A stubborn pre-requisite for data-driven optimized lifelong management
Richard Harris (Ph.D.), Jun Seo (Ph.D.), and Terry Woodcroft (B.Eng. MBA(Tech)) Schneider ElectricInc.
There are many varied methods used in industry to manage the ever-increasing number of aged transformers in the world fleet caused by the industry-wide squeeze on capital expenditure. These range from annual or biennial manual oil testing to high accuracy on-line multi-gas DGA analysis.
Understanding the state of a transformer and its optimum operating envelope throughout life requires analysis from a wide range of perspectives. Theoretically transformer aging should be pro-actively optimised, however this is rarely achieved due to a poor understanding of the true state of the internal paper insulation. The cause of this poor understanding is the affordability of suitably accurate analysis techniques and often little understanding of usage history. Once paper insulation is degraded, the risk of operation becomes excessively high and the transformer should be scrapped. This decision is dependent upon an accurate understanding of insulation state, the risk appetite of the owner organisation, plus the risk profile of the specific installation. Without accurate information on insulation state, the asset manager has little option but to be conservative. This drives premature replacement decisions industry wide.
Maximising transformer life requires (a) minimisation of damage throughout life, and (b) management of use in accordance with risk during the end-of-life phase.
Water ingress and excess temperature events throughout life are the main drivers of irreversible damage to paper insulation. To manage a transformer reliably, especially during end-of-life, requires a clear understanding of current insulation state. Moreover, forecasts of insulation state are also needed for the alignment of increasing risk of failure with managed organised replacement.
However, neither can be usefully achieved using the industry standard - manual oil testing, as it provides a myopic, sporadic and distorted picture of insulation state until significant failure modes are evident.
To manage aging in accordance with usage requirements and long-term goals, continuous monitoring is essential for careful, responsive and appropriate maintenance and operational management. In addition, continuous real time data is also capable of minimising the risk of catastrophic failure.
This paper discloses new techniques using affordable IoT solutions which enable the water content and polymer age of the insulation to be accurately tracked over life with accuracy. These techniques are based on state-of-the-art sensor and IoT technologies and complete transformer insulation system modeling theory. It also discusses how many transformers can be safely operated at nameplate ages far older than traditionally accepted. The proposed technology solves the challenge of identifying critical implicated insulation condition of transformers and the most appropriate operating envelope to maximize their life until replacement.