Transformer State Sensing and Intelligent Diagnosis System

Date: October 31, 2025 16:30:50

What is transformer condition sensing and intelligent diagnosis system?

Transformer State Sensing and Intelligent Diagnosis Systemis a comprehensive online monitoring and asset management platform based on Internet of Things (IoT), big data analytics and artificial intelligence (AI) technologies. Its core objective is to replace the traditional preventive maintenance (TBM) model based on fixed cycles, and shift to the actual health condition of equipment based on theCondition Based Maintenance (CBM)respond in singingPredictive Maintenance (PdM). The system realizes the key operational parameters through a multi-dimensional sensor network deployed on the transformerAll-weather situational awarenessThe data collected is analyzed in depth by advanced diagnostic models, which ultimately outputs preciseEquipment Health Assessment,Early warning of faultsrespond in singingMaintenance of recommendations for decision-makingThe

It is not a simple collection of single monitoring devices, but a closed-loop intelligent system that integrates data acquisition, transmission, storage, analysis and visualization, and it is a way to realize the transformer and even the whole substation.Digital Transformationrespond in singingIntelligent Operation and MaintenanceThe core technical support of the

Table of Contents for this article

System architecture: from data to decision-making

A complete transformer condition sensing and intelligent diagnosis system usually follows a hierarchical distributed architecture to ensure reliable data collection and efficient processing.

  1. Perception Layer: This is the "sense" of the system. It consists of a variety of sensors mounted on the transformer body and accessories.High Performance SensorsIt is composed of the chemical, electrical, and physical information reflecting the state of the transformer and is responsible for converting it into digital signals.
  2. Network Layer:: It is the "neural network" of the system. Massive amounts of data collected at the sensing layer are securely and reliably transmitted to the data center via optical fiber, power line carrier (PLC) or industrial wireless networks (e.g., 4G/5G).
  3. Platform Layer:: It is the "brain center" of the system. Deployed in the cloud or on local servers, it includesBig Data Storage System,real time computing enginerespond in singingIntelligent Diagnostic Algorithm Library, responsible for cleansing, processing, analyzing and mining data.
  4. Application Layer:: This is the "human-computer interface" of the system. Through the web, mobile app and other means, it provides operation and maintenance personnel, management personnel with visualizedTransformer health statusReal-time alarms, historical trends, diagnostic reports and maintenance work order recommendations.

Core technology: multi-dimensional state awareness layer

The effectiveness of the system depends first and foremost on the comprehensiveness and accuracy of the sensory layer data. It must cover all critical fault characterization parameters.

Monitoring of chemical quantities

Dissolved Gas Analysis (DGA) in Oil

This is the most central means of diagnosing latent faults within a transformer. By means ofOnline monitoring of oil spectraThe system monitors the concentration of 9 key gases including H₂, CH₄, C₂H₆, C₂H₄, C₂H₂, CO, CO₂, and other gases in real time by using the optical-acoustic spectroscopy technology, which can be used to determine whether there is any internal overheating or discharge faults.

Microwater and oil quality monitoring

pass (a bill or inspection etc)On-line monitoring of microwaterSensors to monitor the moisture content (ppm) in the insulating oil in real time, preventing degradation of insulation properties and accelerated aging of the insulating paper. Some advanced systems also integrate monitoring of oil quality parameters such as oil dielectric constant and acid value.

Electrical quantity monitoring

Partial Discharge (PD) Monitoring

pass (a bill or inspection etc)High Frequency Current Transformer (HFCT)maybeUltra High Frequency (UHF) SensorsOn-line monitoring of localized discharge signals from transformer bushings, windings and internals is the most direct means of detecting budding insulation defects.

Core ground current monitoring

Continuous monitoringCore ground currentIt is an effective early warning of potential core failures due to multi-point grounding, poor insulation of penetrating screws, etc.

Casing online monitoring

By monitoring the casing'sDielectric loss factor (tanδ)The company is also able to assess the insulation status of the casing in real time, in terms of capacitance and leakage current, in order to prevent vicious flashovers or explosions.

Physical and mechanical monitoring

Temperature monitoring

adoptionFluorescent fiber optic temperature measurementtechnology to measure winding hot spot temperatures directly and in real time. At the same time, the temperature of the winding hotspots is measured through theinfrared thermographyMonitoring of the temperature distribution at external connection points such as bushings and tap changers.

Noise and vibration monitoring

pass (a bill or inspection etc)Acoustic sensorsrespond in singingAcceleration sensorsThe system monitors the noise and vibration characteristics of the transformer online, and is used to diagnose mechanical and structural defects such as loose cores and deformed windings.

Oil level and pressure monitoring

pass (a bill or inspection etc)Multifunctional integrated sensorsThe oil level of the storage cabinet and the internal pressure of the tank are monitored in real time to determine whether there is any oil leakage or poor sealing.

Core Technology: Intelligent Diagnostic and Predictive Layer

If the perception layer solves the problem of "what to see", the diagnostic layer solves the problem of "what it means" and "what is going to happen".

Single Parameter Trend Analysis and Threshold Alarms

This is the most basic diagnostic function. The system analyzes long-term trends for each monitored parameter and sets multi-level alarm thresholds (e.g., warning, alarm, hazard) based on international standards (e.g., IEC, IEEE) and historical equipment data.

Multi-information fusion diagnostic model

This is where the core intelligence of the system lies. While single covariates often suffer from diagnostic ambiguity, fusion diagnostics can dramatically improve accuracy. Example:

  • harmonize withDGA datatogether withPartial discharge data: A clear distinction can be made between high-energy arc discharges and low-energy partial discharges.
  • harmonize withWinding hot spot temperaturetogether withLoad Current: Accurate thermal modeling is possible to evaluate the dynamic load capacity of the transformer.
  • harmonize withnoise spectrumtogether withDGA data: It is possible to determine whether a loose core is a purely mechanical problem or an overheating problem accompanied by gas production.

The system has a built-inDuval's Triangle (math.),trinomial method (math.)A variety of standard diagnostic algorithms such as these are available, and automated correlation analysis can be performed.

Failure Prediction and Health Index (HI)

on the basis ofmachine learningrespond in singingartificial intelligence algorithm, the system learns the normal operating mode of the transformer and predicts the probability of future failures. Eventually, the system weights all the monitoring and diagnostic results and outputs a quantizedHealth Index (HI)Scoring (e.g., 0-100 points) gives managers an at-a-glance view of equipment status and provides an objective basis for decision sequencing for asset replacement or overhaul.

System core values and applications

  1. Improved operational reliability: Significantly reduce the probability of sudden transformer failures through effective early warning of faults, reducing unplanned downtime and directly improving supply reliability indicators (SAIDI/SAIFI).
  2. Optimize O&M costs: Shifting from time-based planned maintenance to condition-based precision maintenance avoids unnecessary overhaul inputs and reduces the high costs associated with accidental emergency repairs.
  3. Extending the life of assets:: Ensure that transformers are operated in the best possible condition through refined management (e.g., controlling overloads, optimizing cooling) to slow down the aging process and maximize their effective service life.
  4. Enhanced O&M Security:: Serious defects were detected and addressed in a timely manner, avoiding safety risks to personnel and the environment that could have resulted from sudden equipment failures.
  5. Enabling data-driven asset management:: Create complete digital "health profiles" for transformers, enabling objective and quantifiable data to support asset assessment, risk ranking and overhaul/replacement decisions.

Why choose Inotera's solutions?

INNOTD (Fuzhou) Sales Limited (INNOTD) Dedicated to providing end-to-end transformer intelligence solutions for the power industry.

  • Comprehensive perceptual layer coverage: We offer a wide range of services includingOil Spectrum DGA,UHF local amplifier,Fluorescent fiber optic temperature measurement,Intelligent Maintenance-free Moisture AbsorberA full range of high-performance sensors, including the following, ensure that the state of the transformer is sensed without any dead space.
  • Advanced Intelligent Diagnostic Platform: Our system platform not only integrates all standard diagnostic algorithms, but also carries the self-developedMulti-information fusion diagnostic enginerespond in singingHealth Index (HI) Assessment Modelthat can provide deep insights beyond a single device.
  • Excellent system integration capabilities: We provide a unified, open platform, not a patchwork of independent systems. The system supports a wide range of standard communication protocols and can be easily integrated with your existing SCADA or asset management system.
  • Deep experience as an industry expert: Our team is not only an equipment supplier, but also your diagnostic consultant. We provide services from solution design, installation and implementation to ongoing data analysis and diagnostic reports to ensure that you can maximize the value of your system.

Choosing Inotera is choosing a complete, intelligent and reliable ecosystem for transformer condition sensing and diagnosis.

The content of this article is only a general technical science and does not represent the performance and specifications of any specific product of our company. For detailed product information, solutions and quotations, please be sure to contact us for...].

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