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Trends in Power Transformer Performance Management

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John Brett's picture
President & CEO Delta-X Research Inc.

Over 30 years, John Brett, P. Eng. has held leadership roles in organizations that operated or provided solutions for demanding applications including utility operations, industrial automation...

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  • Jan 28, 2019

This item is part of the Special Issue - 2019-01 - Predictions & Trends, click here for more

Utilities are facing a crisis when it comes to transformer failure. Because of changes in manufacturing in the late 1980s and the rising cost of certain metals, we are entering a “perfect storm” period, where double the usual number of transformers will become end of life just as the cost for new transformers sharply increases.

This means there is a greater need than ever to ensure the best possible maintenance and care for existing transformer fleets.

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An essential test used to provide early warning of potential faults within transformers is Dissolved Gas Analysis (DGA), which evaluates the transformer through tracking and interpreting certain gases created by heat-generating faults and dissolved in the insulating oil. These fault gases can be light hydrocarbon gases created when the insulating oil is exposed to heat, or carbon oxide gases generated when paper insulation breaks down.

Two important trends are emerging in the field of DGA interpretation:  first, the pace of technology improvements and utility adoption of online DGA monitors is accelerating in line with grid modernization trends; and second, increased data volumes and higher reliability expectations are driving requirements for more rigorous interpretation of DGA test data.  Increased reliability is also a driving force behind the adoption of online monitors.

Online Monitoring

Traditionally, utilities conduct dissolved gas analysis tests on their transformers only once or twice a year, because the cost and inconvenience of manual sample collection outweighed the risk of an unplanned outage as internal transformer faults normally evolve slowly and can be caught with an annual test cycle.  Increased testing frequency was typically reserved for transformers that had previously demonstrated abnormalities or transformers that are highly critical, such as those at generation stations.  In recent years, online DGA monitors have been deployed on the most critical transformers and are often included in bid specifications for new transformers.

Concepts like Internet-of-Things (IoT) and digital twins are increasingly promoted because sensor technologies are more accessible and more useful than they have ever been.  Online DGA monitors, particularly multi-gas monitors, are much more complex than simple temperature or moisture sensors, but in the last five years we have seen the number of vendors offering DGA monitors almost double as a result of new technology development or acquisition.  New technologies such as photo-acoustic and infrared light spectroscopy have recently been introduced to online DGA monitoring in order to increase the reliability and reduce the total-cost-of-ownership of these sophisticated devices.

While online DGA monitors greatly improve visibility of the well-being of a transformer fleet, they also generate more data for processing, which by its nature is more volatile than laboratory sample data.  This volatility challenges conventional DGA software that typically relies on simple limits or thresholds to sound alarms.   To address volatility, high-voltage equipment experts are looking for transformer monitoring software solutions that interpret DGA data using rigorous statistical methods based on real-life reliability data.  Such solutions must: (a) account for the vagaries of data produced by sensitive equipment deployed in harsh substation environments; and (b) provide a dependable result that informs the transformer operator as to whether or not any abnormalities are present.

Despite recent technology advances, online DGA monitors continue to be sophisticated electronic devices with a shorter life expectancy than that of the transformers they are monitoring.  It is not uncommon for monitors to fail or report increasingly inaccurate data unbeknownst to transformer operators.  Hence, the supporting DGA software should also “monitor the monitors” to avoid periods of blind or misinformed oversight over these most critical assets.  The software must be compatible with monitors from any manufacturer in order to avoid limitations of proprietary obstacles and allow operators to choose or change manufacturers as needs dictate.  Treating online DGA monitors as another asset to be proactively managed will help maintain maximum reliability of both the monitors and the transformers they are safeguarding.

Increasing Rigor in DGA Analytics

Interpreting DGA data is a three-step process: (a) detect a fault; (b) assess the severity of the fault; and (c) identify the type of fault.  The third step, fault type identification, is well understood and gas ratio methods such as the Duval Triangle have proven to be highly dependable.  However, the first two steps leave much room for improvement.  Conventional DGA interpretation methods are known to be inconsistent in their ability to detect and assess faults, leading to regular reports of unnecessary maintenance due to false alarms and occasional severe service disruptions due to unforeseen catastrophic transformer failures.

First principles science is being applied to better understand fault gas production leading to transformer failure, in an effort to avoid such failures.  The simple limits or thresholds-based model of conventional DGA has certainly helped, but it does not include a rigorous statistical analysis of transformer reliability data, including failure events.  Reliability-based dissolved gas analysis is a new method for interpreting DGA data that correlates fault gas production with transformer failures.

Using thermochemistry, fault gas concentrations of a transformer can be converted into a fault energy index, which represents the amount of energy expended by an internal fault.  A fault energy index provides a better characterization of a fault than the symptomatic dissolved gases; simply put, the more energy expended by a fault, the greater the severity of the fault.

Reliability-based DGA considers the fault energy indices and the failure histories across a large population of transformers and performs a survival analysis to establish a failure model.  The fault energy index for a single transformer can then be compared with the failure model to understand what percentage of transformers in a similar state would have failed.  If the transformer continues to gas, then Reliability-based DGA provides an objective risk assessment, based on the failure model, of the gassing event.

The application of first principles science, namely thermochemistry and mathematics, is greatly enhancing the interpretation of DGA data.  When Reliability-based DGA was implemented at a leading utility with a large transformer fleet, the utility engineers reported that Reliability-based DGA significantly outperformed conventional DGA.  Many transformers that were identified as abnormal without actually having an active problem were no longer being flagged, thereby eliminating many “false alarms”.  Furthermore, Reliability-based DGA raised awareness of a number of transformers that were experiencing an ongoing fault but had not generated enough gas to exceed the limits provided by conventional DGA.  Such verified alarms allow the utility to take early corrective action, avoid a potential failure, and extend the life of its transformers with proper maintenance.

Overall, Reliability-based DGA improves the efficacy of maintenance planning, saving utilities time and money while also increasing overall system reliability and ensuring that transformers remain functional for as long as possible.  Asset managers are also better informed to plan asset replacements and justify capital spending.

This year will see transformer monitoring continue to grow and more rigor applied to data interpretation in order to provide a clearer understanding of individual transformers and transformer fleets.  With more and better information, operators will be better equipped to prioritize maintenance and asset replacements and will make better operational and financial decisions with confidence.

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