Abstract
Insulation defects that occur in gas-insulated switchgear (GIS), which is one of the most important types of equipment in the power grid, can lead to serious accidents. The ultrasonic detection method is commonly used to detect partial discharge (PD) signals in power equipment to discover defects. However, the traditional method to diagnose defects in GIS with ultrasonic PD signals is still based on the experience of testers. In this study, a classification system was proposed to identify insulation defects of GIS, based on voiceprint recognition technology. Twelve coefficients from mel frequency cepstral coefficient (MFCC) and 24 delta MFCC features were extracted as the acoustic features of the system. A support vector machine (SVM) multi-classifier was constructed to perform the classification and the sequential minimal optimization (SMO) algorithm was used to optimize the computational efficiency of the SVM. The experiments were conducted on a 110 kV GIS with different kinds of insulation defects. The results verified that the classification system with SMO-SVM achieved better identification accuracy and efficiency than the system with SVM. Therefore, it reveals the feasibility of the system to realize identification of insulation defects in GIS automatically and accurately.
Highlights
IntroductionGas-insulated switchgear (GIS) is a metal switchgear which encloses circuit breakers, isolating switches, earthing switches, transformers, surge arresters, busbars, and other electrical devices [1,2]
Gas-insulated switchgear (GIS) is a metal switchgear which encloses circuit breakers, isolating switches, earthing switches, transformers, surge arresters, busbars, and other electrical devices [1,2].Since it was invented, gas-insulated switchgear (GIS) has been one of the most important and widely used types of power equipment in the power grid [3]
This paper presents a classification system based on the technology of voiceprint recognition to automatically and precisely identify the insulation defects of GIS
Summary
Gas-insulated switchgear (GIS) is a metal switchgear which encloses circuit breakers, isolating switches, earthing switches, transformers, surge arresters, busbars, and other electrical devices [1,2]. As the waveform features cannot discriminate the defects of floating electrode and creeping discharge precisely [18] and the wavelet transform is unable to conduct analysis on the whole frequency of signals [21], these methods still have limitations to obtain high accuracy in defect identification. The most common acoustic features include mel frequency cepstrum coefficients (MFCC), linear prediction cepstrum coefficients (LPCC), and perceptual linear prediction [30] Among these acoustic features, the outstanding advantages of MFCC include simple extraction method, high accuracy, and strong resistance to noise [31]. The classification system constructed for the insulation defect diagnosis of GIS adopts MFCC feature extraction and SVM classifiers optimized by SMO to realize automatic and accurate identification. Experiments were conducted on a 110 kV GIS with four kinds of defects to test the performance of the system and the results indicated that with the optimization of SMO, the accuracy and efficiency of the system were increased effectively
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