A Method for Recognizing Switchgear Faults Based on Parallel Long-Short-Term Memory Neural Network

Abstract

The invention discloses a method for identifying switchgear faults based on a parallel long-short-term memory neural network, including: (1) collecting and processing audible sound signals and ultrasonic signals; (2) training data sets generated from audible sound signals The training data set generated by the ultrasonic signal and the ultrasonic signal is put into the LSTM network model with a parallel mutual feed structure for training, and the recognition model is obtained; (3) the sensor is used to collect audible sound data and ultrasonic data, and the switch cabinet is monitored online; (4) Put the real-time collected data into the trained LSTM network model to identify and get the prediction results, and judge the switchgear failure. The invention can save computing resources, improve the identification ability, and improve the accuracy of fault judgment of switch cabinets, thereby making the distribution network more efficient and intelligent.

Description

technical field [0001] The invention relates to the technical field of sound signal diagnosis equipment faults, in particular to a method for identifying switch cabinet faults based on a parallel long-short-term memory neural network. Background technique [0002] The operation and maintenance of power equipment has always been a key concern and research issue in the power system. As one of the main equipment in the power transmission and distribution process, the high-voltage switchgear operates safely and ensures the safety and reliability of the power system. . Affected by voltage fluctuations, equipment aging, insulating gas leakage and other reasons, partial discharge of switchgear equipment will cause insulation damage and cause failures. In addition to voltage, current, temperature, flashover and other phenomena in the process, there are also Discharge sound phenomenon, including audible and ultrasonic signals. On-line monitoring of the insulation status of the swit...

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Problems solved by technology

[0004] The purpose of the present invention is to overcome the problems existing in the existing switchgear fault detection, and invented a method based on the audible sound and the ultrasonic signal of the parallel long-short-term memory neural network to identify the fault of the switchgear. The method collects the audible signal and Ultrasonic signals, obtain a large number of characteristic parameters, use the long-short-term memory (LSTM) neural network in the field of deep learning to train and identify characteristic data sets, so as to judge the working status of the switchgear, and solve the problem of single signal acquisition in the sound diagnosis of switchgear faults at this stage , the problem of inefficient identification model and unsatisfactory diagnosis results

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