Method for identifying insulation fault of gas insulation combined electrical appliance based on photoacoustic spectral information

Abstract

The invention belongs to the field of on-line monitoring and fault diagnosis of gas insulation combined electrical appliances, and specifically relates to a method for identifying insulation fault of a gas insulation combined electrical appliance based on photoacoustic spectral information. The method includes the steps of constructing an original set of fault features based on the photoacoustic spectrum detection component information, optimizing fault features based on the maximum correlation and minimum redundancy criterion, and identifying the type of insulation fault based on a support vector machine. The method improves the fault diagnosis theory for SF6 gas insulation electrical equipment based on the photoacoustic spectrum information, and lays foundation for promotion and application of photoacoustic spectrum technology in the field of gas insulation fault diagnosis and status assessment.

Description

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AI Technical Summary

Benefits of technology

This patented describes an improved way to identify insulating defects (fault) in electric devices that have been installed at different locations or times during their lifespan. By utilizing photosynthetic acoustoic spectra from lightweight materials like sulfur hexafluoride, this technique helps improve understanding of how these products behave over time without being affected by external factors such as temperature changes or humidity levels. Additionally, it includes various techniques used to detect and locate leaks within the device's enclosure, including laser Doppler vibration analysis and spectral imaging methods. Overall, this new approach simplifies the detection process while maintaining its effectiveness across multiple conditions.

Problems solved by technology

Technological Problem: Current Fault Diagnosis Methods For Power Transmission And Conversion Equipment Involve manual testing with various types of gases like sulfur hexafluorsulfide or nitrogen oxides that require specialized training environments and expensive instrumentation devices. These techniques involve analyzing specific parts of the device's gas content through spectral measurements obtained via laser acoustoelectric scattering technique called PSA, but they lack accuracy due to factors including background noise interference, temperature dependency, etc., making them challenged when applied to detect partial discharge events within the device itself.

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