Transformer online monitoring system

Date: August 9, 2025 10:39:30

Transformer online monitoring system is an intelligent system for real-time monitoring of transformer operation status and early warning of faults in power systems. It realizes continuous monitoring of key transformer parameters by integrating sensors, data transmission, analysis and diagnosis technologies, thus improving equipment reliability and reducing power outage losses. The following is a detailed introduction from the core composition, monitoring parameters, working principle, functional applications and other aspects:[The following is the network information, specific details of the product price contact us to obtain].

I. Core system components

Transformer online monitoring system consists ofSensor layer, data acquisition layer, communication layer, data processing and application layerThe four components are composed of four parts that work together to accomplish condition monitoring and analysis:

1. Sensor layer

Responsible for the direct collection of various types of state parameters of the transformer, is the system's "sense organ". Commonly used sensors include:

 

  • Gas Sensors: Detects characteristic gases (e.g., methane, ethylene, acetylene, etc.) dissolved in transformer oil and is used to determine insulation aging or failure.
  • Partial Discharge Sensor: Acquisition of localized discharge signals inside the transformer (an early characteristic of insulation defects) by means of ultrasound or electromagnetic coupling.
  • temperature sensor: Monitoring of winding temperature (fiber optic sensor, anti-electromagnetic interference), oil temperature (platinum resistance), ambient temperature.
  • Oil Level / Oil Pressure Sensor: Monitor fuel tank level (to avoid oil shortage) and oil pressure (to reflect the circulation status of the oil circuit).
  • Current / Voltage Sensors: Collecting transformer running current and voltage (for calculating load rate and judging overload status).
  • Vibration sensors: Monitoring of core or winding vibrations (reflecting mechanical faults such as looseness, deformation, etc.).

2. Data acquisition layer

The raw signals (e.g., weak currents, voltages, sound waves) output from the sensors are processed and converted into digital signals. The core equipment isData Acquisition Unit (DAU), features include:

 

  • Signal filtering (to remove electromagnetic interference), amplification (to enhance weak signals);
  • Analog-to-digital conversion (converting analog signals to digital signals);
  • Preliminary data calibration (elimination of outliers).

3. Communications layer

It is responsible for transmitting the collected digital data to the data processing center, which is divided intowired communicationrespond in singingwireless communicationsTwo categories:

 

  • Wired: Ethernet, fiber optic (for short distance, high bandwidth transmission in substations), RS485 bus (low cost, strong anti-interference);
  • Wireless: LoRa, NB-IoT (low-power WAN for remote areas), 5G (high real-time, supports massive data transmission).

4. Data processing and application layer

The "brain" of the system realizes data storage, analysis, diagnosis and decision-making through a software platform. Core functions include:

 

  • comprehensive database: Stores historical and real-time data (e.g., MySQL, Oracle);
  • analysis engine: Trend analysis of data, troubleshooting through algorithms (e.g., neural networks, expert systems);
  • Human Machine Interface (HMI)Visualization of the transformer status (e.g. on the Web, on the monitoring screen) with graphs, alarm messages, etc.

II. Key monitoring parameters and significance

Transformer failures are mostly related toInsulation aging, overheating, mechanical damageRelated, online monitoring needs to focus on the following parameters:

 

Monitoring parameters Monitoring Objects central significance
Dissolved gas in oil (DGA) Gas composition and concentration in insulating oil Reflects aging of insulating oil/paper (e.g., vinyl corresponds to high-temperature overheating), arc faults (acetylene is a characteristic gas)
partial discharge Insulated parts such as windings, bushings, etc. Early signs of insulation defects (e.g. increased discharge may lead to breakdown)
Winding Temperature Core, Winding Exceeding the permissible temperatures accelerates the ageing of the insulation (e.g. limit temperature 105°C for class A insulation).
Oil Temperature / Oil Level Insulating oil in tank High oil temperature reflects poor heat dissipation; low oil level may lead to insulation exposure
Core ground current Core Ground Circuit Normal ≤ 100mA, too large indicates that the iron core multi-point grounding (may produce eddy current overheating)
Casing Loss / Capacitance High Pressure Casing Increased dielectric loss and abnormal capacitance reflect moisture or aging of casing insulation.

III. Principle of operation

The system follows "Sensing - Transmission - Analysis - Decision-making" the closed-loop process:

 

  1. Real-time sensing: Transformer status parameters are continuously collected by sensors (e.g. DGA data every 10 minutes, partial discharge monitoring in real time);
  2. data transmission: The acquisition layer sends the processed digital signals to the data center through the communication network;
  3. intelligent analysis (religion): The platform compares real-time data with standard thresholds (e.g., DL/T 722-2014 Guidelines for Analysis and Judgment of Dissolved Gases in Transformer Oil), and determines the status in conjunction with historical trends (e.g., growth rate of gas concentration);
    • Example: If the acetylene concentration is >5 μL/L and continues to rise, an arc discharge fault may exist;
  4. Early warning and decision-makingWhen the parameters are abnormal, the system triggers alarms (sound and light, SMS, APP push) and gives diagnostic suggestions (e.g., "Suggest shutdown to overhaul casing") to assist O&M personnel in decision-making.

IV. Core functions

  1. Real-time monitoring and visualizationReal-time display of parameters (e.g. oil temperature curve, gas concentration pie chart) through the monitoring interface, supporting centralized monitoring of multiple devices;
  2. Fault warning and diagnosis: Early warning of abnormal parameters (e.g. "winding temperature over-temperature warning") and algorithms to locate the type of fault (e.g. "overheating fault" "insulation aging");
  3. Condition Assessment and Life Prediction: Evaluate equipment health based on long-term data (e.g., "Health Index 85/100") and predict remaining life (e.g., "Expected to operate safely for 5 years");
  4. Historical data traceability: Stores several years of monitoring data and supports the query of parameter changes during a certain period of time (e.g. "analysis of peak oil temperatures in the summer of 2024");
  5. Remote Operations Management: Operation and maintenance personnel can check the status remotely via cell phone or computer, eliminating the need for on-site inspections (especially applicable to remote substations).

V. Application Scenarios

  • electricity generating station: Monitoring of the main transformer (the core equipment connecting the generator to the grid);
  • (transformer) substation: High voltage transformers 110kV and above (e.g. 220kV, 500kV substations);
  • industrial enterprise: Large-scale plant transformers for steel, chemical and other industries (to ensure continuous operation of production lines);
  • rapid transit: High-speed rail traction substations, subway main transformers (to avoid shutdowns affecting travel).

VI. Advantages over traditional testing

comparison dimension Traditional offline testing (periodic power outages) Online monitoring systems
topicality Long intervals (e.g., once a year) make it difficult to detect unexpected failures in a timely manner. All-weather monitoring, real-time response to anomalies
Impact of power outages Requires power outage for testing, affecting power supply reliability No need for power outages, no disruption to normal operations
early warning of malfunction Relies on manual judgment with a high lag Automatic alerts to detect hazards weeks / months in advance
(manufacturing, production etc) costs Manual inspection + high cost of lost power outages High initial investment but low long-term maintenance costs

VII. Development trends

With the intelligent upgrade of the power system, the transformer online monitoring system is developing in the following directions:

 

  • AI Deep Fusion: Improve fault diagnosis accuracy (e.g., differentiate "partial discharges" from "interference signals") through machine learning (e.g., deep learning models);
  • IoT Integration: Linkage with the monitoring system of other equipment in the substation (e.g., circuit breakers, transformers) to realize the collaborative analysis of the status of the whole station;
  • Wireless Sensing Popularized: Reduced wiring and lower installation costs with passive sensors (e.g. energy harvesting technology);
  • digital twinThe virtual model of the transformer is constructed to simulate the operation status through real-time data, realizing the full life-cycle management of "virtual and real linkage".

 

Inotera transformer online monitoring system is a key technology to ensure the safe and stable operation of the power system, and its application can significantly reduce the probability of transformer failure, and provide important support for the construction of a "strong smart grid".
Tags::