Condition monitoring of transformers and grid components continuously delivers data on the current condition of the equipment. Sensors and intelligent evaluation systems detect changes at an early stage – long before damage occurs. This allows maintenance and replacement to be planned proactively. Condition monitoring is fundamentally changing the maintenance paradigm of energy technology – shifting from a rigid schedule to intelligent, data-driven maintenance.
What parameters are measured in condition monitoring?
Typical measured variables include oil temperature, winding temperature, load profile, partial discharges, gas composition in the oil (DGA), moisture, and the tap changer’s switching cycles. Modern systems analyze this data in real time, compare it against historical values, and raise an alarm if trends change noticeably.
Key Characteristics at a Glance
- Real-time monitoring of key operating parameters
- Online DGA measurement for oil-type transformers
- Partial discharge measurement as an indicator of insulation condition
- Trend analysis with comparison against reference curves
- Integration into asset management systems
- Cloud-based evaluation for cross-asset analyses
- AI-supported forecasts identify developing damage at an early stage
Frequently Asked Questions
What advantages does condition monitoring offer over time-based maintenance?
Instead of fixed maintenance intervals, measures are carried out only when they are actually needed. This saves costs, extends inspection intervals, and reduces the risk of unplanned outages. The added value is particularly significant for strategically important installations.
Is condition monitoring worthwhile for smaller installations too?
For smaller distribution transformers, comprehensive online monitoring is usually not economically worthwhile. Periodic inspections and thermographic measurements are generally sufficient here. However, as sensor and software costs continue to fall, this threshold is increasingly shifting downward.
What role does artificial intelligence play in condition monitoring?
AI algorithms detect patterns and trends in large volumes of data that humans could not otherwise oversee. For example, they identify gradual deterioration or unusual combinations of measured values, enabling significantly more precise prediction of maintenance needs and potential failures.
How does condition monitoring integrate with asset management?
Data from condition monitoring feeds into central asset management systems, where remaining service life, maintenance planning, and risk assessments are carried out for the entire equipment fleet. This allows investment decisions to be made on a data-driven basis and limited budgets to be directed specifically where the need is greatest.
Would you like to increase the availability of your strategically important installations through condition monitoring? We advise you on suitable solutions – contact us.