After referring briefly to the earlier developments of computer-based PD measurements in the 1970s, the paper outlines how the introduction of new technology has resulted in the design and production of a number of digital systems for use in test laboratories and on site. In order to avoid overgeneralization, the basis of the hardware required for detection and recording of the PD pulses is described by reference to a particular system, it being understood that many detailed variations are possible depending on the application. The paper describes a number of parameters that can be calculated by software and used to characterize the PD behavior. These include the IEC-270 integrated quantities, statistical moments and other fingerprints for recognizing the PD patterns within the power frequency cycle. Examples are given of the patterns obtained on equipment and samples. These are necessarily selective and are intended to indicate how the techniques may be applied. It is concluded that these new methods will become valuable in procedures for assessing the condition of electrical insulation, in particular faults or defects, but must be used in conjunction with existing techniques until improved confidence is achieved in interpreting results, especially in respect of the various phase-resolved distributions and calibration procedures. >

Development of computer-based measurements and their application to PD pattern analysis

An attempt was made to use hidden Markov models (HMM) to classify partial discharge (PD) image patterns. After an introduction to HMM, the methodology and algorithms for evolving them are explained. The selection of the model and training parameters and the results obtained are discussed. The utility of the approach is evaluated by applying it to five types of actual PD image patterns. The performance of the HMM approach is shown to exceed that of neural networks. >

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Use of hidden Markov models for partial discharge pattern classification

Computer aided partial discharge (PD) source identification using different multidimensional discharge patterns is widely regarded as an important tool for insulation diagnosis. In this paper, a neural network (NN) approach to PD pattern classification is presented. The approach is based on applying variants of the counterpropagation NN architecture to the classification of PD patterns. These patterns are derived from physically related discharge parameters, different from those commonly used. It is shown that considerable improvements of the classification quality can be obtained when an extended counterpropagation network with a dynamically changing network topology is applied to patterns that employ the voltage difference between consecutive pulses instead of the phase of occurrence as the main discharge parameter. Furthermore, using a particular parameter vector that takes the correlation between consecutive discharges into account also allows to solve the rejection problem with this type of NN.

PD source identification with novel discharge parameters using counterpropagation neural networks

A method is presented for the determination of physical discharge parameters for partial discharges (PDs) of voids in solid insulation. Based on a recently developed stochastic theory of PD processes, a statistical analysis of a measured phase-resolved partial discharge (PRPD) pattern allows the determination of the relevant physical parameters like first electron availability or decay time constants for deployed charge carriers. These parameters can be estimated directly from the measured patterns without the need of performing simulations. Furthermore, error bounds for the parameter values can be given. The parameter estimation algorithm is based on the analysis of a contiguous region of the PRPD pattern where this region can be chosen nearly arbitrarily. Thus, even patterns with several active PD defects or patterns which are corrupted by noise can be analysed. The method is applied to a sequence of patterns of a void in epoxy resin. The change in first electron availability in the course of a day can be determined quantitatively from the data while the other physical parameters remain constant.

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Analysis of phase-resolved partial discharge patterns of voids based on a stochastic process approach

Pulsed power and intense electric field now apply to a large variety of domains for which the need of in situ nonperturbative measurements remains a challenge. The main requirements that sensors should fulfill for that purpose are nonmetallic composition, small size, ultrawide bandwidth, compatibility with liquids and gases, possibility for remote measurements at long distances, true vectorial (field direction) time-domain measurements, capability of measuring electric fields up to the electric breakdown of air, and a large dynamic range. Obviously, all these requirements cannot be achieved by a unique sensor. However, most of them are fulfilled by pigtailed electrooptic (EO) sensors. In this paper, after recalling the principle of the EO effect and its use for electric field measurement, we deal with the measurement linearity and selectivity of the EO sensor. Associated with the nonperturbative behavior of the EO sensor, the measurement dynamics of EO sensors exceeds 100 dB. Furthermore, EO sensors present an intrinsic flat response from quasi d.c. (10 Hz) up to a few tens of gigahertz. Thanks to their composition of high dielectric strength materials and their optical pigtail, EO sensors are completely immune to the electromagnetic environment except on a very small volume (≅ 10 mm3) corresponding to the transducer element of the sensor. This leads to a very high spatial resolution. We finally illustrate the capabilities of this technology through in situ measurement of the electric discharge transients and vectors.

A Nonperturbative Electrooptic Sensor for in Situ Electric Discharge Characterization

An overview is given of partial discharge patterns, their evaluation, and determination of their origin. Some brief comparisons of test equipment considered to be of general interest are made. The intent is to assist practitioners in the interpretation of results as well as in the accurate discrimination between true partial discharges within an insulating structure and other unwanted conduction or radiation-induced interference signals. >

D.A. Nattrass

Papers

Partial discharge measurement and interpretation