Time–Frequency Entropy-Based Partial-Discharge Extraction for Nonintrusive Measurement

Partial-discharge (PD) detection is an important method to evaluate the insulation condition of metal-clad apparatus. Nonintrusive sensors, which are easy to install and have no interruptions to operation, are preferred in onsite PD detection. However, its accuracy often degrades due to the interferences in PD signals. In this paper, a PD extraction method that uses entropy-based time-frequency (TF) analysis is introduced. Entropy, which is a measure of disorder, is an ideal tool to extract PD or pulse-like interferences since the disorder of local TF spectrum increases when large-amplitude TF coefficients are contained. According to the characteristics of nonintrusive sensors and the distribution of entropy, the TF spectrums of PD and interferences are first separated. Then, the PD signal and interferences are recovered via inverse TF transform. The denoised results of some PD data as well as interferences demonstrate that the combination of entropy with TF analysis can discriminate PDs from interferences with a different TF entropy spectrum.

I. Introduction

Detecting and identifying partial discharges (PDs) in gas- or oil-insulated switchgear and transformers with metallic enclosures are well-established procedures [1]. In traditional methods, the PD sensors are mounted inside the metallic enclosure which promises a high signal-to-noise ratio (SNR). But for operational switchgears and transformers without such PD sensors, arranging a shutdown specifically to fit internal couplers can be rarely justified [1]. So external nonintrusive sensors, which are easy to install and do not have operation interruptions, are becoming more popular for the devices which are not suitable to install internal sensors. Usually, the radiating electromagnetic wave from the PD source induces and forms a small pulse-like voltage on the metal tank surface. This is the so-called transient earth voltage (TEV) [2], [3]. The nonintrusive PD sensors detect those pulse-like TEV signals to determine PD existence. When detecting PDs on the external surface of the enclosure, one of the major problems that needs to be addressed is interference from the surroundings. Researchers have proposed many approaches [4]–[6] for the noise reduction. But difficulties still exist due to the following challenges: 1)

Very low SNR. Reference [3] points out that the amplitude of TEV is proportional to the original PD and decreases with the distance from the PD source. When measured on the surface of metal cladding in field tests, the TEV signal is small that noises cannot be ignored. The noises from electronic equipment and the radio-frequency disturbances are believed to be major kinds of interference [7]. Currently, some thresholding methods, such as wavelet analysis, have performed well in PD extraction. But wavelet analysis could only divide the frequency range into several bands. This segmentation is not accurate enough to display the slight energy variations when PD's energy is smaller than noise in most frequency bands.

2)

Lack of datasets for artificial-intelligence (AI)-based methods. In many PD denoising methods, AI has been used to recognize PDs and other pulse-like noises [8], [9]. A basic premise for these AI-based methods is the proper collection of a database with enough size. However, such a large database is almost impossible to collect in most field applications.