Comprehensive online monitoring system for transformer insulation: from single parameter to multi-dimensional diagnosis

Power transformer is one of the most valuable and critical equipment in the power grid. The health state of its internal insulation system directly determines the operating life of the transformer and the reliability of the power supply of the grid. Transformer insulation integrated online monitoring system is an advanced condition assessment technology, which through the integration of a variety of sensing technologies, the key parameters affecting the insulation performance of continuous, real-time monitoring and analysis, realizing the fundamental change from "passive maintenance" to "active predictive maintenance". It realizes the fundamental change from "passive maintenance" to "active predictive maintenance".

Part I: Complexity and Failure Modes of Transformer Insulation Systems

The insulation system of a transformer is an organic composite system consisting of insulating oil, cellulose insulating paper/board and high voltage bushings. Its performance is subject to the combined effects of electrical, thermal, chemical and mechanical stresses and gradual deterioration.

Primary failure mode:

• Thermal aging: Prolonged operation at high temperatures leads to a decrease in the degree of polymerization (DP) of the insulating paper and a reduction in mechanical strength.

• Electrical aging: Under the action of strong electric fields, partial discharges (PD) occur within the insulating medium, gradually destroying the insulating structure.

• Chemical aging: Moisture, oxygen and high temperature work together to catalyze the oxidation and hydrolysis of insulating oil and insulating paper.

• Mechanical damage: Short-circuit current shocks, vibration, etc. lead to deformation of windings and insulation displacement.

A single monitoring parameter often reflects only one aspect of the insulation condition. Therefore, a multi-parameter, multi-dimensional integrated monitoring strategy must be used to make an accurate assessment of the overall health of the transformer.

Part II: Core monitoring technologies and key parameters

A comprehensive transformer insulation monitoring system typically integrates several core technologies:

1. On-line analysis of dissolved gases in oil (Online DGA)

• Diagnostic significance: It is known as the "blood test" of the transformer. When an overheating or discharge fault occurs inside a transformer, the insulating oil and paper will decompose to produce specific types of gases. By analyzing the components and generation rate of these gases, the type and severity of the fault can be accurately determined.

• Key monitoring gases:

  • Hydrogen (H₂). The signature gas for localized discharges.

  • Methane (CH₄), ethane (C₂H₆), ethylene (C₂H₄). Corresponding to low, medium and high temperature overheating faults respectively.

  • Acetylene (C₂H₂). The only gas that characterizes a high-energy arc discharge is the most dangerous signal.

  • Carbon monoxide (CO), carbon dioxide (CO₂). It mainly reflects the aging and overheating of solid insulation (insulation paper).

2. On-line monitoring of microwater in oil (Online Moisture Monitoring)

• Diagnostic significance: Moisture is the primary catalyst for accelerated insulation deterioration and significantly reduces insulation strength. Online monitoring tracks the dynamic balance of moisture within the insulation system in real time.

• Key monitoring parameters:

  • Water activity (aw): Directly reflecting the degree of moisture in the solid insulation, it is a more reliable assessment than the absolute moisture content (ppm). High aw values (e.g. > 0.4) indicate a serious risk of "bubble effect".

  • Absolute moisture content (ppm): Aids in determining the state of the oil, but its value is drastically affected by temperature.

3. On-line monitoring of partial discharges (Online Partial Discharge Monitoring)

• Diagnostic significance: It is considered as an early detection of "cancer cells" within the insulation system. Partial discharges are one of the main causes of eventual breakdown of insulation. On-line monitoring is able to capture discharge signals at an early stage when they are still weak.

• Key monitoring techniques:

  • Ultra High Frequency (UHF) method: UHF antenna sensors are installed on the transformer tank to receive the electromagnetic wave signals generated by the discharge. It has high sensitivity, strong resistance to external interference and can localize the discharge source.

  • Acoustic: An acoustic sensor is mounted on the wall of the box to receive the ultrasonic signal generated by the discharge. It is mainly used for the precise positioning of the discharge source.

  • High frequency current method (HFCT): High-frequency current transformers are installed in high-voltage bushings, neutrals or grounding wires to detect the impulse currents generated by the discharge.

4. On-line monitoring of high-voltage casing (Online Bushing Monitoring)

• Diagnostic significance: The high-voltage bushing is the most vulnerable external insulation component of a transformer and one of the components with a high failure rate. Damage to its insulation is often sudden.

• Key monitoring parameters:

  • Dielectric loss factor (tanδ): Very sensitive to moisture, dirt and internal defects in the insulation. significant increase in tanδ is an important indication of insulation deterioration.

  • Capacitance (C1): The change in capacitance reflects the presence or absence of serious defects in the main insulation of the casing such as turn-to-turn short circuits.

  • Leakage current analysis: By analyzing the leakage current flowing to ground at the end screen of the casing, tanδ and capacitance can be calculated.

Part III: System Architecture and Data Fusion Diagnostics

A modern insulation monitoring system is not just a simple stack of sensors, but a layered, intelligent system.

• Perception Layer: It consists of a variety of sensors such as DGA, micro water, local discharge and casing.

• Acquisition and transmission layer: Various types of data acquisition units gather data to the local monitoring host via fiber optics or industrial bus.

• Analysis and Diagnostic Layer: This is the "brain" of the system. The back-end expert system or AI diagnostic platform carries out the diagnosis of data from different sensors.Data fusion analysis. For example, the simultaneous detection of H₂ and UHF signals provides a high degree of confirmation of the presence of a partial discharge; the simultaneous presence of C₂H₂ and intense UHF signals would indicate that the fault has progressed to a dangerous arc discharge. This cross-validation capability is unmatched by a single monitoring technique.

Part IV: Overview of the technical specifications of the integrated monitoring system

Part V: Frequently Asked Questions (FAQ)

Q1: Why use an integrated monitoring system rather than just installing a DGA or microwater monitoring?
Answer: The mechanism of transformer insulation faults is complex and different fault types are sensitive to different monitoring parameters. For example, DGA is very sensitive to slow-developing thermal faults, but may provide insufficient warning of sudden casing breakdown. Localized discharge monitoring, on the other hand, can detect tiny insulation air gap discharges that are difficult for DGA to detect. Only by combining information from multiple dimensions can a complete diagnostic chain be formed, realizing comprehensive coverage of various types of faults and avoiding "blind men feeling the elephant".

Q2: What is the return on investment (ROI) of this system?
Answer: This is reflected in three main areas:

1. Avoid catastrophic accidents: The direct and indirect losses avoided by successfully warning of a serious fault that could have resulted in a burnt transformer and widespread power outage would have covered the cost of the system.

2. Optimize the O&M strategy: Moving from time-based planned maintenance to predictive maintenance based on actual conditions reduces unnecessary outages and maintenance overhead.

3. Extending the life of assets: By identifying and dealing with problems such as excessive moisture and localized overheating early on, the rate of insulation deterioration can be effectively slowed down, thus extending the actual service life of the transformer as an expensive asset.

Q3: What role does Artificial Intelligence (AI) play in the IMS?
Answer: AI is the key to improving diagnostic accuracy. Traditional monitoring systems rely on fixed alarm thresholds, which are prone to false or missed alarms.AI diagnostic model can build complex multi-parameter correlation models by learning from massive historical monitoring data and fault cases. It can identify combinations of weak fault signs that are not easily detected by a single parameter, predict fault development trends, and even give intelligent operation and maintenance suggestions, greatly enhancing the intelligence of the system.

Q4. Is the installation of a complete integrated monitoring system complex and does it require long power outages?
Answer: Not required. Modern on-line monitoring systems are designed with ease of installation in mind. All sensors, including DGA (via bypass oil circuit), micro water, local discharge and bushing monitoring units, can be installed during normal transformer operation. The whole process is usually completed within 1-2 days with essentially no impact on grid operation.