Investigating cost-benefit analysis for digital distribution transformers – Part I
Due to the changing dynamics and increasing complexity of today’s electricity grid, the need for dynamic asset management strategies is very evident.
byBhaba P. Das
Due to the changing dynamics and increasing complexity of today’s electricity grid, the need for dynamic asset management strategies is very evident, especially for distribution transformers. However, asset management strategies have not evolved accordingly and still rely on the same time-based maintenance strategies that have been used for decades. Others have relied on N-1 contingency or maintaining surplus stock.
Sometimes, these strategies are insufficient for today’s needs and lead to uninformed investments and operational decision by businesses. An example of such a strategy is transformer sizing in residential applications. This selection is based on a number of assumptions and the fact that distribution transformers can be easily stocked and replaced when the failure occurs. One such assumption is to provide an average load based on home size, while a second assumption involves sizing the transformer based on a peak period (e.g., few hours). An unofficial policy adopted by many is to install a higher rated transformer than necessary and rely on the fact that most transformers see only around 40- 50 % of its rated load. Based on this, end-users have enjoyed very long lives from transformers that have only been loaded heavily at times of peak load and high temperatures. However, there is a growing concern that such transformers may become overloaded than when they were originally planned due to new types of load, such as electric vehicles [1], and the question is – how much higher sized transformer would be appropriate?
Over time, these simple decisions become more expensive to correct. Thus, increasing the risk of unplanned outages and resulting losses. This challenge is more serious for transformers in mission-critical applications or large loading variability as seen in the chemical, oil and gas, renewables, semi-conductor, data centres, and marine and mining industries. Without the right kind of data, only a small percentage of transformer issues can be proactively addressed.
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