INTELLIGENT CABLE FAULT DETECTION TECHNOLOGY CONSIDERING CURRENT HARMONIC CHARACTERISTICS

With the rapid development of the power system, cable faults have become an important factor affecting the stable operation of the power system. In this paper, for the problem of cable faults, we improve the overshoot, undershoot phenomenon and sieve speed of the envelope fitting in the Hilbert-Huang transform algorithm, and extract the harmonic characteristics of the current of cable faults by using the improved HHT model. Then, we utilize the information entropy and wavelet singular entropy algorithm to integrate semi-parametric support vector machine algorithm, S-SVM, and construct the wavelet singular entropy and S-SVM model. The information entropy and wavelet singular entropy algorithms are fused with semiparametric support vector machine algorithm, S-SVM, and constructed into wavelet singular entropy and S-SVM models, which are applied to the cable fault identification experiments for detecting different faults in cables. The experimental results show that, when the cables are short-circuited, the currents of different short-circuited cables are all lower than the normal currents, and the wavelet singular entropy and S-SVM models reach more than 92% of the accuracy of the identification of the degradation of the cable line and short-circuited faults. The accuracy of the wavelet singular entropy and S-SVM model for the identification of cable line deterioration and short circuit faults reaches more than 92%, and the overall cable fault detection reaches 98.04%.The maximum error value of the wavelet singular entropy and S-SVM model for detection is 0.5329, and there are only two groups of data more than 0.5.The algorithms in this paper are able to detect the various localization of the cable faults quickly and accurately, and they have a high practical value.