Partial discharge recognition method and system

Abstract

The invention provides a partial discharge recognition method and a partial discharge recognition system. The method comprises the steps of collecting audio signal of high-pressure equipment, analyzing the time domain and frequency domain of the audio signal to extract the feature of the signal, employing the method of principal component analysis to reduce the dimension of the signal feature, and classifying and identifying the dimension-reduced signal feature to determine whether the partial discharge is generated. The method and the system provided by the invention employ the method of principal component analysis to preprocess data and reduce the dimension of sample space, thus ensuring the recognition rate and also greatly improving computational speed.

Description

technical field [0001] The invention relates to the technical field of partial discharge, in particular to a partial discharge identification method and a partial discharge identification system. Background technique [0002] Partial discharge is mainly the discharge of the internal insulation of transformers, transformers and other high-voltage electrical equipment under the action of high voltage. It is one of the main causes of failure of various high-voltage equipment. [0003] With the rapid development of my country's electric power industry and the continuous deepening of urban and rural power grid construction and transformation, social development and progress have continuously increased the requirements for power supply reliability. The occurrence of sudden accidents caused by quality problems has become particularly important. [0004] At present, there are mainly two research methods at home and abroad on whether partial discharge occurs in high-voltage equipmen...

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Problems solved by technology

[0004] At present, there are mainly two research methods at home and abroad on whether partial discharge occurs in high-voltage equipment. The first method is to compare the collected signal with a certain empirical threshold after processing. Although the feature parameter identification method is convenient and concise, the identification rate of partial discharge is low

The second is to extract the multi-dimensional characteristic parameters of the partial discharge signal, train the model of whether it is partial discharge, and use the partial discharge identifier of the support vector machine (SVM) to identify the collected signal. This method effectively improves the detection of partial discharge. recognition rate, but SVM has a disadvantage: as the number of samples increases, the required computing time and space storage resources will increase geometrically. When identifying large-capacity samples, the SVM partial discharge recognizer often cannot work effectively.

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