the hazard rate, which can be evaluated from the density and survival functions by calculating their ratio and which provides the joint probability that a unit will survive up to an
age in question and then fail in exactly that year. This particular hazard rate function is relevant to transformer populations aging under thermal and chemical environments typical for 132-kV transformers in the United Kingdom. It should not be applied to represent the life expectancy of any other specifi c transformer population because application variables—most signifi cantly, loading and insulation system conditions—have a signifi cant impact on the hazard rate. Nevertheless, these data are useful in the validation and calibration of the methodolo-gies developed to model transformer population aging.
Formulation of Prototype Methodologies
Several innovative methodologies have been analyzed and formulated for the types of scenarios and business case studies that typical utility asset managers need to consider. Integral to the development of such business case analyses is the ability to project the rate of transformer failure of the population at risk. In the basic case, this is calculated by convolving the hazard rate function with demographic data as illustrated in Fig. 5.
The convolution is the “sum of the products” of the number of transformer units in each age bin multiplied by the value of the hazard rate function for that specifi c age bin. The hazard rate function is fi xed for a given population. However, for each year or interval into the future, the demographic distribution moves to the right, causing more overlap and higher numbers of projected failures.
The appropriate hazard rate function can be derived from ac-tual failure data from the utility (if available and if the mode of failure is exceptional), from industry data for normal aging or
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TRANSMISSION & DISTRIBUTION WORLD/www.tdworld.com/July 2005from a model for aging based on standardized methods used in ANSI C57.91 or IEC standards. Such well-established models relate hot-spot temperature to life expectancy as a function of insulation condition. Based on such a model, the hazard rate function can be calculated for specifi c loading conditions and insulation system condition as illustrated in Fig. 6.
In this example, we have assumed some degradation of the insulation system and adjusted the loading distribution to approximate the industry-based hazard rate function. With such a model, the hazard rate function can be derived for other loading distributions to evaluate the effect of loading levels on population life expectancy and projected failure rates for a given demographic distribution. Alternatively, this model can be used to evaluate the value of maintaining insulation systems in good condition or to evaluate derating costs that would be implicit in allowing deterioration in insulation sys-tems. The impact of maintaining insulation system integrity can be signifi cant, as illustrated in Fig. 7.
An interesting scenario involves the potential recovery of life expectancy from a deteriorated state through the use of reconditioning processes. While application of such technolo-gies involves a signifi cant investment, estimation of the cost/ benefi t in terms of improved life expectancy and reduction in prospective failure rates is important. In this case, the length of time transformers are in a deteriorated state is a signifi cant factor. Therefore, if the deteriorated state is relatively short, recovery is relatively rapid, as shown in the fi rst graph in Fig. 8. On the other hand, if the transformer maintenance policy has allowed the population’s condition to decline and stay in a deteriorated state for several years, recovery of life expectancy is slow.
In some cases, an asset manager is faced with a generic or systemic problem with a segment of the company’s trans-former fl eet. The problem may have been a specifi c design or a manufacturing fl aw involving several units that was not discovered until all of the units were in service. Or, the prob-lem might be an operational issue involving the occurrence of excessive stresses that were not anticipated at the planning or specifi cation stage. In any case, the problem is manifest through a signifi cant number of transformer failures at service Fig. 5. Basic failure rate projection.
Fig. 6. Hazard rate function derived from ANSI Models [1].
Fig. 7. The effect of insulation condition on equipment mortality. lives far less than the normal service life. Without careful forensic analysis after failures, the problem may exist for sev-eral years before the trend is suspected and identifi ed. Often, failure statistics may obscure the trend somewhat, because failures of all types would normally be combined. Recognition of the subgroup of at-risk transformers and separation of the corresponding generic and normal failures is a critical step. Figure 9 illustrates this concept.
Generic problems of this type tend to be unique, which implies that industry or aggregated past failure data for the utility in question will probably not give a good representation of the hazard rate for the group of transformers affected by the generic failure mechanism. Therefore, it is important to separate the overall failure statistics of the utility or industry data from the actual failure data related to the generic mecha-nism of failures as illustrated in Fig. 9. The hazard rate for the remainder of the population that is aging normally can be represented by industry data or company data, if available (with the generic failure data removed).
Conclusions
man-Total Failures Age Case 1: Continuation of Status Quo
Case 2: Eliminate Generic Problem
Total Failures Years Ahead Years Ahead N um be rs o f U ni ts H az ar d R at e N um be r of F ai lu re s N um be r of F ai lu re s
Normal Aging Hazard Rate Based on Failure Data or Industry Data
Normal Failures
Normal Failures
Generic Failures
Generic Failures Generic Problem
Hazard Rate Based on Failure Data 5 10 15 8 2 0 0 20 23 30 40 40 1 2
Fig. 9. Analysis of generic transformer problems.
Fig. 8. Effect on hazard rate function over time following insulation system refurbishment.
agement decision making. Utilities are also becoming aware that satisfying increasing regulator and shareholder scrutiny requires the development of better tools to support transformer fl eet O&M and capital investment. Signifi cant advances in methodology are feasible and are being developed in this important EPRI-funded project. ◗
Christopher A. Kurtz is manager of substation construction and main-tenance at Kansas City Power & Light. Prior to his present position, Kurtz supervised the relay department for eight years. Kurtz received a BSEE degree from the University of Missouri-Rolla and an MBA degree from Rockhurst University in Kansas City. [email protected]
Gary L. Ford is a principal of PowerNex Associates Inc. Previously, Ford spent 32 years in system planning and research with Ontario Hydro, where he was active in investigating problems in electrical power systems and equipment. Ford received BS, MS and PhD degrees in electrical engineering from Queen’s University, the University of Toronto and Waterloo University, respectively. [email protected]
Mark Vainberg is a principal of PowerNex Associates Inc. and focuses on asset management, decision support and technology assessment. Vainberg spent 23 years with a major Canadian util-ity in a variety of technology assessment and development roles. Vainberg received an MSEE degree from the University of Toronto.
Mike Lebow is an independent engineering consultant providing project management, design and application engineering support. Lebow previously worked for the Consolidated Edison Company of New York, where he held various management positions in engineering and research and development. Lebow earned BSEE and MSEE degrees from the University of Pennsylvania. [email protected]
Barry H. Ward is a technical leader, Transmission & Substations, in the Science & Technology Development Division at the Electric Power Research Institute (EPRI) in Palo Alto, California, U.S. He joined EPRI in 1997. Ward was previously vice president of engineering for AVO International, where he was responsible for the develop-ment of test and measuredevelop-ment instrudevelop-mentation. Ward serves on the Transformers Committee of the IEEE Power Engineering Society. Ward earned a BSEE degree from the University of Bradford, England.
