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The COVID-19 pandemic has shown all of us that the reliability of our critical electrical infrastructure is the foundation for a safer society.
byIsmail Güner
- Introduction
The COVID-19 pandemic has shown all of us that the reliability of our critical electrical infrastructure is the foundation for a safer society. Utilities faced new challenges such as limiting or cancelling planned outages, disruptions in the supply chain, need for personal protective equipment for maintenance crews, adapting to changes in electricity consumption patterns, delays in maintenance activities, loss of personnel due to self-isolation measures. These new challenges have forced electrical utilities to adjust their prioritisation methodologies of critical equipment lifecycle management decisions.
Prioritisation of maintenance activities is the most challenging task in asset management. Best prioritisation decisions are made based on reliable asset health indices, data analysis, failure mechanism prediction models, budget, required outage duration, and human resources considerations.
Every electrical utility is unique in its own way of defining condition assessment and prioritisation of maintenance activities. Different approaches are used by utilities when it comes to deciding between risk-based or time-based maintenance using the health index of its transformer fleet. [2]
An effective maintenance program requires reliable model algorithms that consider multi-level failure mechanisms and interpretations of condition-assessment actions. Health or assessment indices are the foundation of an efficient transformer lifecycle management prioritisation model.
Investment in performance reporting and predictive modelling is one of the key elements that will ultimately provide electrical network operators with an important decision-making tool. Truly successful decision making relies on a balance between deliberate and instinctive thinking. The goal for all of us is to be able to make quick judgements based on reliable predictive models in order to prioritise proactive maintenance actions.
- Anticipating the future
Rapidly ageing transformer fleets require more complex lifecycle management metrics. Due to the unprecedented industrial growth in the 60s and 70s, power networks in North America were forced to expand significantly in the following 20 years. Now, a significant majority of all power transformers in operation in North America are in the second half of their life expectancies. The good news is we have accumulated the empirical data on older designs over the years. This will let us identify genetic or design-related problems and predict possible failure mechanisms.
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