Method for discharge fault diagnosis of transformer

Abstract

The invention discloses a method for discharge fault diagnosis of a transformer. The method is characterized in that a local discharge parameter and a parameter of gas dissolved in oil which relate to online detection of the transformer are combined, the two parameters are organically combined through a neural network, and combined diagnosis is performed on the type of a discharge fault happening in the transformer through the detected local discharge parameter and the detected parameter of gas dissolved in oil. According to the method, the accuracy for the discharge fault diagnosis is improved, and limitation of analysis of a local discharge signal and limitation of analysis of the gas dissolved in oil in the fault diagnosis are overcome; when the artificial neural network is established, training on the neural network can be completed by doing a series of typical experiments, and the method is easy and convenient to achieve.

Description

Technical field [0001] The invention relates to a method for diagnosing the discharge fault of a transformer, in particular to a dual-parameter joint diagnosis method of a transformer based on a BP neural network. Background technique [0002] The power industry is a basic industry and public undertaking that supports the development of the national economy and society. The development of all industries is inseparable from a stable power supply. With the steady advancement of my country's economic development, the entire society is increasingly dependent on electricity. [0003] As the core high-voltage equipment of the substation, the reliability of power transformer operation is directly related to the safety and stability of the power system. According to statistics, half of the large transformer accidents of 110kV and above in my country are inter-turn insulation accidents, and they are all damaged during normal operation. The internal insulation of the transformer is mainly...

AI Technical Summary

Problems solved by technology

These independent on-line monitoring equipment developed by various manufacturers at home and abroad can monitor the parameters and operating status of the transformer in real time, but they cannot cooperate with each other, and because this type of technology has not yet developed very maturely, the monitoring data is still It can only provide some reference opinions, and the power grid staff cannot obtain the operation status and fault parameters of each power transformer very accurately anytime, anywhere, let alone self-diagnosis and self-fault recovery of power transformers

Images