The Hebei Electric Power Research Institute recently announced that the "Online Monitoring and Diagnosis Technology for Mechanical Defects in Power Transformers" developed by their research team has won a gold medal at the 49th Geneva International Exhibition of Inventions. This innovation paves a new technological path for diagnosing mechanical defects in Chinese transformers, effectively overcoming the limitations of traditional electrical parameter detection methods, such as the difficulty of direct vibration source measurement and susceptibility to environmental interference. The technology can improve the accuracy of mechanical defect diagnosis to 80%, equipping power transformers with a highly sensitive "stethoscope."
Power transformers are pivotal components of the power grid. The mechanical vibrations generated under high voltage, high current, or sudden short-circuit conditions often cause deformation of key components like windings and cores. Statistics show that mechanical vibrations account for up to 60% of transformer failures. Traditional detection technologies that rely primarily on electrical parameters struggle to accurately identify and locate such defects. Therefore, accurately identifying defects and assessing vibrations has become an urgent problem in the power industry.
To find more precise and effective defect detection methods, researchers at the Hebei Electric Power Research Institute shifted from traditional electrical parameter detection to analyzing vibration signals collected from transformers. Through extensive fault simulation experiments and data analysis, they deeply revealed the vibration modes and response characteristics of critical transformer components under mechanical instability. Based on this, they developed an online monitoring system for transformer mechanical defects.
This system can monitor transformer vibrations in real-time, capturing both vibrations caused by short-circuit current impacts and minor vibrations during normal operation. By processing and analyzing these vibration data, the system can quickly identify and locate mechanical defects inside the transformer, akin to performing a "physical examination" of the transformer, enabling timely insights into its internal operational status. By handling vast amounts of vibration data under different conditions and analyzing the steady-state deformation modes and transient acoustic-vibration time-frequency characteristics, researchers established criteria for assessing the mechanical vibration state of transformers. Based on these criteria, the research team successfully achieved quantitative evaluation of the cumulative effects of winding damage under short-circuit impact.
Currently, this system is being applied in substations with voltage levels ranging from 220kV to 1000kV in the southern Hebei power grid. It has successfully identified and accurately located over ten instances of mechanical defects in transformers, significantly enhancing the safety and reliability of power grid operations.
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