Shorted Coil Analysis

Challenges

Thousands of distribution transformers fail each year. Most transformers are unmetered assets and therefore, assessing degradation of performance is difficult to discover due to the lack of data. These critical system assets are expensive and difficult to replace during an unplanned outage and therefore need to be constantly monitored for performance to avoid sudden and potentially catastrophic degradation.

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Predicting Distribution Transformer Failures

Today many utilities have deployed advanced metering infrastructure (AMI) meters that report voltage and load readings in 30-minute intervals. This unprecedented volume of data not only enables a deeper understanding of the operation of the system, it provides the utility a means to perform predictive analytics — the exploration of data with machine learning methods — to predict distribution transformer outages. The machine learning methods applied may also be used for predictive analytics on any devices where downstream data is being collected.

Tens of thousands distribution transformers fail each year. Generally the point of failure for some transformers is preceded by an abnormally high output voltage from a fusion of the high-side windings, which changes the effective ratio of the transformer. A similar failure mode exists wherein the low-side windings fuse, resulting in an abnormally low output voltage. By examining the voltage profile of transformers PowerRunner can accurately and proactively identify distribution transformers that require immediate decommissioning and replacement.

PowerRunner Benefit

  • Identify problem assets before an outage occurs

  • Extend the life of existing transformers

  • Predict transformer failure in advance and avoid costly repairs

  • Minimize customer outages by proactively replacing failing transformers

  • Cheaper and safer scheduled replacements during normal working hours

  • Protect the safety of staff consumers and the general public

  • Avoid “blue sky” outages

  • Improve CAIDI  and SAIFI metrics

PowerRunner Analytics

  • Analyze voltage readings and profiles at every meter

  • Correlate changes in voltage at the meter with meter events or upstream system events

  • Create virtual voltage readings and profiles unmetered assets