High-voltage circuit-breaker mechanical fault diagnosis method based on atomic sparse decomposition

Abstract

The invention provides a high-voltage circuit-breaker mechanical fault diagnosis method based on atomic sparse decomposition. The method comprises the following steps of using a high-voltage circuit-breaker test to simulate mechanical faults of different types; collecting vibration signals, and carrying out atomic sparse decomposition on the vibration signals to obtain an attenuation modal parameter; carrying out data preprocessing on the attenuation modal parameter to obtain characteristic vectors under different mechanical fault types; dividing the characteristic vectors into a training sample and a test sample; taking the training sample as input, and the mechanical fault types as output, establishing a high-voltage circuit-breaker RBF network mechanical fault diagnosis model; trainingthe fault diagnosis model to acquire the trained high-voltage circuit-breaker RBF network mechanical fault diagnosis model based on the atomic sparse decomposition; and inputting the test sample intothe trained fault diagnosis model to judge the mechanical faults, and outputting a diagnosis result. The method has characteristics of high reliability, good accuracy and the like and can be widely applied to the fault diagnosis field.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a method for diagnosing mechanical faults of high-voltage circuit breakers based on atomic sparse decomposition. Background technique [0002] As a switchgear for protection and isolation in the power system, the high-voltage circuit breaker is responsible for the dual tasks of control and protection. The status and reliability of the high-voltage circuit breaker are directly related to the safe and stable operation of the power system. In the operation of high-voltage circuit breakers, mechanical failures account for more than 70% of the total failures of high-voltage circuit breakers. Therefore, it is of great significance to strengthen the research on the mechanical fault diagnosis method of high-voltage circuit breakers and find potential faults as early as possible to improve the reliability of high-voltage circuit breakers and enhance the safety, reliability and eco...

AI Technical Summary

Problems solved by technology

The neural network has a simple structure and strong problem-solving ability, and can handle noisy data well, but the algorithm has local optimal problems, poor convergence, and limited reliability

[0003] It can be seen that in the prior art, there are problems such as low precision, poor reliability, and large deviations in diagnostic results in the high-voltage circuit breaker mechanical fault diagnosis method.

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