Circuit breaker service life prediction method based on small sample learning

Abstract

The invention discloses a circuit breaker service life prediction method based on small sample learning, and belongs to the technical field of circuit breaker equipment. According to the circuit breaker service life prediction method based on small sample learning, a small sample learning method is utilized, the needed sample size can be generated according to a small amount of data, a prediction model can be established without a large number of fault diagnosis sample libraries, the prediction efficiency is high, and the application cost is low; and the health condition of the circuit breaker is analyzed and the service life of the circuit breaker is predicted according to the vibration waveform of the circuit breaker. According to the circuit breaker service life prediction method, a Markov chain prediction method is not adopted, complex probability calculation of each state is not needed, the prediction accuracy can be effectively improved, the actual prediction accuracy and prediction efficiency can meet the requirements of field application, and the method is simple, effective and feasible. Furthermore, the maintenance efficiency of the circuit breaker is improved, operation and maintenance personnel can master the operation state in time, and safety and reliability of power supply are guaranteed.

Description

technical field [0001] The invention relates to a method for predicting the service life of a circuit breaker based on small sample learning, and belongs to the technical field of circuit breaker equipment. Background technique [0002] Low-voltage circuit breakers are one of the most important components of the entire low-voltage distribution system. They are widely used in the transmission and distribution of electric energy, the control and protection of electrical equipment, but due to the design, manufacture, material quality and Due to many reasons such as operation, the malignant failure of equipment occurs from time to time, which seriously affects the safe and stable operation of the power grid. Therefore, it is very important to grasp the health status of the circuit breaker to ensure the safe operation of the power system. [0003] The complexity of the circuit breaker mechanism and various uncertain factors will cause its performance to degrade during use, result...

AI Technical Summary

Problems solved by technology

[0005] However, the above method requires more variables to be collected, and the Markov chain prediction method is used. All items are statistically independent, so the future state is independent of all past states, and at the same time, it is necessary to understand the various probabilities of each state change , if there is an inaccurate error in the calculation of a state, it will lead to an error in the entire prediction result; the matrix operation process is also very complicated, the diagnosis efficiency is low, and it is difficult to meet the requirements of field applications. Therefore, the above method still has its own limitations. The actual diagnosis Accuracy and diagnostic efficiency are low

Images