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Comparative Analysis of Thermography Studies and Electrical Measurement of Partial Discharges in Underground Power Cables

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Abstract

The principal cause of damage in underground power cable installations is partial discharge (PD) activity. PD is a localized non-linear phenomenon of electrical breakdown that occurs in the insulating medium sitting between two conducting materials, which are at different potentials. The damage to the insulating material is induced by the AC voltage to which the insulator is subjected during the discharge process, and it can be directly or indirectly measured by the charge displacement across the insulation and the cavity defect. Non-invasive detection techniques that help in identifying the onset of the discharge process are required as PD is a major issue in terms of maintenance and performance of underground power installations. The main locations of failure are the accessories at points of connection such as terminals or splices. In this article, a study of electrical detection of PD and image processing of thermal pictures is presented. The study was carried out by controllably inducing specific failures in the accessories of the installation. The temporal evolution of the PD signals was supported with thermal images taken during the test in order to compare the PD activity and thermal increase due to failure. The analysis of thermographic images allows location of the failure by means of intensity-based texture segmentation algorithms. This novel technique was found to be suitable for non-invasive detection of the PD activity in underground power cable accessories.

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Abbreviations

\({\varvec{C}}_{\varvec{a}}\) :

Insulation without defect

\({\varvec{C}}_{\varvec{b}}\) :

Dielectric in series with the gaseous capacitance c

\({\varvec{C}}_{\varvec{c}}\) :

Cavity or part of the surface in which the PD occurs

\({\varvec{A}}\) :

Capacitor area

\({\varvec{d}}\) :

Insulation thickness

\(\varvec{\varepsilon }_{\mathbf{0}}\) :

Permittivity of free space

\(\varvec{\varepsilon }_{\mathbf{r}}\) :

Relative permittivity of the defect

\({\varvec{t}}\) :

Defect thickness

\({\varvec{q}}\) :

External charge displacement

\({\varvec{q}}_{\mathbf{1}}\) :

Real charge displacement

\({\varvec{V}}\) :

Voltage in dielectric

\({\varvec{V}}_\mathbf{i }\) :

Instantaneous PD inception voltage of the cavity

\({\varvec{U}}\) :

Voltage across the cavity before a discharge of c

\(\varvec{\Delta }{\varvec{V}}\) :

Drop voltage over c caused by the discharge

\(\varvec{E}(\varvec{\varOmega }_{\varvec{k}})\) :

Local entropy in the neighborhood \({\varOmega }_{k}\)

\({\varvec{L}}\) :

Maximal gray scale

\({\varvec{M}}_{\varvec{k}}\times {\varvec{N}}_{{\mathbf {k}}}\) :

Window size

\({\varvec{n}}_{\varvec{i}}\) :

Pixel number with gray scale i in the neighborhood

\({\varvec{P}}_{\varvec{i}}\) :

Probability of gray scale i that appears in the neighborhood \(\varOmega _{k}\)

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Acknowledgments

This work was supported in part by the University of Guanajuato by Grant PIFI 2013.

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Correspondence to A. Gonzalez-Parada.

Appendix 1: Image Processing Definitions Textual Citation from References [9–11]

Appendix 1: Image Processing Definitions Textual Citation from References [911]

Region of Interest (ROI) is a region of interest (often abbreviated ROI), is a selected subset of samples within a dataset identified for a particular purpose. An image may be considered to contain sub-images sometimes referred to as regions of interest, ROIs, or simply regions.

Texture is concerned with the spatial distribution of the image intensities and discrete tonal features. When a small area of the image has little variation of discrete tonal features, the dominant property of that area is gray tone. When a small area has wide variation of discrete tonal features, the dominant property of that area is texture. There are three things crucial in this distinction: (1) the size of the small areas, (2) the relative sizes of the discrete tonal features, and (3) the number of distinguishable discrete tonal features. Texture can be described along dimensions of uniformity, density, coarseness, roughness regularity, intensity, and directionality.

Image segmentation is a process which typically partitions the spatial domain of an image into mutually exclusive subsets, called regions, each one of which is uniform and homogeneous with respect to some property such as tone or texture and whose property value differs in some significant way from the property value of each neighboring region. Regions produced by an image segmentation process using image intensity as a property value produce regions which are called discrete tonal features.

Preprocessing is an operation applied before pattern identification is performed. Preprocessing produces, for the categories of interest, pattern features which tend to be invariant under changes such as translation, rotation, scale, illumination level, and noise. In essence, preprocessing converts the measurement patterns to a form which allows a simplification in the decision rule. Preprocessing can bring into registration, bring into congruence, remove noise, enhance images, segment target patterns, detect, center, and normalize objects of interest.

Image processing or picture processing encompasses all the various operations which can be applied to image data. These include, but are not limited to, image compression, image restoration, image enhancement, preprocessing, quantization, spatial filtering, matching, and recognition techniques.

Background Texture It is the bottom part of the image. This structure is compared and it decides, based on a threshold, the part that must be removed.

Feature extraction is the process by which an initial measurement pattern or some subset of measurement patterns is transformed to a new pattern feature. Sometimes feature extraction is called property extraction. The word pattern can be used in three distinct senses: (1) as measurement pattern; (2) as feature pattern; and (3) as the dependency pattern or patterns of relationships among the components of any measurement n-tuple or feature n-tuple derived from units of a particular category and which are unique to those n-tuples, that is, they are dependencies which do not occur in any other category.

The contrast of an object against its background can be measured by (1) its contrast ratio, which is the ratio between the higher of object transmittance or background transmittance and the lower of object transmittance or background transmittance; (2) its contrast difference, which is the difference between the higher density of object or background and the lower density of object or background; (3) its contrast modulation, which is the difference between the darker of object or background image intensity and the lighter of the two divided by the sum of object image intensity and background image intensity.

Mathematical morphology refers to an area of image processing concerned with the analysis of shape. The basic morphologic operations consist of dilating, eroding, opening, and closing an image with a structuring element.

Dilating an image I by a structuring element s having support or domain S produces a dilated image denoted by \(I\oplus S\) which is defined by \(I\oplus S\left( {r,c} \right) =max _{\left( {i,j} \right) \in S} \left\{ {I\left( {r-i,c-j} \right) +S\left( {i,j} \right) } \right\} \). Dilating is a commutative, associative, translation invariant, and increasing operation. Dilating is the dual operation to eroding.

Eroding an image I by a structuring element s having support or domain S produces an eroded image denoted by \({\mathbf {I}}\ominus {\mathbf {S}}\) which is defined by \({{\mathbf {I}}}\ominus {{\mathbf {S}}} \left( {{\mathbf {r}}},{{\mathbf {c}}} \right) ={{\mathbf {min}}} _{\left( {{\mathbf {i}}}, {{\mathbf {j}}} \right) \in {{\mathbf {S}}}} \left\{ { {{\mathbf {I}}}\left( {{\mathbf {r}}}+{{\mathbf {i}}}, {{\mathbf {c}}}+{{\mathbf {j}}} \right) +{{\mathbf {S}}}\left( {{\mathbf {i}}},{{\mathbf {j}}} \right) } \right\} \). Eroding is a translation invariant and increasing operation. It is the dual operation to dilating.

Opening an image I with a structuring element s produces an opened image denoted by \(I\circledcirc S\) which is defined by \(I\circledcirc S =\left( {I\ominus S} \right) \oplus S\). Opening is an increasing, anti-extensive, and idempotent operation. It is the dual operation to closing. Opening an image with a disk-shaped structuring element smooths the contour, breaks narrow isthmuses, and eliminates islands and capes smaller in size or width than the disk structuring element.

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Gonzalez-Parada, A., Guzman-Cabrera, R., Torres-Cisneros, M. et al. Comparative Analysis of Thermography Studies and Electrical Measurement of Partial Discharges in Underground Power Cables. Int J Thermophys 36, 2356–2369 (2015). https://doi.org/10.1007/s10765-015-1926-z

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