Transformer Loading Considerations Using AMI Loading Data to Calculate Loss of Life Presented by Steven Dennis May 2-4, 2018 SWEDE 2018 – Tulsa, Oklahoma

Why Consider Transformer Loading Oil/air cooled transformers are typically between 95 and 99 percent efficient at transformation The inefficiency of a transformer is materialized as an energy loss in the form of heat Excessive heat accelerates the deterioration of the paper insulation, oil and gaskets The breakdown of these components will ultimately lead to failure with all or some of the following consequences Bridged winding (typically an increased output voltage) Faulted winding Tank rupture Oil spill The ability to predict and prevent an overloading failure limits environmental impact, improves customer experience and reduces operational expenses C57.91-1995 - IEEE Guide for Loading Mineral-Oil-Immersed Transformers Reference: https://www.l-3.com/private/pacific_crest/articles/Fundamental_Principles_Of_Transformer_Thermal_Loading_And_Protection_ERLPhase_TexasAM2010.pdf

Factors to Consider When Determining Risk Traditional Approach Estimate loading from kWh readings This method is error prone because of all of the assumptions that have to be made What the coincident peak is in kVA The duration of excessive load How much longer the transformer will last before failure That the connectivity model is accurate With this method you have to set an arbitrary threshold such as 140% nameplate at an estimated peak

Factors to Consider When Determining Risk AMI Approach Loading comes from kWh intervals which gives a more accurate picture of the actual load curves and durations This method mitigates some of the traditional assumptions Coincident peak in kVA – Much higher resolution (15 minute vs 30 Days) The duration of excessive load Calculate the loss of life in percent (C57.91-1995 - IEEE ) Validation of the connectivity model with voltage statistics (Pearson Correlation) This approach allows you to accumulate loss of life and rate of change With this system you can prioritize by the likelihood of a near term failure

Effective Load Metering When the load changes on a transformer the temperature equilibrium lags behind the actual loading Individual AMI meters can supply enough resolution to bridge the time constant of thermal lag for calculating internal temperatures Aggregated loading can be used from the connected meters as long as there is confidence in the accuracy of the connectivity model

Transformer Characteristic Tracking We use an inventory model to track the physical characteristics of each transformer Rating in kVA Weight Oil capacity Purchase date Install date This inventory model is linked to a table of manufacture specifications for each transformer design No load loss Full load loss Thermal time constant Etc. Tables in our electrical model hold the meter to transformer connectivity data The transformer details are accurate but the hit rate back to the connectivity model is less than 100%.

Risk Ranking Risk is calculated using Validated connectivity Loss of life in percentage Rate of change for the loss of life Risk is ranked as a projection to the nearest point of 100% loss of life using historical data

Optimal Conditions for Load Modeling Loss of life modeling is a continuous and accumulative process The rate of change in loss of life is key to forecasting a failure and is not necessarily correlated to seasonality. In many cases it is associated with connected load changes.

Questions?