Dissolved gas monitoring in oil (DGA) It is the core technology of transformer internal fault early warning, and the mainstream technology path includes chromatography, electrochemical method and photoacoustic spectroscopy. Chromatography realizes the concentration detection through precise separation of gas in oil, electrochemical method directly obtains the gas content with the help of special sensors, and photoacoustic spectrometry does not require carrier gas and consumables, and can realizeSimultaneous rapid multi-gas detectionThe system is also capable of analyzing the changes in the composition and concentration of characteristic gases such as hydrogen, methane, and acetylene. By analyzing the composition and concentration changes of characteristic gases such as hydrogen, methane, acetylene, etc., it can accurately determine the types of faults such as internal overheating and partial discharges, and part of the system can also predict the remaining life of the equipment in combination with AI algorithms, providing data support for the transformation of operation and maintenance modes from "regular overhaul" to "on-demand overhaul". Providing data support for the transformation of the operation and maintenance mode from "regular maintenance" to "maintenance on demand". This technology is applicable to transformers of all voltage levels, and is a "necessity" for ensuring safety, especially on key equipment such as main transformers in key substations.

Partial Discharge Monitoring Focusing on capturing the discharge signals generated by insulation defects inside the transformer, the core relies on the ultrasonic method and the ultra-high frequency (UHF) method. Ultrasonic method receives the mechanical vibration signal generated by the discharge, combined with the propagation time difference to realize the discharge point localization; UHF method receives the electromagnetic signal in the discharge process, because the interference in the field is concentrated in the low-frequency band, and itsParticularly good anti-interference capabilityThe system can be used to locate the target within 10 centimeters of the target. Through the combined acoustic and electric detection, the positioning accuracy can be controlled within 10 centimeters, and it can also automatically search for the detection frequency with the best signal-to-noise ratio, avoiding the influence of communication interference. This technology is of great significance for high-voltage transformers of 220kV and above, especially for assessing the insulation status of newly commissioned equipment, which can detect latent insulation deterioration problems in time.

Winding temperature monitoring Mainly throughFluorescent fiber optic sensingand infrared sensing to achieve, of which fluorescent fiber optic sensing is the current core choice for high-precision monitoring. Fluorescent fiber sensing is based on the temperature characteristics of fluorescent materials, through the measurement of fluorescence decay time to calculate the temperature, can be directly implanted into the winding to monitor the hot spot temperature, the measurement accuracy is as high as ± 1 ℃; at the same time, the fiber itself is non-conductive, no metal components, can be in theSafe operation in strong electromagnetic and high voltage environmentsIt also supports multi-channel simultaneous measurements to obtain information on the temperature distribution of the windings. Infrared sensing can indirectly estimate the winding temperature by monitoring the surface temperature of the oil tank, which is easy to operate without complicated installation procedures. Both technologies are of great value in heavy-duty transformers and in scenarios where precise load control is required to ensure that equipment loads are in a safe range through real-time temperature data.

Oil condition monitoring Directly reflecting the insulation performance of the transformer oil and the degree of deterioration, the core through the dielectric loss sensor, moisture sensor to achieve key parameters detection. The dielectric loss sensor accurately measures the dielectric loss value of the oil, the moisture sensor captures the trace moisture content of the oil, and part of the system can be integrated with multiple modules to realize the simultaneous acquisition of a number of oil parameters in a single sample. At the same time, machine learning algorithms can be combined with the establishment of fault assessment model to further improve the diagnostic accuracy, and the entire monitoring process without complex maintenance, can capture the oil quality trend in real time. This technology is especially suitable for transformers with deteriorating oil quality in humid outdoor environments, and can provide early warning of the risk of insulation failures caused by oil quality problems.

Selection can be prioritized according to the needs of the priority configuration: if the budget is limited, the priority with theDGA monitoring + partial discharge monitoringThe combination of the two can cover most of the internal fault warning needs. For the renovation of old transformers, infrared temperature measurement, electrochemical DGA and other solutions that do not require power outages can be selected; for newly commissioned transformers, it is recommended to embed fluorescent fiber optic temperature measurement and UHF local discharge monitoring to achieve a more comprehensive state awareness. In the end, it is necessary to take into account the transformer voltage level, operating environment and load characteristics to ensure that the monitoring system accurately matches the actual needs.