As a core component of modern power systems, the operational quality of dry-type transformers directly impacts the reliability and cost-efficiency of electrical networks. Statistics show that 37% of transformer failures are caused by improper operation, resulting in annual losses of hundreds of millions of yuan for enterprises. Moving beyond traditional maintenance frameworks, this article proposes a three-dimensional operational management model from a lifecycle perspective, offering innovative solutions for the scientific operation of dry-type transformers.

1. Three-Dimensional Dynamic Balance of Environmental Parameters

The operating environment of dry-type transformers is a dynamically changing complex system. Field data from a substation reveals seasonal temperature variations in windings up to 28°C and humidity fluctuations exceeding 40%. Such drastic changes demand dynamic control mechanisms.

Temperature control should adopt gradient management rather than fixed thresholds. When ambient temperature rises from 20°C to 35°C, the recommended load rate should decrease gradually from 100% to 85%, and axial fan operation should shift from continuous to intermittent mode. Humidity management requires precise dew-point monitoring: if relative humidity exceeds 75%, dehumidification systems must activate even when the transformer is idle.

Dust prevention necessitates a three-tier filtration system:

• Architectural-grade dust screens for large particles
• Positive-pressure ventilation in equipment rooms to block fine dust
• Nanocoatings on windings to prevent electrostatic adhesion
  A coastal substation implementing this approach reduced dust accumulation on windings by 82%.

2. Multidimensional Applications of Intelligent Monitoring Systems

Traditional infrared thermometers have blind spots covering 30% of surfaces, while distributed optical fiber temperature sensors enable full-field winding thermal imaging. In an industrial park case study, this technology detected a 2°C abnormal temperature rise in a Phase C winding, providing a 48-hour early warning for interturn short circuits.

Partial discharge monitoring requires multiparameter correlation analysis. By integrating discharge magnitude, frequency, and phase distribution with machine learning algorithms, fault identification accuracy can exceed 95%. A data center using this system reduced average response time to 15 minutes.

Vibration spectrum analysis is critical for mechanical fault detection. Loose core clamps shift vibration frequencies from 100Hz to 80Hz with amplitude increases over 3dB. Quarterly baseline spectrum comparisons and vibration signature databases are recommended.

3. Full Lifecycle Health Management Strategies

3. Full Lifecycle Health Management Strategies

Load management should follow the peak-valley dynamic adjustment principle. A commercial complex using smart capacity-switching systems saved 180,000 kWh annually by shifting to low-capacity mode during off-peak hours, extending equipment lifespan by 15%.

Predictive maintenance relies on a forecast-diagnose-decide closed-loop system. Big-data-driven models accurately predict insulation aging. A substation applying this model optimized maintenance intervals from fixed 12-month cycles to flexible 8–15-month schedules, reducing spare parts inventory by 40%.

Abnormal condition response requires a three-tier protocol:

• Automatic load reduction for temperature anomalies
• Audible/visual alarms for excessive discharge
• Millisecond-level tripping for short circuits
  A chemical plant using this protocol prevented a major failure, avoiding ¥3 million in losses.

Conclusion

Scientific operation of dry-type transformers is a systematic integration of physical parameters, intelligent monitoring, and management strategies. A provincial power grid company implementing the three-dimensional model increased mean time between failures (MTBF) from 5.2 to 7.8 years while reducing operational costs by 29%. This demonstrates how innovative management models can unlock significant value in power asset management. With the future integration of digital twin technology, dry-type transformer operation will enter an era of holographic intelligence.