Who this is for: DISCOM engineers, asset managers, grid operations and reliability teams
Begin with one asset group and failure mode
A distribution transformer, transmission transformer and switchgear assembly have different operating stresses and inspection requirements. Choose a bounded group of assets and identify the condition you want to detect, such as abnormal heating, loading behaviour or repeated protective events. Ask engineers which observations currently trigger checks and which records would be needed to confirm an abnormal condition. Avoid promising that one generic model can predict every failure mode.
Connect measurements to asset identity
Telemetry becomes useful when every reading is mapped to a known equipment identifier, location and timestamp. Join permitted SCADA events, loading history, maintenance work orders and inspection observations. Pay attention to meter changes, time synchronisation, missing periods and calibration. If a model cannot tell which asset produced a reading, a confident risk score is not actionable. Establish a clear data owner and a correction process before integrating more sources. Engineers should also mark planned sensor maintenance, communications outages and meter replacement periods in the event history. An AI alert that arrives during a data gap must show that limitation explicitly; treating missing readings as normal observations can produce misleading confidence.
Compare against operating context
A higher temperature can be related to demand, ambient conditions or abnormal performance. Engineers therefore need a contextual comparison, not just a fixed dashboard threshold. Explain which signals changed, over what time window, and how they differ from comparable normal behaviour. Use explicit engineering constraints where available and avoid letting an opaque prediction override the utility’s protection or switching procedures. Model outputs are decision support, not dispatch commands.
Turn the signal into a reviewable alert
A useful alert should identify the affected asset, the observed deviation, evidence sources, data freshness, operational consequence and the next safe investigation step. Provide links to recent maintenance and inspection records. Let operators mark an alert as confirmed, false, already known or requiring more data. Alert fatigue is a quality problem: a flood of unexplained warnings can decrease rather than increase operational reliability. The model must demonstrate enough specificity to deserve the crew’s attention.
Preserve field engineering control
Inspection priorities and maintenance actions have safety, scheduling and resource implications. The platform should route the suggested task to authorised engineers, who determine safe access conditions and whether a work order is justified. Do not automate switching, isolation or other critical grid operations based solely on AI. Record what the crew found, how the equipment behaved later, and whether the alert helped. That feedback becomes part of future validation.
Choose performance measures that reflect value
Report alert precision, missed relevant events, lead time before verified intervention, inspection backlog, operator review time and data coverage. Analyse performance by equipment type and season rather than quoting one blended accuracy percentage. Any avoided-outage or maintenance-savings claim requires a defensible counterfactual and enough observation time. A model that appears accurate on retrospective data may still fail when sensors drift or asset operation changes.
A bounded demonstration for a utility team
The first activity is a technical discovery with the asset register, available telemetry and maintenance history. Build a quality report, then test a limited anomaly-detection method against historical events. Show a supervisor-ready screen where alerts include source records and a clear acknowledgement path. Pilot on a bounded feeder or substation and compare alert usefulness with existing engineering practice. Expand only when false-alarm costs and data limitations are acceptable.
Make the next discussion specific.
Bring the current workflow, available records and the decision you need to improve. We can discuss an evidence-based assessment and sensible controls before proposing technology.
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