Medium-voltage circuit breaker fault diagnosis method based on deep learning and intelligent optimization

Abstract

The invention discloses a medium-voltage circuit breaker fault diagnosis method based on deep learning and intelligent optimization. The method comprises the following steps: 1) collecting medium-voltage circuit breaker vibration signals in a normal state, tripping closing electromagnet blockage, main shaft blockage and half shaft blockage as an original data set; 2) performing normalization processing on training set data and test set data; 3) constructing a deep CNN model; 4) performing optimization training on an SVM classifier by combining the trained deep CNN model with quantum particle swarm optimization; and 5) inputting test sample data into a trained fault diagnosis model to carry out circuit breaker fault diagnosis. According to the invention, data features are effectively extracted by using the advantage of strong feature extraction capability of a convolutional neural network; and furthermore, the accuracy of data classification is improved by utilizing the advantage that the quantum particle swarm optimization can effectively eliminate a local optimum phenomenon.

Description

Technical field [0001] The present invention relates to the field of on-line diagnosis of major mechanical failure voltage circuit breaker, particularly, to a convolutional neural network-based and QPSO a medium voltage circuit breaker intelligent fault diagnosis method is applied to medium voltage circuit breaker in which solution troubleshooting problems. Background technique [0002] Breaker as an important power equipment plays an important role in the power system, the status of their work directly affects the safety and reliability of the power system. But the huge maintenance costs of the circuit breaker, and the traditional manual inspection mode, not only time consuming, but also bring a lot of unnecessary downtime. Non-invasive diagnostic method due to the failure to facilitate data collection, has become an important research direction breaker maintenance technology. [0003] Fault diagnosis technique mainly includes fault and fault diagnosis feature extraction algorit...

AI Technical Summary

Problems solved by technology

Although the diagnostic accuracy of traditional artificial intelligence algorithms has been significantly improved, they are also used in engineering practice to a certain extent, but these intelligent algorithms generally have problems such as slow diagnosis speed, poor generalization ability, and easy to generate local optimal solutions, which increase the Unnecessary downtime and maintenance time of circuit breakers limit the development of circuit breaker fault diagnosis technology

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