On May 23, it was reported that the "Online Monitoring and Diagnostic Technology for Mechanical Defects in Power Transformers" innovatively invented by a research team at the Hebei Electric Power Research Institute has recently won a gold award at the 49th Geneva International Exhibition of Inventions. This achievement has opened up a new technological path for monitoring and diagnosing mechanical defects in domestic transformers, effectively overcoming the shortcomings of traditional electrical parameter detection methods such as difficulty in direct measurement of vibration sources and susceptibility to environmental interference. It can increase the accuracy of mechanical defect diagnosis to over 80%, essentially adding a highly sensitive "stethoscope" to power transformers.
Power transformers are pivotal equipment in the power grid. The mechanical vibrations generated by high voltage, large currents, and even sudden short circuits often cause deformation in key components such as windings and iron cores. Statistics show that mechanical vibration-induced transformer failures account for over 60% of total failures, and traditional detection technologies primarily based on electrical parameters struggle to accurately identify and locate such defects. Precisely identifying defects and evaluating vibrations have become urgent challenges in the power industry.
In the quest for more precise and effective defect detection methods, researchers at the Hebei Electric Power Research Institute have departed from traditional electrical parameter detection methods. Starting from the collected transformer vibration signals, they conducted numerous fault simulation experiments and data analyses to deeply reveal the vibration modes and response characteristics of key transformer components under mechanical instability conditions. Based on this, they developed an online monitoring system for mechanical defects in transformers.
The system can monitor transformer vibrations in real-time, capturing vibrations generated by short-circuit current impacts as well as minor vibrations during normal transformer operation. By processing and analyzing this vibration data, the system can quickly identify and locate mechanical defects inside the transformer, akin to giving the transformer a "check-up" to promptly understand its internal operating condition. Through processing vast vibration data under different conditions and analyzing the steady-state deformation vibration modes and transient acoustic vibration time-frequency characteristics, researchers have established criteria for evaluating the mechanical vibration status of transformers. Based on these criteria, the research team successfully quantitatively assessed the cumulative effects of winding damage in transformers under short-circuit impacts.
Currently, this system has been deployed in transformer substations ranging from 220 kV to 1000 kV in the southern part of Hebei's power grid. It has identified and accurately located over 10 mechanical defects in transformers, significantly enhancing the safety and reliability of power grid equipment operation.
