Managing Aged Transformers

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 Utilities 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 significantly 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 flat portion of the bathtub curve, this would be a valid approach. In practice, however, many utilities have demographic distributions 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. Fig. 2. Sample of failure data. failures increase rapidly over time. As a result, new asset-management approaches are needed for the effective management of the boomer generation of aging transformers. A new EPRI project surveyed utility practices and needs and identified 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 fleets. In view of these concerns, a significant portion of utilities are beginning to proactively replace transformers. Of the surveyed managers, two-thirds are increasingly using on-line diagnostic monitoring to assess transformer condition. The survey confirmed that most utilities use historic failure rate data to project future failure rates. As a result, utility managers are unanimous in their assessment that the development of improved methodologies for managing such transformer fleets 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 specific needs, which can include: Spares/replacement projection, which takes into account the specific 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 reconditioning. Related to this topic is the evaluation of the tradeoff between maintenance of transformer condition versus loading capability needed to achieve a standard or extended life expectancy. Generic problems can occur from mistakes in the design, manufacture or application. Utilities must decide whether they are better off addressing these problems or doing nothing and replacing a transformer at failure. Analysis of such problems requires the development of suitable methodologies and the availability of suitable and quality data. Data availability and quality as a precursor to development of high-level transformer fleet management models was assessed through detailed pilot studies and a review of industry data. Typical utility data include inventory information such as transformer nameplate (manufacturer, 36 TRANSMISSION & DISTRIBUTION WORLD/www.tdworld.com/July 2005

Fig. 3. Experience with rebuilt transformers. date of manufacture and rating), operating data such as loading records, and maintenance history and diagnostic test results. 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 38 specific to the transformer populations being considered. A sample of failure data for one specific type of transformer is shown in Fig. 2. Apart from a startlingly high infant-mortality peak in the first year, the distribution appears to peak in the range of 11 to 14 years of service. This result is probably consistent with difficulty in providing short-circuit withstand capability and/or heating effects caused by unequal loading of the dual secondary 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. A common practice in many utilities is to rebuild transformers that have failed in a noncatastrophic mode. Such TRANSMISSION & DISTRIBUTION WORLD/www.tdworld.com/July 2005

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 specific transformer population because application variables most significantly, loading and insulation system conditions have a significant impact on the hazard rate. Nevertheless, these data are useful in the validation and calibration of the methodologies developed to model transformer population aging. Fig. 4. Industry failure rate data from the United Kingdom. units can be rebuilt at independent shops or by transformer manufacturers. However, the overall benefit 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 significantly 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 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 specific age bin. The hazard rate function is fixed 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 actual failure data from the utility (if available and if the mode of failure is exceptional), from industry data for normal aging or Superior Concrete Products is a respected design-build company that manufactures and installs decorative precast concrete fences, screening walls, and sound barriers. Superior has over 20 years experience, with satisfied customers throughout United States. Contact us today, so we can be thinking of you. 40 TRANSMISSION & DISTRIBUTION WORLD/www.tdworld.com/July 2005

Fig. 5. Basic failure rate projection. from 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 specific 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 systems. The impact of maintaining insulation system integrity can be significant, 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 technologies involves a significant investment, estimation of the cost/ benefit 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 significant factor. Therefore, if the deteriorated state is relatively short, recovery is relatively rapid, as shown in the first 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 transformer fleet. The problem may have been a specific design or a manufacturing flaw involving several units that was not discovered until all of the units were in service. Or, the problem might be an operational issue involving the occurrence of excessive stresses that were not anticipated at the planning or specification stage. In any case, the problem is manifest through a significant number of transformer failures at service 42 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 several years before the trend is suspected and identified. 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 mechanism 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 Failure analysis and the maintenance of reliable failure statistics provide valuable insight and support for asset man- TRANSMISSION & DISTRIBUTION WORLD/www.tdworld.com/July 2005

Numbers of Units Hazard Rate Fig. 8. Effect on hazard rate function over time following insulation system refurbishment. Normal Aging Hazard Rate Based on Failure Data or Industry Data Generic Problem Hazard Rate Based on Failure Data 30 20 5 40 23 10 40 1 2 0 2 15 8 0 Age Number of Failures Case 1: Continuation of Status Quo Total Failures Generic Failures Normal Failures Years Ahead Number of Failures Case 2: Eliminate Generic Problem Total Failures Normal Failures Generic Failures Years Ahead Fig. 9. Analysis of generic transformer problems. July 2005/www.tdworld.com/TRANSMISSION & DISTRIBUTION WORLD agement decision making. Utilities are also becoming aware that satisfying increasing regulator and shareholder scrutiny requires the development of better tools to support transformer fleet O&M and capital investment. Significant advances in methodology are feasible and are being developed in this important EPRI-funded project. Christopher A. Kurtz is manager of substation construction and maintenance 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. chris.kurtz@kcpl.com 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. GaryFord@pna4u.com 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 utility in a variety of technology assessment and development roles. Vainberg received an MSEE degree from the University of Toronto. MarkVainberg@pna4u.com 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. mike@mikelebow.com 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 development of test and measurement instrumentation. Ward serves on the Transformers Committee of the IEEE Power Engineering Society. Ward earned a BSEE degree from the University of Bradford, England. baward@epri.com 43