Circuit breaker fault identification method based on time-domain statistical characteristics of closing coil current

Abstract

The invention discloses a circuit breaker fault identification method based on time-domain statistical characteristics of the closing coil current, which collects the current data of the closing coil during the closing operation of the circuit breaker, and extracts its feature points; calculates the characteristic points of the signal to be identified and the known The Euclidean distance between the feature points of the category signals, and find out the multiple signals closest to the feature points of the signal to be identified, and judge the state of the circuit breaker where the signal to be identified is located according to the circuit breaker status of these signals, so as to realize the circuit breaker. fault identification. The invention can diagnose the common mechanical faults of the operating mechanism of the circuit breaker, and accurately locate the faulty components and faulty parts when the fault occurs, thereby helping maintenance personnel to handle faults, improving maintenance efficiency, and ensuring safe and reliable operation of the circuit breaker.

Description

technical field [0001] The invention relates to the technical field of circuit breaker fault diagnosis, in particular to a circuit breaker fault identification method based on time-domain statistical characteristics of closing coil current. Background technique [0002] Circuit breakers undertake key tasks of control and protection in power systems. If the circuit breaker fails to open and close in time, it will inevitably expand the fault range in the power system and seriously threaten the safe and reliable operation of the power system. According to the survey, most of the faults of circuit breakers are mechanical faults of its operating mechanism; in addition, the structure of the circuit breaker operating mechanism is complex. When a mechanical fault occurs, it is often difficult to determine the fault location or faulty component, which increases the difficulty of maintenance work. . [0003] The closing coil is one of the key components in the operating mechanism of...

AI Technical Summary

Problems solved by technology

At present, for the selection of the current feature points of the opening and closing coils, the essential characteristics of the different distributions of the coil current feature points in the time domain under different states are not considered, and the selected feature points cannot fully reflect the change of the coil current; Most of the methods for diagnosing the gate coil current use artificial intelligence algorithms such as support vector machines and neural networks. The training speed is slow, and problems such as overfitting and local optimum are prone to occur, which affects the accuracy of fault diagnosis. Therefore, it is necessary to introduce a feature A fault diagnosis method for open circuit with more reasonable point selection and more accurate and reliable fault identification

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