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A step forward in the characterization of the partial discharge phenomenon and the degradation of insulating material through nonlinear analysis of time series

pubblicazioni - Articolo

A step forward in the characterization of the partial discharge phenomenon and the degradation of insulating material through nonlinear analysis of time series

This paper presents a new method to analyze the data acquired from the measurements of the partial discharge phe-nomenon. We describe the principal concepts of nonlinear dynamics and, recalling the concept of ”chaos” and the properties asso-ciated to it, we describe the techniques for the analysis of the acquired time series. First it is shown how to detect the presence of determinism within these through a test of null hypothesis. Afterwards, we introduce the main techniques to process and character-ize time series. Sets of experimental data, concerning partial discharge phenomena acquired in laboratory, under controlled condi-tions, have been processed using these techniques. The experimental results reported in this work show that the phenomenon of partial discharg follow the laws of chaotic dynamics. Finally, it is shown that, through a particular parameter typical of nonlinear dy-namics, based on the analysis of invariants, it is possible to distinguish different types of phenomena related to partial discharges (corona or treeing). Moreover we show that the degradation of types of insulating material is correlated with a dynamic invariant that can be estimated using via external measurements.

The partial discharge (PD) phenomenon is an electrical discharge that affects only part of a dielectric connecting two conductors [1]. This process may occur in the vacuoles of solid insulation, in gas bubbles in insulating liquids, or between dielectric layers. PDs can also occur on spikes or sharp edges of metal surfaces. Generally, the PDs affect the insulator integrity only if it persists for a long time. However, the continuous release of small amounts of energy may cause a slow and progressive deterioration of the dielectric that can lead to its final breakdown [2], [3]. The degradation rate depends more or less on the microscopic structure of the dielectric, the production process, the types of electrical stresses, and other operating conditions [4]. At the beginning of the 20th century, the problem was not even recognized. However, after the middle of the century, along with the introduction of new dielectrics, the development of more compact insulators, and a general increase in the operating voltages, diagnostics of PDs have become a primary issue in electrical engineering [5]. Today, measuring PDs is a common practice even in operational medium- and high-voltage devices. The data gathered are useful to determine the weak points of the components before irreversible damage occurs. Thus, PD detection and recognition have become important tools for the evaluation of insulating degradation in large power devices. The phase φ, the apparent charge q, and the occurrence n have generally been accepted as the basic parameters for the pattern recognition of PDs by means of the standardized algorithm termed phase-resolved PD analysis (PRPDA) [6]. However, the application of this method does not easily lead to identifying the nature of the PD source [7]. According to PRPDA, the measures of PD are classified by taking into account only equivalent discharge intensity and the phase of the alternate current. In this way, the correlation between consecutive pulses cannot be identified. In particular, the connections with the applied voltage and the timing of occurrence are discarded. Furthermore, inadequate modeling of the PD mechanism has resulted in the pattern identification and insulation diagnosis being far from reaching practical applications. In the last 10 years, there has been a growing interest in revealing temporal relations in the stream of PD events. For instance, Patsch and Berton [7] concluded that the analysis of discharge sequences is more meaningful than PRPDA, which is a particular statistical analysis of a large number of discharge events. They proposed the pulse-sequence analysis as a way to reveal the inherent determinism within the discharge process. The only data available are often single temporal sequences of only one kind of measure. Typically, no multiple, independent measures are used. In this case, because the physical dynamics of the PD system are largely unknown and because the observations of this phenomenon are limited, the only viable approach is the time-series analysis of acquired data. To be more precise, we aim to produce, from our work, some indicators correlated with the degeneration of the dielectric material. Because of its nonlinearity, the long-term evolution of the system is unpredictable and hence appears to be random. For this reason, we have explored an alternative method to analyze the data acquired from the measurement of PD phenomena.

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