Partial discharge (PD) detection and diagnosis based on the ultra-high frequency (UHF) signals is one of the most widely adopted methods to evaluate the internal insulation status of high voltage equipment. Benefit from the rapid development of computing hardware and data processing algorithms, the intelligent PD fault diagnosis method based on the UHF data has made considerable progress in the past two decades. This two-part paper aims to give a comprehensive review about the application of signal processing and machine learning technologies in UHF PD detection and diagnosis. These technologies are divided into three categories according to their respective purpose, which are the preprocessing technology, source localization technology and pattern recognition technology. As the first one of the two-part review, we focus on the preprocessing and localization approaches in this paper. Specifically, for the preprocessing topic, the methods for signal denoising, multi-source separation, and pulse segmentation are included. While for the localization topic, the time difference of arrival (TDOA) method, direction of arrival (DOA) method, received signal strength indicator (RSSI) method, and other latest methods are reviewed. For each topic, the basic ideas, recent research progresses, advantages and limitations are discussed in detail. Before the conclusion, we also make a discussion about the application effects of the above technologies and prospect some future directions accordingly. In the second paper, the pattern recognition problems based on the UHF PD data will be concentrated, especially the application of deep learning algorithms.

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (I): Preprocessing and Localization Approaches

Method of GIL partial discharge localization based on natural neighbour interpolation and ECOC-MLP-SVM using optical simulation technology

In this article, it is provided a comprehensive survey on the most relevant research in the area of detection, classification and location of partial discharge (PD) sources using the radiometric or ultra high frequency (UHF) method, focusing mainly on the works related to the use of antennas. To the best of the authors knowledge, this is the first review paper that focuses on the application of antennas based on UHF method in the complete diagnosis of PD. Specifically, it is first presented the main contributions to the development and optimization of UHF sensors for application in the detection of PD sources. Second, articles that used the UHF method to identify different types of PD in high voltage equipment are described. Finally, the main contributions regarding the location of sources of defects using the radiometric method are presented. Summary of works in the area of UHF detection sensors, PD classification and PD location, with their respective contributions, are shown. Finally, research trends, challenging and open issues are discussed. The purpose of this article is to provide important information about the complete diagnosis of PD, which could be used to guide the development of more efficient techniques in this research area.

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Detection, Classification and Location of Sources of Partial Discharges Using the Radiometric Method: Trends, Challenges and Open Issues

Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses

Partial discharge detection and identification at low air pressure in noisy environment

To achieve comprehensive insulation deterioration motoring of power equipment and early fault warning in air-insulated substations, a data-driven partial discharge (PD) source localisation method employing noisy ultra-high frequency (UHF) received signal strength indicator (RSSI) and particle filter is proposed in this study. Compared with the existing UHF time-difference-based techniques, UHF wireless sensor arrays and RSSI-based methods provide an economical and high-adaptability solution. However, owing to the multi-pathing and shadowing effects, UHF signal attenuation cannot be modelled. Therefore, a Kalman filter was employed to smoothen the RSSI signal. Furthermore, a semi-parametric regression model is proposed to achieve a more accurate relationship between the RSSI and the transmission distance. Finally, in contrast to traditional localisation algorithms directly based on the RSSI ranging model, a particle filter was used to achieve higher accuracy. It predicted the best distribution of the position of PD by learning and considering all the system states of the previous moment. The laboratory test was performed within an area of 6 m × 6 m, and the results demonstrate that the mean PD source localisation error was 1.16 m, which gives a potential application for the identification of power equipment with insulation deterioration in a substation, while the accuracy is still needed to be verified further by field tests.

Hui Song

Papers

Intelligent learning approach for UHF partial discharge localisation in air-insulated substations