High-voltage circuit breaker fault diagnosis method based on unsupervised learning model

Abstract

The invention discloses a high-voltage circuit breaker fault diagnosis method based on an unsupervised learning model. The high-voltage circuit breaker fault diagnosis method comprises the following steps: obtaining a coil current waveform in a switching-off and switching-on process of the high-voltage circuit breaker, and obtaining the state parameter of the high-voltage circuit breaker according to the waveform; sampling a vibration signal of the high-voltage circuit breaker, and selecting the time node t of the high-voltage circuit breaker; classifying and numbering the faults of high-voltage circuit breaker as the output of a fault model identification system, and taking coil current, the vibration signal and the time node as the input of the fault model identification system; taking a denoising self-decoding algorithm as a typical unsupervised learning model, training the model, adopting a SVM (Support Vector Machine) structure, , and obtaining a regression expression of a circuit breaker fault through a loss function obtained by the denoising self-decoding algorithm; and according to the regression expression of the circuit breaker fault, obtaining a corresponding relationship between coil current data and a fault type when a fault happens, and judging the fault type through fault data to be determined. The training optimization process of the high-voltage circuit breaker fault diagnosis method can avoid a local optimal solution.

Description

technical field [0001] The invention relates to the field of high-voltage circuit breaker fault diagnosis, in particular to a high-voltage circuit breaker fault diagnosis method based on an unsupervised learning model. Background technique [0002] The safety of power transmission and transformation equipment is the basis for the safe operation of the power grid. The comprehensive and accurate evaluation, diagnosis and prediction of equipment status is the prerequisite for the condition maintenance and life cycle management of power transmission and transformation equipment, and it is the key to improving the reliability of power supply and the power grid. It is an important way to operate the intelligent level and also an important basis for intelligent dispatching operation, which can provide strong technical support for the safe, reliable and economical operation of the power grid. [0003] The initial research on high-voltage equipment status and abnormal status monitori...

AI Technical Summary

Problems solved by technology

First of all, the circuit breaker has a very complex mechanical structure, and the environmental factors on site have non-negligible noise interference, so that the vibration signal monitored by the sensor has a large natural error.

In fact, a certain number of vibration processes will be generated to varying degrees during the opening and closing operations of the circuit breaker, and the complex changes in the physical parameters of the vibration signal and other parameters in the actual operation, including the dispersion, reflection and refraction of the vibration signal, make the monitoring work difficult. become more difficult, sometimes even more difficult to monitor when the vibration signal is negligible

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