Xiaodi Song

Second generation wavelet transform for data denoising in PD measurement

Detection and diagnosis of partial discharge (PD) activity has been widely adopted in electrical plant condition monitoring. Analysis and detection of PD in practical applications is often hampered by noise in the signal. Recent research has shown that the discrete wavelet transform (DWT) is effective in extracting PD pulses from severe noise. One disadvantage, however, is that DWT does not reproduce accurate PD pulse magnitude and pulse shape after thresholding in the presence of strong noise. This paper presents the application of the second generation wavelet transform (SGWT), as an improved algorithm, to extraction of PD pulse from electrical noise. The paper begins with the description of the fundamental theory and structure of SGWT analysis and comparisons with DWT. The method is then applied to both simulated and real world PD data. Results prove that SGWT can significantly improve the effectiveness of PD denoising.

An Algorithm for Indentifying the Arrival Time of PD Pulses for PD Source Location

In terms of locating the source of partial discharge, transient earth voltage (TEV) detection is well recognised to have advantages over electrical methods. A variety of sensors are suitable for on-line PD testing of outdoor equipment, including ultra-high frequency (UHF) and radio frequency (RF) antenna, transient earth voltage (TEV) sensors, acoustic emission (AE) sensors and high frequency current transformers (HFCT). Among these, UHF, TEV and AE are usually adopted to detect PD activity and to determine the partial discharge (PD) location. These three detection methods display similar response to PD activity. The main characteristic shown is multi-oscillation and gradual attenuation. To locate the source of a PD requires correlation of the PD pulse arrival times, accuracy of the location is determined by the ability to discriminate the difference in arrival time across the sensors. Unfortunately, since the UHF and TEV detection systems have higher sensitivity than others, the major problem faced in determining the arrival time of each pulse is the effect of interference and noise. The present paper proposes the use of discrete wavelet transform (DWT) to overcome the problems associated with noise in TEV measurement of PD in power plant. The method, which applies an effective denoising algorithm developed by the present authors to extract TEV signal from noise and identify the arrival time, is proved to be effective in on-site tests.

Denoising and feature extraction in PD based cable condition monitoring systems

There has been increasing application of on-line partial discharge (PD) based cable insulation condition monitoring among utilities worldwide due to the ability of on-line PD monitoring to allow incipient insulation faults to be detected and aged cable replacement program to be prioritised. However the application is also accompanied with a number of challenges. Data from on-line PD monitoring systems shows presence of higher levels of interference, including sinusoidal RF noise, switching pulses, PD from local plant, radio and power line carrier communication systems, etc. The biggest challenge associated with on-line cable PD monitoring is to distinguish PD generated in cable insulation from noisy raw data, which requires not only application of data denoising techniques but also feature extraction techniques to differentiate signals coming from different sources based on their characteristics. This paper aims to overcome the above-mentioned challenge. Following a brief introduction the paper introduces an effective denoising technique involving the adaptive second generation wavelet transform (ASGWT). To describe the various PD pulses, which the authors have observed from on-line cable PD monitoring systems, methods for PD feature extraction are discussed. These include analysis of raw PD signal, phase-resolved PD pattern, etc. Finally, based on data denoising and feature extraction, signal classification for an on-site PD testing experiment is introduced.

Partial discharge monitoring in medium voltage cables

Of the vast amount of MV cables in the UK, the majority was installed in 1950psilas and 1960psilas. With a design life 60-70 years, age induced degradation and failure of the UK MV systems is becoming increasingly prominent in the minds of plant owners and operators. In this paper, the authors discuss the business case for on-line PD monitoring vs. off-line and compare on-line vs. off-line PD monitoring in MV cables. The paper will also present Wavelet-based denoising techniques as applied to on-line PD test data with examples of analysis and interpretation of practical, in-service data. The effectiveness of the Wavelet-based technique in comparison with other techniques such as the matched filters and the traditional filters will be discussed. Finally challenges for the employment of large amount of PD data for degradation diagnostics purposes will be discussed.

Analysis of on-line power cable signals

For many years identification of incipient Partial Discharge (PD) faults in power cables has been made through off-line investigation techniques. The periodic monitoring of power cables resulted in unexpected failures, with consequent financial penalties for the utilities. More recently, in an effort to allow continuous asset management of the medium voltage cable network to be carried out, on-line monitoring systems are being installed with the aim of reducing unexpected failures. This paper presents work on the analysis and handling of data acquired from an on-line system. A short review of on-line vs. off-line cable PD monitoring will be presented, in terms of their respective advantages and disadvantages. The authorspsila experience of applying Wavelet-based denoising techniques to extract PD data from external noise interference will be presented. Analysis of PD activity and noise interference, with respect to time-of-day, will offer insight into the challenges relating to these systems. Finally, a means of handling the vast amounts of data and of acquiring knowledge from on-line condition monitoring data will be discussed.

A successful on-site PD testing experience of 11kV EPR cable insulation systems

A power generating station in the United Kingdom has reported a number of in service failures in its 11kV single core Ethylene-Propylene Rubber (EPR) insulated cables. Several industrial companies have carried out on-site condition assessment to determine whether other insulation defects were present but no conclusive results have been found due to presence of strong background electrical noise. The present authors were invited to carry out on-site testing to demonstrate the effectiveness of their denoising techniques. This paper presents the processes of the on-site cable partial discharge signal detection experience and the signal processing of the raw data. Following a brief introduction to the tests, equipments and connections, the paper analyses sources of different types of interference signals. These are found to originate mainly from UPS Inverter Supplies or 11kV Motor drive circuits. Thereafter, second generation wavelet transform (SGWT) data denoising algorithm is introduced. SGWT is proved to be an effective denoising technique for the detected data. Also presented in the paper are PD pattern identification and PD source localization methods which are used to identify the source of the PD signal. Finally the diagnosis results, with indication of potential insulation defect and cable joint problems, are provided.

Knowledge discovery from on-line cable condition monitoring systems

Detection and diagnosis of partial discharge (PD) activity has been widely adopted in electrical plant condition monitoring. For many years incipient partial discharge faults in power cables have been identified through off-line investigation techniques. With the development of measurement technology, more recently, continuous on-line monitoring systems are being installed, because in comparison with off-line measurement, it owns more advantages such as low cost, easy set-up etc. This has been instigated with the aim of reducing unexpected failures. Unfortunately, due to a lack of knowledge rules which can be applied to the data detected from on-line PD condition monitoring, this technology has not shown its full potential so far. This paper presents work on the analysis and development of a knowledge acquisition system based on rough set (RS) theory. Results prove that the proposed algorithm can successfully discover the hidden correlations between cable faults and PD measurement data and improve the effectiveness of on-line condition monitoring systems.

Identification and extraction of PD current pulse shapes in a cable monitoring system

The most important indicator of the health of electrical plant items is the condition of their insulation for which partial discharge (PD) test or monitoring has been widely recognized as one of the best tools. The strategic importance of plant in the transmission network, and its high replacement and refurbishment cost, has categorized research and development aimed at PD based condition monitoring at transmission voltages (132 kV to 400 kV). This paper presents the signal analysis- a key part for effective condition monitoring, carried out by the present authors on a cable on-line PD measurement system where PDs from the cable under analysis, PDs from local substation plant items and various forms of noise can all exist simultaneously. In this situation, the most critical step lies in the identification and extraction of the pulses associated with cable PD activities. The paper presents an on-line cable condition monitoring system which is installed in a UK utility and the resultant typical data samples. The current PD pulse shapes are categorised into several types using wavelet transform. Based on their characteristics, a method is proposed to separate pulse signals with different shapes. Results presented in this paper prove that the proposed classification method may constitute a step forward in the automatic PD diagnosis.