64 Measurement Automation Monitoring, Mar. 2015, vol. 61, no. 03 Ireneusz URBANIEC, Paweł FRĄCZ OPOLE UNIVERSITY OF TECHNOLOGY, INSTiTUTE OF ELEKTROENRGETICS, Prószkowska 76, Budynek 2, 45-578 Opole

Application of UV camera for PD detection on long rod HV insulator

Abstract The aim of the research works, results of which are presented in this paper, was to determine the feasibility and to indicate the application scope of optical method in the diagnosis of high-voltage (HV) long rod insulators. Diagnosis of technical condition of HV insulators is critical, because the insulation system undergoes aging processes, which cause deterioration of the insulating properties. The measurements were performed during test under laboratory conditions using an UV camera. Corona partial discharges occurring on the long rod insulator were registered, while the supply voltage value was increased. The presented analyses consider dependencies of the supply voltage value on the count number of UV photons emitted by partial discharges occurring in long rod insulator made of porcelain. Keywords: surface partial discharges, PD measurement, optical method, UV camera, HV insulator diagnosis. 1. Introduction

In recent years, more and more research centers are involved in the measurement of partial discharges (PD) that may be generated on devices for transmission and distribution of high voltage electricity. Corona discharges are one of the three PD types, which are connected with voltage breaks, not causing, however, short circuit in the device. There exist also surface discharges (SPD), associated mainly with contamination on the insulator surface and inner discharges, associated with degradation inside the insulation material. Corona discharges are created by flow of electrical current, e.g. in the air, from the discharge electrode, which is at high voltage. The high voltage is producing ionization of air causing plasma around the electrode. The ions carry the load in place of a lower potential. For diagnostic tests related to the assessment of the technical condition of high-voltage transmission lines and power equipment, professional cameras that allow registration of UV radiation are widely used. [1-8]. These cameras enable for detection and localization of corona, arc and surface partial discharges, which occur on high and medium voltage devices. Such discharges generate among others ultraviolet (UV) radiation. The most unfavorable effect of corona discharges is their negative impact on the insulation, which leads to damage the insulating elements, and thus, to the immobilization of whole energy transmission systems. Hence, the aim is to prevent the corona and eliminate them at the early stage. In the next phase arcing and permanent damage occur. Even the presence of small discharges in one place can cause adverse effects. This is because the current flow and pollution cause the formation of corrosive substances, which enhance deterioration of the insulators and allow moisture to access inside the material and faster progressive degradation. Localization of corona discharge is a useful warning message prior to their visualization, similar to detection of problems using infrared techniques. UV camera detects and locates corona by detecting the UV signal generated during discharges. The discharges are counted and the number of counts is given as result. This allows one to determine the level of discharge from a safe distance without the need of turning off the object.

In following some examples of similar research is stated here. The authors of paper [9] used a CoroCAM camera, produced by the CSIR from South Africa, in order to determine the relationship between the number of UV radiation photons, and the size of the amplification of the supply system, and the distance between the source of PD, and the place of positioning the recording camera. In [10], the same authors discuss the advantages and

disadvantages of the use of optical methods for diagnosis of high voltage equipment and gave examples of typical defects. Similar studies using the CoroCAM camera are presented in [9, 11-13]. The problem of calibration of the measurement system when using this type of camera for to measure the UV radiation emitted by corona discharges is considered in a diploma thesis in [14]. Also other camera types are applied for the measurement of UV radiation applied to the PD detection: Lilin6, CornoaScop and DayCor [9]. Construction and operation of the DayCor camera, which enables the detection and measurement of corona PD, at a distance of 100 m from the emission source in daylight is described in detail in the paper [15]. In the paper [16] design, basic parameters and functional properties of a camera applied for measurement of electromagnetic waves emission in the infrared, visible light and ultraviolet are presented. Authors have performed these studies during diagnostic testing of high voltage overhead lines and electrical equipment. In the opinion of the authors the described camera can successfully be used for diagnosis of electrical insulators, for the presence of corona and detection of local overheating.

The aim of the research works, results of which are presented in this paper, was to determine the feasibility and to indicate the application scope of UV camera type DayCor from Ofil Ltd for detection of corona discharges generated on long rod HV insulator made of porcelain. 2. Measuring setup and methodology

The measurements were performed during tests under laboratory conditions using an UV camera. The applied UV camera (Fig. 1), DayCor Superb, enables for corona and arcing detection by daytime. The camera has the following parameters: minimum UV sensitivity: 31018 W/cm2, minimum discharge detection: 1.3 pC at 10 m, minimum RIV detection: 10.7 dBµV at 10 m. The camera uses detectors that simultaneously record images in two bands: UV (in the range from 250 nm to 280 nm) and visible. The measurement principle is based on counting the number of PD using an UV radiation sensitive matrix, and imposing the image on the visible picture of the tested object, which is obtained by using standard CCD matrix that is sensitive to visible light. After one image is imposed to another, the precise image of PD intensity is obtained, while the remaining part of the picture represents the picture in visible light. The lens and optical filter that are used in the camera have been optimized to illustrate the PD effect and its surroundings in the visible light. During measurements, the recorded pictures were displayed directly on a color LCD screen, and then recorded on the SD memory card as images.

The object studied – a long rod HV insulator (Fig. 1), was powered by an assay system enabling continuous adjustment of the applied voltage in the range from 0 to 60 kV. The power supply consisted of a control panel and testing transformer. The control panel (Fig. 2) consisted of the following elements: autotransformer, overcurrent protection and a digital voltmeter to measure the voltage value. The voltage regulated by the autotransformer was forwarded to the primary winding of the single phase testing transformer of type TP60, rated with 220/100000 V/V. From its secondary winding through the water resistor used for limiting the short-circuit current the tested long rod insulator was powered. In experiments a standard control panel which allows for manual adjustment of the voltage was applied.

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The number of counts, associated with corona discharges occurring on the long rod insulator was registered, while the supply voltage value was decreased. The performed analyses consider estimation of dependency of the voltage value on the number of discharges, generated on the object. A single measurement, for each voltage value, lasted for 3 minutes.

Fig. 1. The object under study and UV camera for measurement of optical signals

Fig. 2. The view of the applied control panel

3. Analysis of gathered signals

Example result showing optical signals represented by count number registered by the UV camera during one trail by which the supply voltage was set to 0.82%Up (16.8 kV) is presented in Fig. 3. The dots in the figure depict the particular values measured by the camera. The bold line depicts the arithmetical mean over the data.

Fig. 3. Example of registered count number values registered during experiment

where the supply voltage value was equal to 0.82%Up

In Fig. 4 the count number values gathered from all performed trails are presented in dependence from the supply voltage value. One can recognize a characteristic exponential growth of the cont number with accordance to rising voltage value. This dependence was confirmed by regression analysis using Gauss-type model given by eq. (1). In Fig. 4 we can also recognize two outliers: one in the positive and the other in the negative direction. These values were taken in the analysis, but had not a significant impact on the achieved result.

The applied Gauss-type model is given by eq. (1). The estimates were calculated using a data set containing arithmetical means over all data samples gathered for each voltage value. The gathered result is depicted in Fig. 5.

2

exp)(

C

Bt

AtGaussModel (1) where: t - time, independent quantity, min, A,B,C – model coefficients, which were estimated in the regression analysis.

Fig. 4. Spectra of UHF band for 28 kV: a) background; b) PD spectra

The values of model coefficients were estimaed by means of the

Least Squares method. The estimates and the 95% confidence bounds are given in Table 1. Tab. 1. The estimated coefficient values

Coefficient name Estimated value 95% confid. bounds

A 175900 67970; 283800

B 51.75 46.28; 57.23

C 12.25 10.29; 14.22

In order to evaluate the goodness of the estimated model

parameters statistic measures were calcutated, as given in Table 2. The SSE - summed square of residuals value, measures the total deviation of the response values from the fit to the empirical response values. A value closer to 0 indicates that the model has a smaller random error component, and that the fit will be more useful for prediction. In our case this value is high what indicated that the model is not appropriate for prediction purposes. The R2 value measures the variation of the data. This value is the square of the correlation between the response values and the predicted response values. A value close to one indicate that a greater proportion of variance is accounted by the model. In out case the fit explains 99.86% of the total variation in the data about the average. The RMSE – root mean square error, is the standard error of the regression. Similar to SSE this value if closer to one indicates a more usful model for prediction.

66 Measurement Automation Monitoring, Mar. 2015, vol. 61, no. 03

Tab. 2. The estimated goodness parameter values

Name of the goodness parameter Value

2~ t tt yySSE 1.07e+7

2

22

~1

t tt

t tt

yy

yyR 0.9986

mnyyRMSE t tt /~ 2 619

where: ty - empirical data registered at time t, ty~ - estimated data

at time t, ty - mean over empirical data, n – number of data points

(n=31), m –number of coefficients (m=3), where n-m is the residual degrees of freedom.

Fig. 5. The measurement data in form of arithmetical mean values and the estimated

Gauss-type model

The next step in the analysis performed, regarded determination

and the stochastic nature of the considered phenomena. Each data set gathered from particular trails was subjected to normal distribution analysis. The histograms were calculated and then the probability distribution was estimated. For this purpose the same Gauss-type model was applied in this analysis with some modifications regarding the coefficient values, as given in Table 3. The equation (1) was modified to reduce the number of coefficient. The new parameters which were estimated in the regression analysis, was the µ and σ, which were different for each dataset. For estimation the MVUE (minimum variance unbiased estimator) algorithm was applied. Tab. 3. The coefficients calculated in the dataset distribution analysis

Name of the coefficient and its form

π2

1

A B 2C

The goodness of the applied model was evaluated using the square of the correlation coefficient r, the so called determination coefficient (2).

2

22

2

~~

~~

i i ii

i ii

yyyy

yyyyr , (2)

where: r - value of the correlation coefficient, iy~ - estimates of the regression function, y~ - average over estimates iy~ , iy – empirical data, iy - average over empirical data iy , i = 1..31 – number of samples.

Two examples of the achieved results depicted histograms and

the model estimated through regression are presented in Fig. 6-7. First example depict a bad correlation and the second one a good correlation. In Fig. 8 all determination coefficient calculated for all datasets are presented. Basimg on the results it was stated that despite good correlated trend (Fig. 5), not always are the particular data samples normally distributed (using Normal Distribution).

Fig. 6. Example graph showing histogram and estimated probability distribution over

dataset recorded during trail by constant supply voltage equal to 16.8 kV

Fig. 7. Example graph showing histogram and estimated probability distribution over

dataset recorded during trail by constant supply voltage equal to 32.8 kV

Fig. 8. Example graph showing histogram and estimated probability distribution over

dataset recorded during trail by constant supply voltage equal to 32.8 kV

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4. Conclusions

PD detection and localization is presently one of the fundamental methods used in diagnosis of HV insulation systems. In this paper research works regarding PD occurring on long rod insulator made of ceramic are presented. The optical method was used for detection and localization of corona discharges. Optical signals in the UV range were registered with DayCor camera. The measurements were performed under laboratory conditions, complying with the required standards and measurements methodologies.

The analysis results depict number of counts that are measured with the UV camera and are connected to UV photons emitted by PD occurring in the vicinity of the object under study. Dependency of the supply voltage on the count number was investigated. The gathered relations were evaluated using statistical methods. Stochastic nature of the phenomena was confirmed by mathematical regression using normal distribution. Furthermore, a significant dependency of rising voltage on the number of PD was also confirmed by mathematical modeling. Although the designed model fits well to empirical data, the gathered correlation coefficient values are not always high. Optimal correlation values, such exceeding 0.65, were calculated for the following voltage values: 26.4, 30.4, 32.8, 37.6 kV.

The presented results will be applied in further studies where optical spectroscopy method will be used for detection of PD occuring on the long rod HV insulator. The following experiments will be performed only for those voltage values, for which the highest correlation was calculated and where the voltage-count number dependance is linear.

The work was co-financed from funds of the National Science Centre (NCN) as

part of the OPUS program, project no.: 2013/09/B/ST8/01736.

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_____________________________________________________ Received: 24.01.2015 Paper reviewed Accepted: 02.02.2015

M.Sc. Ireneusz URBANIEC He received M.Sc. degree in electrical engineering in 2000, from the Opole University of Technology, Poland. He is currently a Ph.D. student in electrical engineering at the Opole University of Technology. His research works are focused on detection and identification of partial discharges occurring on high voltage insulators. e-mail: i.urbaniec@interia.pl

Ph.D., D.Sc. Paweł FRĄCZ He received the M.Sc. degree in electrical engineering in 1999, the Ph.D. degree in electrical engineering in 2006, and the doctor science degree in 2011 from the Opole University of Technology, Poland. He is an associate professor and research fellow at the Opole University of Technology, Poland. His research interests embody spectrophotometry and acoustic emission studies in the application for diagnosis of electric power devices. e-mail: p.fracz@po.opole.pl