Transformer Intelligent Online Monitoring System Manufacturer

I. System architecture and composition

1. Perception Layer (Data Acquisition Layer)

• sensor network::
  • Electrical parameters: Voltage/current transformers, winding resistance testers, short circuit impedance sensors.
  • Temperature monitoring: PT100 RTD (oil temperature), fiber optic sensor (winding temperature), infrared camera (casing/case).
  • oil service status: Oil chromatography online monitoring device (built-in gas chromatograph), oil quality sensors (water content, acid value, breakdown voltage).
  • Mechanical and Insulation: Vibration acceleration sensors, ultra-high frequency (UHF) partial discharge sensors, ultrasonic PD sensors, core ground current sensors.
  • Environment and support: Temperature and humidity sensors, smoke detectors, cooling system current/flow sensors.

2. Network layer (data transmission layer)

• communication method::
  • Fieldbus (e.g. Modbus, CANopen): Connects local sensors to the acquisition unit.
  • Wireless transmission (4G/5G, LoRa, WiFi): for remote sites or mobile monitoring scenarios.
  • Optical fiber communication (OPGW): high bandwidth, anti-interference, suitable for high-speed data transmission within substations.
• Edge Computing Unit: Preliminary filtering and feature extraction of the collected data to reduce the pressure of cloud transmission.

3. Platform layer (data processing and application layer)

• Cloud Server / Local Server::
  • Stores historical data (e.g., temperature trends, oil chromatograms) and supports terabyte-level data management.
  • Deploy data processing algorithms (e.g., machine learning models, expert systems for troubleshooting).
• Intelligent Analytics Platform::
  • Troubleshooting: based on threshold comparisons, historical data comparisons, and pattern recognition (e.g., DGA triple ratio method for analyzing oil chromatograms).
  • Lifetime prediction: Estimation of the remaining lifetime (RUL) of the equipment by means of a degradation model (e.g. Weibull distribution).
  • Health Score: Comprehensive multi-dimensional parameters to generate the device health index (HI), divided into "normal / attention / abnormal / serious" status levels.

4. Application layer (HCI layer)

• monitoring terminal::
  • Web-based management platform: visualized interface to display real-time data, trend curve, alarm list and 3D equipment model.
  • Mobile APP: Support remote view, alarm push (SMS / WeChat), O&M task dispatch.
• Reporting and Decision Support: Automatically generate operation and maintenance reports, fault analysis reports, and assist in the development of maintenance plans (e.g., condition maintenance instead of periodic maintenance).

II. Core functions and technical characteristics

1. Multi-parameter fusion monitoring

• Cross-dimensional data correlation: For example, abnormal oil temperature is analyzed in conjunction with load current and cooling system status to avoid misjudgment of a single parameter (e.g., high oil temperature may be caused by load overload or fan failure).
• case (law)When the concentration of C₂H₂ in the oil chromatogram rises, the system automatically retrieves the partial discharge monitoring data and determines whether there is an arc discharge fault.

2. Intelligent Diagnostics and Early Warning

• algorithmic model::
  • Threshold warning: Preset safety thresholds (e.g. oil temperature ≥95°C alarm, ≥105°C trip).
  • Trend alerts: Establishment of a baseline based on historical data (e.g. triggering of a warning if the growth rate of the winding temperature is >5°C/h).
  • machine learning: Use neural networks or random forest algorithms to identify hidden faults (e.g., early insulation moisture) from massive amounts of data.
• fault localization: Estimation of winding deformation positions or discharge point coordinates by spectral analysis of vibration signals or by an array of PD sensors.

3. State Visualization and Remote O&M

• 3D visualization: Construct a digital twin model of the transformer to visualize the internal structure and the location of anomalies (e.g., highlighting hot spots).
• unattendedThe cloud can realize the closed loop of "Monitoring - Analysis - Early Warning - Disposal" through edge computing and cloud collaboration, and reduce the frequency of manual inspection.

III. Typical application scenarios

1. Substation main transformer monitoring

• Requirement: 24/7 monitoring of high voltage class (110kV and above) transformers to prevent short circuit faults or insulation breakdowns.
• Solution: Configure oil chromatography online monitoring, winding deformation FRA test, UHF local discharge monitoring, combined with the substation integrated automation system to realize the linkage protection.

2. Intelligent monitoring of distribution transformers

• Requirements: Lightweight monitoring of transformers in low and medium voltage distribution networks (10kV/0.4kV) to reduce O&M costs.
• Solution: Adopt low-cost sensors (e.g. wireless temperature sensors, vibration modules) and upload data via LoRa network, focusing on monitoring overload, temperature rise and three-phase imbalance.

3. Specialty Transformer Monitoring

• Scenarios: wind power transformers (offshore / plateau environments), rectifier transformers (metallurgical industry), traction transformers (rail transportation).
• Characteristics: Increase the environmental adaptability design for special working conditions (such as anti-salt spray, anti-vibration), strengthen the temperature and partial discharge monitoring.

IV. Technical advantages and value

1. Improve equipment reliability

• Early detection of latent faults (e.g., early partial discharges can advance fault warning time by 3-6 months), reducing the probability of unplanned outages.
• Case: A 500kV transformer was found to have a continuous rise in C₂H₂ through oil chromatography monitoring, and timely maintenance was carried out to avoid a winding turn-to-turn short-circuit accident.

2. Optimize O&M efficiency

• Shift from "Periodic Maintenance" to "Conditional Maintenance" to reduce the number of unnecessary outages (O&M costs can be reduced 30%-50%).
• Automatically generates maintenance work orders to guide O&M personnel in pinpointing faults (e.g. "Winding phase A is deformed, frequency response test is recommended").

3. Supporting Smart Grid Construction

• Provide the underlying data support for the Electricity Internet of Things (EIOT), and help power grid digital transformation (e.g. data interoperability with SCADA system and EMS system).

V. Development trends

• AI Deep Application: Introducing deep learning algorithms (e.g., Transformer model) to process unstructured data (vibration waveforms, infrared images) to improve fault recognition accuracy.
• digital twin deepening: Combine the physical model with real-time data to construct a high-precision transformer virtual model to simulate the performance changes under different working conditions.
• Edge - Cloud Collaboration: Deploy lightweight AI models on the edge side to enable real-time data analysis and local alerts, reducing cloud dependency.

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