This paper presents a new wavelet analysis approach in partial discharges cable joint measurements in noisy environments. The proposed technique uses the Cross Wavelet Transform (XWT) to separate PD signals from noise and external disturbances in partial discharges measurements in cable joints using two opposite polarity sensors. The partial discharge measurements were performed during impulse and superimposed voltages, leading to a huge amount of noise and pulse shaped external disturbances. The XWT foundations, the experimental setup and the XWT methodology proposed are presented together with the results of the recognition of PD originated in the cable joint. In the experiments, 51,898 signals were acquired, in which 733 were PD signals from the joint and 51,165 corresponded to noise or external disturbances. The XWT performance was studied, finding that 97% of the PD signals were correctly separated by the technique proposed. The results demonstrate the effectivity of the XWT in separating PD signals from noise and external disturbances in this particular measuring system configuration.
Nowadays, Partial Discharges (PD) detection is an essential tool for the diagnosis of high-voltage equipment because of their accuracy to detect and quantify defects and damages in the dielectric insulation, where the detection implies the measurement, acquisition, storage and processing of the PD phenomenon . In general, the most widespread PD detection system is based on electrical measurements, in which the PD signals are acquired in the form of individual or series of electrical pulses. In offline PD cable field tests and in laboratory tests, capacitive coupled sensors installed at the cable ends are used [2,3]. In cable systems, statistically most of the partial discharges come from either the cable terminations or the cable joints, being necessary to locate them by time domain reflectometry techniques. In spite of the PD measurement has been exhaustively researched over the years, the separation of PD pulses from noise is one of the main challenges, especially in online applications. Therefore, noise contamination is one of the significant problems of PD detection , because noise, disturbances and interferences can give rise to complex Phase Resolved Partial Discharges (PRPD) patterns or clusters, leading to misleading interpretations . For this reason, several studies [1,4,6–13] have focused on the PD pulses separation and denoising techniques for PD measurements. Among these studies, the wavelet transform has been broadly used because is capable of locating time and frequency components allowing the analysis of aperiodic signals with irregular and transition features, such as the partial discharges . In the wavelet analysis techniques, the Discrete Wavelet Transform (DWT) has been used extensively for denoising PD signals. In general, in the DWT denoising, the wavelet coefficients are calculated for a given signal and then the coefficients are passed through a threshold (soft or hard) and followed by the reconstruction of the signal by taking the inverse wavelet transform of the modified DWT coefficients. However, a major problem that most of these denoising techniques face is the ingress of external interferences having time-frequency characteristics similar to the partial discharge signals (pulse shaped disturbances); for instance, periodic pulse shaped interferences from power electronics or another periodic switching , PD and corona discharges from the external power system, electrical pulses from switching operations, lightings, etc. This external noise can cause a false indication of PD activity, jeopardising the PD measurements as a diagnostic tool. To reduce the false indications, in PD measurement systems more than one electrical sensor (HFCTs, UHF antennas, coupling capacitors, etc.) is used, meaning that multiple waveforms are simultaneously acquired, because recording each signal through different sensors may provide extra useful information about the real nature of the waveform recorded. Tools like the correlation and trend analysis can provide the significance of relationships between the signals recorded .