Linpeng Yao

Application of adaptive neuro fuzzy inference system to the partial discharge pattern recognition

The application of adaptive neuro fuzzy inference system (ANFIS) to the partial discharge (PD) pattern recognition is presented in this paper. Four types of defect models are made according to the main reason of insulation failures in real power system. Experiments are carried out to acquire the sample data, from which eight statistical features are extracted to construct the ANFIS. Different characteristics of the proposed defect models are compared based on the extracted features. Then the ANFIS is trained by characteristic features. Testing samples are utilized to validate the performance of the recognition system. The result shows that ANFIS reaches a successful recognition rate in the application of PD pattern classification.

Intelligent morphological filter applied to the signal processing of partial discharge for XLPE cable

Today the sensitive measurement of partial discharge (PD) signals in XLPE cable can be an important element for quality assurance and diagnostics in the factory and during the on-site test procedure. The results clearly indicate that PD analysis of XLPE cable is an excellent online tool to assess its dielectric condition. However, as the serious interference in-field, the acquired PD signals may be interrupted by periodic interferences and white noises, easily. This paper describes an intelligent morphological filter algorithm to remove the periodic pulse interference and white noise contained in the PD signals. The algorithm consists of two stages of morphological processing. The performance of the algorithm is evaluated with simulated and real PD signals. Compared with classical wavelet, there are substantial improvements in the indexes of average error, root mean square (rms) error and maximum error, especially in reducing distortion of the baseline waveform.