Method for identifying local discharge signals of switchboard based on support vector machine model

Abstract

The invention discloses a method for identifying local discharge signals of a switchboard based on a support vector machine model. The method comprises a model training process and an audio identifying process, and particularly comprises the following steps of: preprocessing audio signals; extracting effective audios according to short-time energy and a zero-crossing rate; segmenting the effective audios and extracting characteristic parameters such as Mel cepstrum coefficients, first order difference Mel cepstrum coefficients, high zero-crossing rate and the like of each segment of the audios; training a sample set by using a support vector machine tool, and establishing a corresponding support vector machine model; after preprocessing audio signals to be identified and extracting and segmenting the effective audios, classifying and identifying segment-characteristic-based samples to be tested according to the support vector machine model; and post-processing classification results, and judging whether partial discharge signals exist. By using the method, the existence of the partial discharge signals of the switchboard is accurately identified, the happening of major accidents involving electricity is prevented and avoided, economic losses caused by insulation accidents are reduced, and the power distribution reliability is improved.

Description

technical field [0001] The invention belongs to the field of audio signal processing and identification, and relates to audio signal processing and pattern identification technology, in particular to a method for identifying partial discharge signals of a switch cabinet based on a support vector machine model. Background technique [0002] With the continuous enhancement of users' awareness of power quality, the requirements for the reliability of power supply are also getting higher and higher. The distribution network is the main foothold of power supply reliability. The switchgear is one of the main equipment of the distribution network. Statistics show that 85% of the insulation faults are caused by partial discharge. Serious electrical accidents can result in economic losses and endanger personal safety. [0003] Actively carrying out the live test or online monitoring of the insulation state of the switchgear is currently the most effective means to prevent early ins...

AI Technical Summary

Problems solved by technology

Due to the limitation of the physiological structure of the human ear, sometimes the real partial discharge sound signal cannot be captured. At the same time, the difference in the hearing of different operators will also cause misjudgment of the insulation failure of the switchgear, and the detection reliability will be threatened. Unnecessary economic losses and security threats

The limitations of the traditional partial discharge recognition algorithm will also lead to low detection accuracy, misjudgment and missed judgment, and failure to detect potential faults in time, resulting in huge economic losses and safety accidents

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