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

Abstract

The present invention discloses a circuit breaker fault identification method based on closing coil current time domain statistical characteristics. The method collects current data of a closing coil during closing operation of a circuit breaker, and extracts characteristic points thereof; and calculates the Euclidean distance between a characteristic point of a signal to be identified and a known category signal characteristic point, finds a plurality of signals that are closest to the characteristic point of the signal to be identified, and according to state of the circuit breaker of the signals, determines the state of the circuit breaker where the signal to be identified is located, thereby realizing fault identification of the circuit breaker. The method can diagnose common mechanical faults of the circuit breaker operating mechanism, and accurately locate faulty components and faulty parts when fault occurs, thereby helping maintenance personnels to perform fault handling, improving maintenance efficiency, and can ensure 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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