Partial discharge (PD) signals generated within electrical power equipment can be used to assess the condition of the insulation. In practice, testing often results in multiple PD sources. In order to assess the impact of individual PD sources, signals must first be discriminated from one another. This paper presents a procedure for the identification of PD signals generated by multiple sources. Starting with the assumption that different PD sources will display unique signal profiles this will be manifested in the distribution of energies with respect to frequency and time. Therefore the technique presented is based on the comparison of signal energies associated with particular wavelet-decomposition levels. Principal component analysis is adopted to reduce the dimensionality of the data, whilst minimizing lost information in the data concentration step. Physical parameters are extracted from individual PD pulses and projected into 3-dimensional space to allow clustering of data from specific PD sources. The density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm is chosen for its ability to discover clusters of arbitrary shape in n-dimension space. PD data from individual clusters can then be further analyzed by projecting the clustered data with respect to the original phase relationship. Results and analysis of the technique are compared for experimentally measured PD data from a range of sources commonly found in three different types of high voltage (HV) equipment; ac synchronous generators, induction motors and distribution cables. These experiments collect data using varied test arrangements including sensors with different bandwidths to demonstrate the robustness and indicate the potential for wide applicability of the technique to PD analysis for a range of insulation systems.

Discrimination of multiple PD sources using wavelet decomposition and principal component analysis

Power utilities are struggling to reduce power failure incidents in substations and their components to operate more reliably and economically [1]. Many power failures are produced directly or indirectly because of the insulation system of utility components [2], [3]. The selection of the insulation should ensure power plant operational continuity along with completely resolving or significantly limiting the actual power system's failures [4]. Gas insulated substations (GIS) have the best insulation performance which ensures achieving minimum failure incidents, although at high installation cost. The most common insulating gas used in GIS is Sulfur hexafluoride (SF 6 ) gas, which is widely used as an effective electrical insulation as well as an arc-quenching medium [5]. Basic GIS and gas insulated transmission lines (GITL or GIL) consist of a conductor supported by solid insulators inside an enclosure filled with SF 6 gas or its mixture [6].

Partial discharge detection and diagnosis in gas insulated switchgear: State of the art

Partial discharges (PD) in high-voltage equipment cause damage to insulation. One method for detecting them is the ultra-high-frequency (UHF) method. Subsequent analysis using phase-resolved patterns or time-of-flight measurements can identify PD type or locate the defect. This study presents a method for comparing UHF signal envelopes to differentiate between multiple PD sources. Experimental investigations are conducted to identify an approach that strikes a compromise between competing aims of minimising signal sampling rates while retaining sufficient ability to discriminate between PD sources in different locations. The envelope comparison method is efficient and has potential for incorporation within automated systems. Results clearly demonstrate the ability to distinguish between PD sources in different positions, which offers a means to determine the number of PD sources present. Since each PD pulse can then be associated with a particular subset of data, subsequent PD interpretation could be applied to separate PD date streams, rather than to mixed data. An radio frequency (RF) detector circuit was designed and used to validate the approach by means of experiments carried out in a model transformer tank. The technique should be applicable to other condition monitoring systems that are required to distinguish between complex transient signals.

Differentiating between partial discharge sources using envelope comparison of ultra-high-frequency signals

This study proposes a noncontact type of acoustic measurement system and applies wavelet transform to noise suppression in order to raise the correct partial discharge (PD) signal identification rate. We first investigate an acoustic measurement method in the laboratory and apply the wavelet transform to suppress noise. A wavelet mother function most similar to the acoustic PD signals is chosen to set the filtering threshold value for the wavelet transform. Finally, the proposed acoustic measurement system is applied on line to epoxy-resin transformers, power distributors and the like. The superior measurement results we obtained will be able to help maintenance personnel eliminate field noise to correctly identify the PD fault types of power equipment.

Study of Partial Discharge Measurement in Power Equipment Using Acoustic Technique and Wavelet Transform

UHF method to detect partial discharge (PD) phenomenon has been proven to be an effective way. Many types of UHF antenna have been developed in order to achieve a better sensitivity and accuracy in certain bandwidth (300 MHz-3.0 GHz) for detecting the ultra-high frequencies generated by partial discharge. Bowtie antenna is proven to be sensitive, accurate and easily designed and fabricated. This paper focuses on the enhancement of bowtie antenna to achieve better performance in detecting partial discharge using UHF method. Characteristics of antenna both on the simulation result prior to the fabrication and measurement using a network analyzer show the good agreement. Experiment was carried out in a gas insulated switchgear (GIS) model with a protrusion on the conductor as PD source and antenna located at different positions with applied voltage range from 20-25 kV. EM wave intensity and frequency captured by antenna were observed. The relation between the distances of PD source to antenna, either from one spacer aperture to others, or distance from one spacer aperture were investigated. Antenna mounted 55 cm from the closest spacer aperture to the PD source can still detect EM wave intensity very well. The lowest EM wave peak envelope intensity measured at 30.03 mV from the same spacer aperture compared to 1.952 mV peak-peak EM wave intensity from internal GIS UHF sensor demonstrates a sufficient sensitivity performance.

Enhanced bowtie UHF antenna for detecting partial discharge in gas insulated substation

In this paper, in order to represent three kinds of typical partial discharge respectively happened in dielectric, out of dielectric and surface of dielectric, five kinds of typical partial discharge models, which included needle to plate, needle to needle, plate to plate, surface and air void, were designed and manufactured with aluminum. Based on the designed models, an experiment system of ultra high frequency partial discharge detection was comprised with non-partial discharge transformer, 2.5 GHz bandwidth oscilloscope, broad band amplifier and the developed double helical antenna. Afterwards, partial discharge experiments were carried out in air, and the method recommended in IEC60270 was referenced to measure the apparent quantity of partial discharge in pC. The experimental waveforms were given out. At last, the characteristics of the ultra high frequency signals excited by partial discharge were analyzed in time domain, frequency domain and wavelet coefficients. Using kernel principal component analysis, the feature extraction was realized. Then, several characteristics, which included deflection, protruding, frequency distributing, decay time, wavelet coefficients and so on, were proposed to describe the ultra high frequency signals. The conclusion educed in this paper is significant to research the feature extraction and pattern recognition of ultra high frequency partial discharge detection for high voltage apparatus.

Experimental study and feature extraction on UHF partial discharge detection for typical model in air

In order to detect and identify the defects on GIS, the ultra-high frequency envelope detection circuit and high-speed data acquisition system was designed, and the tests of five typical defects for GIS were made in the laboratory and the envelope signal was acquired based on Ultra-high frequency (UHF) envelop detection circuit. And wavelet denoising technique was used to reduce the white noise of the measurement envelop signal. A large number of experiments show that time-domain characteristics of partial discharge signals generated by the same defect model are about the same, envelopes are nearly identical too. The time-domain characteristics of partial discharge signals generated by different defect models are different, and their envelope shapes are different too. A new method of pattern recognition was proposed which analyses the characteristic parameters of the envelope signal at time-domain with BP neural network. A large number of test data in Lab indicate that the method is effective.

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Partial discharge diagnosis on GIS based on envelope detection

The suppression of discrete spectral interference (DSI) is always an important topic in on-line partial discharge monitoring and for this purpose many digital signal processing methods are put forward. Based on the research of the threshold value method of frequency domain in fast Fourier transform, an improved algorithm is presented in which by use of polynomial curve fitting method the processing of the peripheral frequency band of the spectral is carried out at the both sides of the zero setting area of the original threshold value method, so the algorithm can adapt the DSI in a wider frequency band. The results from simulation and measured data indicate that using this algorithm a better interference suppression can be obtained.

Research on improved fast fourier transform algorithm applied in suppression of discrete spectral interference in partial discharge signals

To effectively prevent the line-related accidents and improve the line management level,the paper designed a uniform application platform acting as an integrated on-line monitoring system for analyzing and evaluating the operating state and fault situation of the overhead power transmission lines.The developed system,which is composed of some remote data acquisition devices installed in the field and a main station installed in the control center,is responsible for synthetically monitoring main parts of the transmission lines including conductors,towers and insulators.According to the practical requirements,the remote data acquisition devices can be flexibly configured to realize multiple functions such as on-line monitoring of insulator pollution,detecting low and zero resistance insulators,measuring the conductor sag,tracing the conduct temperature,monitoring dangerous points by real-time video,watching the local climate including humidity,temperature and wind speed along the line corridor,detecting and locating the line faults and evaluating the line operating state by giving a predefined line healthy index,etc.The system user can timely get a comprehensively impression on the change of the line operating key states by analyzing different kinds of monitoring data recorded in the integrated system,which can be applied in the line operation and management department and regarded as a foundation for condition-based maintenance of transmission lines.

Integrated Monitoring System for Evaluating Overhead Transmission Lines Operation and Fault State

In order to research the pattern of partial discharge(PD)excited from different defect models in gas insulated switchgear(GIS),we prepared four kinds of defects,such as the floating electrode,protrusion corona,freedom particle and void.By adopting the PD signals,three two-dimensional distributions,such as the maximum discharge quantity,the mean discharge quantity and the amount of discharge,were achieved,thereby,24kinds of statistical fingerprint parameters were extracted.According to problems of the cluster validity,in the choice of the cluster number c and the fuzzy weighting exponent mfor the given PD samples when using the fuzzy clustering algorithms such as the fuzzy C-means(FCM)and the Gustafson-Kessel(GK)methods,we introduced a new evaluation indicator for cluster validity,namely,U(c).The conclusion can be drawn that,the higher the value of U(c)is,the closer to the true value the cluster number c will be.The comparison with other validity indices verifies the efficiency and accuracy of the U(c).

Huang Chengjun

Papers

Experimental study and feature extraction on UHF partial discharge detection for typical model in air

Partial discharge diagnosis on GIS based on envelope detection

Research on improved fast fourier transform algorithm applied in suppression of discrete spectral interference in partial discharge signals

Integrated Monitoring System for Evaluating Overhead Transmission Lines Operation and Fault State

Optimization Method of Parameter for Fuzzy Clustering Algorithm and Application in the PD Pattern Recognition for GIS