Partial discharge detection in dry-type transformers is a crucial method for assessing their insulation condition and preventing insulation faults. Accurate location of insulation defects is essential for ensuring safe equipment operation. Currently, multi-technology integrated detection solutions have become mainstream. By comprehensively utilizing electromagnetic signal analysis, acoustic localization, high-frequency current detection, and intelligent diagnostic algorithms, the accuracy and reliability of defect localization can be significantly improved.
Electromagnetic signal analysis technology, with ultra-high frequency (UHF) as its core, achieves rapid defect localization by detecting electromagnetic waves in the 300MHz to 3GHz frequency band generated by partial discharge. This technology utilizes UHF sensors to capture high-frequency pulse signals during discharge. Its high-frequency characteristics allow it to effectively avoid low-frequency corona interference, improving the signal-to-noise ratio. Sensors are typically placed on the inner wall of the transformer tank or built into the high-voltage bushing. By analyzing the time difference of electromagnetic waves arriving at different sensors and combining this with signal intensity distribution, the three-dimensional spatial coordinates of the discharge source can be constructed. The UHF method is sensitive to defects such as internal air gap discharge and surface discharge, and is particularly suitable for defect localization in epoxy resin cast dry-type transformers. Its anti-interference capability and localization accuracy are outstanding among various detection technologies.
Acoustic localization technology, represented by ultrasonic methods, achieves precise defect location by detecting sound wave signals from 20kHz to 300kHz generated by partial discharge. During the discharge process, the pressure waves generated by the thermal expansion of the medium propagate in the form of spherical waves. Ultrasonic sensors, attached to the transformer casing, can capture these weak sound waves. Since the propagation speed of sound waves in solids is much lower than that of electromagnetic waves, the distance between the discharge source and the sensor can be calculated by analyzing the time difference of the sound waves reaching different sensors, thus determining the defect location through a multi-point localization method. Ultrasonic methods are highly effective in locating defects such as surface discharges on insulation and floating potential discharges in metal components. Their non-invasive detection characteristics make them suitable for online monitoring scenarios.
High-frequency current detection technology indirectly locates defects by capturing pulse current signals generated by partial discharge using high-frequency current transformers (HFCTs). When a discharge occurs inside the transformer, the pulse current propagates along the grounding wire or the iron core grounding loop. HFCTs, attached to these paths, can detect nanosecond-level pulse currents. By analyzing the amplitude, frequency, and phase distribution of the pulse signal, the type and severity of the discharge can be determined. By combining the propagation time difference between electromagnetic waves and sound waves, the defect range can be further narrowed down. High-frequency current detection technology is simple to operate and low in cost, and is often used as a supplement to ultra-high frequency (UHF) and ultrasonic methods for preliminary screening and auxiliary location.
Multi-technology fusion detection schemes integrate multiple detection methods such as UHF, ultrasonic, and high-frequency current to achieve complementary verification of defect location. For example, UHF can quickly locate the approximate area of internal insulation discharge, ultrasonic can accurately determine the spatial coordinates of the discharge point, and high-frequency current provides a quantitative assessment of the discharge type and severity. By simultaneously acquiring multiple types of signals and combining them with intelligent diagnostic algorithms for signal feature fusion analysis, the accuracy and reliability of defect location can be significantly improved. Multi-technology fusion schemes are particularly suitable for dry-type transformers with complex insulation structures, such as multi-layer epoxy resin casting structures or hybrid insulation systems, and their comprehensive detection capabilities can cover various potential defects.
The application of intelligent diagnostic algorithms further enhances the intelligence level of defect location. Machine learning-based diagnostic models, trained with a large amount of field data, can automatically identify the signal characteristics of different types of discharges and predict defect location and risk level. For example, the random forest algorithm can extract and classify features from multi-sensor signals, and combined with a historical fault database, achieve intelligent matching and location of defects. Deep learning algorithms, by constructing convolutional neural networks, deeply mine the time-frequency features of discharge signals, improving the ability to identify weak discharge signals. The application of intelligent diagnostic algorithms transforms defect location from "manual analysis" to "automatic diagnosis," significantly improving detection efficiency and accuracy.
The deployment of online monitoring systems enables real-time and dynamic defect location. By pre-installing ultra-high frequency sensors, ultrasonic sensors, and high-frequency current transformers inside the dry-type transformer, combined with an edge computing module, real-time signal acquisition and processing are achieved, continuously monitoring the changing trends of insulation status. When an abnormal discharge signal is detected, the system automatically triggers an alarm mechanism and locates the defect location through multi-technology fusion analysis, providing maintenance personnel with precise repair guidance. Online monitoring systems are particularly suitable for dry-type transformers in important power-consuming locations, such as data centers and hospitals, where their real-time monitoring capabilities can effectively prevent unplanned power outages.
Partial discharge detection in dry-type transformers achieves precise location of insulation defects through the integration of multiple technologies, intelligent diagnostic algorithms, and online monitoring systems. Complementary verification of ultra-high frequency (UHF), ultrasonic, and high-frequency current detection technologies, combined with intelligent analysis using machine learning and deep learning algorithms, and real-time dynamic tracking by the online monitoring system, collectively constructs a defect location system covering the entire process of "detection-location-assessment-early warning." This system not only improves the reliability of insulation detection in dry-type transformers but also provides crucial technical support for condition-based maintenance and intelligent operation and maintenance of power equipment.
