Transformers Testing TRIZ Cases
- Current Transformer Secondary Open-Circuit Fault Detection
Dynamic time-window selection from secondary current changes improves open-circuit detection and avoids false breaker trips.
- Current Transformer Secondary Circuit Interruption Detection
Mean current and change-threshold analysis detects secondary circuit interruptions in current transformers and avoids false breaker trips.
- CVT Voltage Divider Dissipation Factor Measurement Without Disassembly
GST and UST current measurements let engineers assess CVT voltage divider insulation and faults in situ without disassembly.
- Transformer Partial Discharge Sensing With UHF Position Localization
Combining omni-directional and directional UHF sensing with light detection locates transformer partial discharge without shutdown or oil draining.
- Transformer Distance Protection Using Compensated Fault Signals
Compensated delta-side voltages and currents with wye neutral current restore backup distance protection for ground and phase faults.
- Transformer Bushing Partial Discharge Monitoring With Local SIP Digitization
Local SIP digitization near transformer bushing sensors shortens weak-signal paths, cuts electromagnetic interference, and improves monitoring accuracy.
- High-Voltage Device Overload Capacity Using Sensor-Based Load Forecasting
Continuous sensor data, state parameters, and cloud load forecasts reveal real overload capacity from actual device lifetime use.
- Electrical Equipment State Assessment Using Adapted DGA Data
Equipment-specific parameters normalize dissolved gas data so machine learning can assess power equipment state more accurately.
- Transformer Acoustic Monitoring for Partial Discharge and Vibration
Audible sound and vibration analysis helps detect partial discharge, gas-related faults, and abnormal transformer frequencies before failure.
- Transformer Tank Vibration Monitoring for Early Structural Degradation
Dynamic excitation and vibration sensing reveal early transformer structural degradation, helping detect winding and clamping faults sooner.
- Transformer Fault Forecasting Using Dissolved Gas Rate of Change
Machine learning forecasts dissolved gas concentration and rate of change to cut false alarms and improve transformer maintenance timing.
- DC Transformer Harmonic Error Detection With Self-Calibration
Built-in resistor calibration and Fourier-based compensation improve DC transformer error checks for pulsating harmonic signals at remote sites.
- Transformer Failure Diagnosis Using Integrated Deep Belief Networks
Multiple deep belief networks with different learning rates combine vibration-based Fourier features to improve transformer fault diagnosis accuracy.
- Transformer Core Magnetization Control for Consistent Exciting Current Tests
A DC offset is adjusted during transformer tests to minimize residual core magnetism and stabilize exciting current and loss results.
- Delta-Wye Transformer Open Phase Detection via Current Imbalance
By comparing current balance on delta and wye sides, the controller detects open phase faults without extra sensors or injection signals.
- Neutral-to-Ground Voltage Fault Detection in LV Distribution Networks
Neutral-to-ground voltage monitoring detects neutral loss and phase-to-ground faults more accurately than current-based relays in low-voltage networks.
- LT Transformer Monitoring With Remote Failure Alerts
Continuous sensing with GSM-GPRS, GPS, and relay control detects LT transformer faults early and reduces risky manual inspection.
- Transformer Thermal Monitoring With ML-Based Failure Alerts
Thermal imaging and current sensing are combined with machine learning to flag transformer anomalies early and reduce unplanned downtime.
- Transformer Health Monitoring for Through-Fault and DGA Diagnosis
By combining winding current analysis with dissolved gas data, this case detects subtle transformer through-faults before long-term damage occurs.
- Electrical Device Lifetime Prediction Under Temperature and Harmonic Loads
Ambient temperature plus voltage and current data are used to model aging and harmonic heating, enabling more accurate service life prediction.
- Parallel Half-Bridge High-Current Source for Reliable Power Device Testing
Redundant parallel half-bridges distribute test current to deliver high output with lower stress and more reliable portable power-device testing.
- Voltage Transformer Diagnosis Using Homopolar Voltage Thresholds
Peak homopolar voltage and phase RMS comparison expose voltage transformer ratio drift early, helping schedule maintenance before grid instability.
- Three-Phase Asset Fault Detection Using Sequence Error Metrics
Power-flow error metrics and threshold checks reveal developing faults in three-phase assets before damage and unplanned outages occur.
- Distribution Transformer Optical Event Detection for Arcing Alerts
Optical sensing matches light signatures from arcing, fire, and hatch opening to send fast remote alerts for distribution transformers.
- Inductive Equipment Monitoring for Hotspot Temperature Limits
Real-time sensor data and condition-based failure rates are used to infer maximum permitted hotspot temperature for safer inductive equipment loading.
- Distribution Transformer Optical Event Detection for Arc And Fire Alerts
Optical sensing tracks arc, fire, and hatch-opening light signatures in distribution transformers to trigger fast remote alerts and safer maintenance.
- Distribution Transformer Fault Detection Using Oscillography Signals
Current sensors and oscillography profiles detect transformer overloads and faults in real time, enabling earlier alerts and remote monitoring.
- Transformer Inductance Measurement for DAB Converter Health
Capacitor discharge voltage reveals magnetizing-inductance changes, helping DAB converters detect transformer degradation and adjust power output.
- Transformer PLC Auto-Squelch Using RSSI for Fault Isolation
A transformer-by-transformer RSSI test sets PLC squelch levels to limit false downstream signals and improve fault isolation.
- Transformer Fault Diagnosis with Deep Coupled Dense CNNs
Adaptive synthetic oversampling and two-dimensional gas-feature reconstruction help a dense CNN diagnose transformer faults despite imbalanced samples.