Utilities have made significant investments in substation and transmission assets. Substations are critical for reliable operation of power systems and serving customer loads. Substation assets, such as transformers, circuit breakers, instrument transformers (Current Transformers (CT), Potential Transformers (PT), Coupling Capacitor Voltage Transformers (CCVT)) and Intelligent Electronic Devices (Relays, PMUs, DFRs), must operate properly for power system operations, grid reliability, and personnel safety. Failure of these assets could cause forced outages, personnel safety issues, switchyard equipment damage, power system reliability issues, etc. PMU data from PTs, CTs, CCVTs, etc., can be used to monitor equipment signatures and identify anomalies that are precursors of potential equipment failure. Early warning, and timely intervention to repair or replace equipment before catastrophic failure will promote safety, reduce costs and prevent outages.
Intelligent Transmission Asset Monitor (iTAM) Platform uses advanced analytic methods to detect equipment malfunction and precursors to equipment failure. By monitoring signal signature anomalies iTAM can detect potential equipment malfunction and alert utility personnel in real-time to diagnose the malfunction and take appropriate actions to prevent equipment failure, customer outages and injury. It will also help avoid relay system mis-operation, equipment damage, forced outages, and safety hazards, and support calibration of Instrument Transformers (IT) & Intelligent Electronic Devices (IED).
iTAM provides unprecedented visibility into the status and health of secondary equipment, especially CTs, PTs and CCVTs, by using high-resolution (30 samples per second and above for 60 Hz systems and 25 samples per second and above for 50 Hz systems) synchrophasor data to monitor signatures and data patterns to identify anomalies. It enables utilities to use synchrophasors as a complement to existing asset health monitoring tools.
The model-based method uses Substation Linear State Estimation (SLSE) and compares the SLSE estimated data to the measured PMU data. This method ignores system events such as line faults and tripped breakers and only detects local events/anomalous data that may indicate equipment malfunction. The data driven methods rely on statistical comparison of previous data in a time window to the latest data. When the latest data has an error and falls outside of what is expected given its history, an alarm will be triggered. The data driven methods cannot by themselves differentiate between system events that affect all the measuring devices, such as line faults and tripped breakers, and local events/anomalous data that effect very few measuring devices and may indicate hardware failure. Therefore, additional logic has been implemented to avoid alarming on system events.