Operation Temperature Monitoring and Thermal Aging Assessment of Power Transformers

1. Significance of Temperature Monitoring in Power Transformers

• Real-Time Health Assessment: Detecting abnormal heat sources (e.g., loose connections, overloads).

• Lifespan Extension: Maintaining temperatures within design limits to slow thermal aging.

• Safety Enhancement: Preventing thermal runaway and fire hazards.

1.1 Thermal Stress and Insulation Degradation

2. Temperature Monitoring Technologies

2.1 Sensor Types and Installation

2.1.1 Contact Sensors

• Thermocouples: Measure temperature via the Seebeck effect; suitable for high-temperature zones (e.g., winding hotspots).

• Resistance Temperature Detectors (RTDs): Use materials like platinum (Pt100) for high accuracy (±0.1°C) in oil or air.

• Thermistors: Semiconductor-based sensors with high sensitivity but limited temperature range (-40°C to 125°C).

2.1.2 Non-Contact Sensors

• Infrared (IR) Cameras: Detect surface temperatures via thermal imaging; ideal for outdoor transformers and bushings.

• Fiber Optic Sensors: Use Raman scattering or Bragg gratings for distributed temperature sensing (DTS), enabling precise hotspot localization in windings.

2.1.3 Online Monitoring Systems

• IoT-Based Solutions: Wireless sensors (e.g., Zigbee, LoRa) transmit data to cloud platforms for real-time analytics.

• SCADA Integration: Connect temperature sensors to supervisory control systems for centralized monitoring and alarm triggering.

2.2 Key Monitoring Points

• Winding Temperature: Directly measured via embedded RTDs or inferred from oil temperature and load (IEC 60076-7 standard).

• Oil Temperature: Monitored using thermometers in the tank or cooling system; reflects heat dissipation efficiency.

• Core Temperature: Indirectly assessed via oil temperature and magnetic loss calculations.

• Cooling System Components: Fans, pumps, and radiators to ensure proper heat dissipation.

3. Thermal Aging Mechanisms and Assessment Models

3.1 Mechanisms of Thermal Aging

1. Cellulose Degradation (Paper Insulation):

• Hydrolysis and oxidation break down paper fibers, reducing tensile strength and increasing conductivity.

• Byproducts include furfural, CO, and CO₂, detectable via oil chromatography (DGA).

2. Oil Degradation:

• High temperatures cause oil to oxidize, forming acids, sludge, and gases (e.g., methane, ethylene).

• Acid number and breakdown voltage (BDV) are key indicators of oil health.

3. Polymer Degradation (Dry-Type Transformers):

• Epoxy resins and synthetic insulators undergo thermal cracking, leading to partial discharges (PD) and insulation failure.

3.2 Aging Assessment Methods

3.2.1 Life Expectancy Models

• IEEE Guide for Loading Transformers (C57.91):
  Uses the thermal aging rate $v$ and cumulative damage $D$ to predict remaining life:$v=\exp \left(\frac{E_a}{k}\left(\frac{1}{T_0}-\frac{1}{T}\right)\right)$$D=\sum (v\times \Delta t)$

• $E_a$: Activation energy (40–60 kJ/mol for oil-paper systems).

• $T_0$: Reference temperature (e.g., 98°C).

• $T$: Operating temperature (K).

• Endurance Time Method:
  Determines the time to reach 20% tensile strength loss in paper insulation at a given temperature. For example, paper aged at 110°C may fail in 5 years, versus 20 years at 98°C.

3.2.2 Diagnostic Techniques

1. Oil Chromatography (DGA):
   Analyzes dissolved gases (e.g., CH₄, C₂H₄, H₂) to identify thermal faults. The IEC 60599 code links gas ratios to specific failure modes (e.g., low-temperature pyrolysis vs. high-energy arcing).
2. Frequency Domain Spectroscopy (FDS):
   Measures the dielectric response of insulation to assess moisture content and aging. A higher dielectric loss tangent ($\tan \delta$) indicates degraded insulation.
3. Dynamic Thermal Rating (DTR):
   Uses real-time load, ambient temperature, and cooling data to calculate the transformer’s actual capacity, optimizing load distribution and aging management.

4. Thermal Aging Assessment Workflow

4.1 Data Collection

• Baseline Data: Establish normal temperature profiles under full load and typical ambient conditions.

• Trending Data: Monitor temperature trends over weeks/months to detect gradual increases (e.g., 0.5°C/month rise may indicate deteriorating cooling).

4.2 Risk Evaluation

• Thermal Severity Index (TSI): Classifies risks based on temperature deviations from baseline:

• Green (TSI < 20): Normal operation.

• Yellow (20 ≤ TSI < 40): Moderate aging, investigate causes.

• Red (TSI ≥ 40): Critical, immediate action required.

4.3 Mitigation Strategies

• Load Curtailment: Reduce load during peak temperature periods.

• Cooling System Upgrades: Install additional fans or replace blocked radiators.

• Insulation Refurbishment: Replace aged paper or oil in transformers with high thermal stress.

5. Industry Standards and Case Studies

5.1 International Standards

• IEEE C57.104: Provides guidelines for load forecasting and aging assessment.

• IEC 60076-14: Specifies thermal endurance tests for transformer insulation.

• ISO 19438: Defines methods for monitoring transformer oil quality.

5.2 Case Study: Thermal Aging in a 110 kV Transformer

• Diagnosis: DGA revealed high ethylene (C₂H₄) levels (150 ppm), indicating winding overheating due to a loose connection.

• Action: Replaced the faulty connector, reducing temperature to baseline and avoiding a potential failure.

6. Future Trends in Thermal Monitoring

• AI and Machine Learning: Predictive models using neural networks to correlate temperature data with aging parameters.

• Digital Twin Technology: Virtual replicas of transformers for real-time thermal stress simulation.

• Self-Healing Insulation: Nanocomposite materials that repair thermal damage autonomously.

7. Conclusion