Robust classification of partial discharges in transformer insulation

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Robust classification of partial discharges in transformer insulation based on acoustic emissions detected using fiber Bragg gratings Srijith Kanakambaran, R Sarathi and Balaji Srinivasan  Abstract— Incipient discharges formed due to corona activity, surface discharge and particle movement in transformer insulation are identified based on acoustic emission signals captured using fiber Bragg grating sensors and analyzed in the frequency domain. To improve the SNR of these signals, the use of adaptive line enhancement based technique is systematically explored through simulations and the associated parameters are optimized. The noise filtered spectra analyzed through ternary diagrams suggest the possibility of classifying the discharges which are further validated using appropriate classifiers. Experimental comparison of discharges generated in different oil media like mineral oil, nanoparticle-dispersed mineral oil, ester oil and nanoparticle-dispersed ester oil reveals that the discharge characteristics are similar across multiple media and the classification holds good. Index Terms— Partial Discharge, Fiber Bragg Gratings, Transformer insulation, Acoustic Emission, Classification, Nanoparticle dispersed oil

I. INTRODUCTION

I

NSULATION breakdown is one of the primary causes for failure of a transformer, which is a critical component of the smart electric grid [1]. As such, there is a definite need to continuously monitor the transformer insulation to check for any signs of degradation. Partial discharges (PD) which occur in localized regions of the insulation serve as early markers of degradation of the insulation [2]. Various methods based on electrical, electromagnetic, chemical and acoustic techniques have been demonstrated to detect the occurrence of PD [3], [4]. Although the sensitivity of acoustic method is not as high as the electrical method, this method of PD detection is quite beneficial as it offers the possibility of in-situ monitoring and also can potentially perform localization of PD [4]. While conventional acoustic emission monitoring is performed with the help of piezoelectric sensors mounted

Srijith Kanakambaran is a Project Associate in the Department of Electrical Engineering, Indian Institute of Technology, Madras, India 600036 (e-mail: [email protected]). R Sarathi is a Professor in the Department of Electrical Engineering, Indian Institute of Technology, Madras, India 600 036 (e-mail: [email protected]). Balaji Srinivasan is a Professor in the Department of Electrical Engineering, Indian Institute of Technology, Madras, India 600 036(e-mail: [email protected]).

externally on the tank walls, they suffer from electromagnetic interference (EMI) issues and can only pick up a narrow band of frequencies due to their inherent resonances. The signals in such sensors are prone to EMI while coupling from the sensor head to the measurement unit, thereby resulting in false alarms. On the contrary, acoustic sensors based on optical fibers are EMI-free due to their dielectric construction and can be installed inside the transformer tank walls in a minimally invasive manner [5]. These fiber optic sensors may be broadly classified as phase modulated or wavelength modulated. The interferometric techniques, which are phase-encoded achieve high sensitivity with the use of long lengths of sensing fibers making the sensor bulky and susceptible to fringe fading problems due to random polarization fluctuations. On the contrary, the wavelength modulated fiber Bragg grating (FBG) based sensors are shown to be excellent candidates for acoustic emission sensing [6]. The advantage of adoption of FBG sensor is that it can act as multi-parameter sensor system by not only identifying the PD event, but also measuring the local temperature variations. In addition, the sensing technique can be used in harsh environments including high energy irradiation zones and in electromagnetically intense zones. In such sensors, the transduction mechanism is quite robust as the sensing information is encoded in wavelength of the optical signal which is impervious to noise. Another attractive feature which makes them highly suitable for PD detection is the fact that these sensors can capture signals over a wide range of frequencies typically covering the entire possible PD spectrum as they are agnostic to frequency of the acoustic emissions. Moreover, unlike piezoelectric based acoustic emission sensors, FBG sensors do not have any inherent bandwidth limitation. They can pick up acoustic emissions with frequencies ranging from DC to few MHz [7]. In recent times, nanoparticle-dispersed mineral oil and ester oil are gaining popularity as potential insulants in transformers replacing the conventional mineral oil [8]. With the advent of nanoparticles and its adoptability to recent technologies, it is essential to understand the fundamental properties of the insulant which can act as a good reference for the insulation designer. The impact of differences in viscosity and the presence of nanoparticles on the spectral signatures of the acoustic emission signals need to be ascertained. As such, one of the key objectives in our work is to understand the acoustic emission signals due to partial discharges in a variety of

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Sensors-23167-2018 insulants viz. mineral oil, nano-mineral oil, ester oil, and nano-ester oil. As mentioned above, several techniques have been proposed as well as demonstrated by researchers to detect and localize PD events in a power transformer. On the other hand, identifying the type of PD is an equally challenging task [9]. The criticality of damage due to insulation degradation can be assessed based on the type of PD activity including corona discharge, particle movement, and surface discharge. It has been reported previously that different types of discharges emit acoustic signals with distinct spectral features [9], [10]. Our work is focused on identifying these wideband signals using a frequency-agnostic FBG sensor. In this paper, we present a simple technique of classifying discharges based on the acoustic signals captured using FBG sensors. The fundamental studies carried out here provides one to understand the feasibility of the developed sensor for identification of incipient discharges and its characteristics so that they can be extended for use in real transformers. Moreover, the adoption of FBG sensors for identification and classification of a variety of incipient discharges that occur in the transformer insulation is a unique contribution of this work. The methodology provided can enable the researchers and transformer industries to initiate further research on the use of FBG sensors. We demonstrate that we are able to classify the different types of partial discharges through their unique spectral signatures. Such a classification is based on identifying the relative spectral energy content in different bands of frequencies and visualizing them on ternary diagrams. We validate our claim of classification through the use of appropriate classifiers. In addition, the study is validated in a variety of insulating liquids viz. mineral oil, nano-mineral oil, natural ester and nano-ester oil.. II. DATA ACQUISITION AND PROCESSING OF ACOUSTIC SIGNAL As mentioned above, the acoustic sensor used in this work is an optical fiber based fiber Bragg grating (FBG). FBG is a periodic perturbation spanning over a limited length in the core refractive index of an optical fiber. When broadband light is incident on the grating, it tends to reflect a narrow band of wavelengths centered around a unique wavelength known as Bragg wavelength [6]. The Bragg wavelength changes with strain or temperature variations experienced by the FBG. Therefore, as discussed previously, the sensing information is encoded in the wavelength in such sensors. A significant challenge in using FBG based sensors for sensing acoustic emissions is the effective conversion of wavelength encoded information to intensity variations. A few techniques which have been demonstrated by researchers include using tunable narrow band lasers [11], edge filters [11],[12], arrayed waveguide gratings [14] and interferometric methods [15]–[17]. In this work, the tunable laser based interrogation of FBG has been employed for detection of acoustic emissions generated due to discharges. As shown in Fig. 1, a tunable laser source (TLS) is biased to the reflection slope of the FBG. Any change in Bragg wavelength caused due to variations in strain/temperature is converted into an

equivalent change in optical intensity as the reflectivity of the FBG will be different for different levels of perturbation as shown in Fig. 1 (b). These optical intensity variations are detected using an optical receiver resulting in an equivalent variation in voltage. Another point to be noted is that the Bragg wavelength of FBG sensors are sensitive to both strain and temperature. However in our technique, we have the flexibility to tune the laser used for interrogating the grating prior to the measurement to compensate for any temperature-induced drift in Bragg wavelength. Moreover, the temperature changes typically occur at a much slower rate (few Hz) compared to acoustic emission (50 - 350 kHz). As such, any temperature variation during the measurement of acoustic emission signals can be neglected. P Tunable laser source

1

2 FBG 3

Receiver

B



(a) (b) Fig. 1. (a) Tunable laser based FBG interrogation configuration (b) Illustration of wavelength to intensity conversion in tunable laser configuration

The acoustic emissions generated due to partial discharges are typically so feeble that the resultant detected optical signal is in the sub-nW range. Hence, an appropriate technique to improve the SNR of the detected signal needs to be implemented. Previous work on detection of acoustic signals using FBG has shown that adaptive line equalization (ALE) is a good choice for improving the SNR [13], [18]. However, ALE is known to adapt to a specific frequency tone. Since partial discharge signals are wideband in nature typically extending over tens of kHz [19], it was necessary to first test the suitability of ALE for application in partial discharge signals. As discussed previously, in order to ascertain the severity of partial discharge events it is necessary to identify the type of insulation defect. There have been several reports on the classification of PD based on time domain [20], frequency domain [21], [22] , and time-frequency analysis [23]–[25]. Of these, the time-frequency analysis is a powerful tool to classify discharges as well as localize them in an accurate manner. However, since we are focused on only the classification of partial discharges in this work we rely on the frequency domain classification techniques. It is to be noted that the computational effort required for frequency domain classification is much lesser compared to that based on timefrequency analysis. Frequency domain analysis visualizes the PD pulse in the frequency domain and looks for descriptors like frequency at peak magnitude, median frequency, percentage signal energy in frequency band etc. One approximate method of classifying the various partial discharges is through a ternary plot, which provides a clear

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Sensors-23167-2018 visual indication of the frequency content in the discharge [10]. We choose this method to demonstrate the difference in the spectra of acoustic signals for different types of partial discharges. However, the ternary plot does not provide precise quantification of the discharge classification as it relies on the user to decide on the classification of PD. Such decisionmaking capability is achieved in our work through the help of classifiers. Various classifiers based on neural networks, Principal component analysis (PCA), support vector machines (SVM) [26] and sparse representation [27] are used for identifying the type of discharge based on the descriptors. III. CLASSIFIERS ADOPTED FOR CLASSIFICATION OF DISCHARGES

In this paper, we have investigated the use of three different classifiers which are discussed below: A. Decision Tree A decision tree is grown in a recursive manner by partitioning the training samples into subsets [28]. If all the samples at a node t belong to the same class y, then it is considered as a leaf node yt. On the other hand, if the samples at a node belong to more than one class, an attribute test condition is used to partition the data into smaller subsets. The attribute we have used for our studies is the relative fraction of normalized spectral energy in defined frequency ranges. We have divided the entire acoustic spectrum into three suitable regions and calculated the spectral energy in each of these ranges. This is normalized with respect to the total energy and is used as the attribute for our classification studies. A commonly used condition is to evaluate the Gini's diversity index given by n

Gini(t )  1    p(i | t )

2

(1)

i 1

where p(i|t) represents the fraction of the samples belonging to class i at a node t and n represents the number of classes. The best split position corresponds to the one that produces the smallest Gini's diversity index. B. K-Nearest Neighbour (KNN) KNN is a non-parametric classification algorithm which classifies new samples based on a similarity measure [29]. The most commonly used similarity measure is the Euclidian distance between the samples. If p and q represent the coordinates of two samples in the training data set, the Euclidian distance is calculated as follows d pq 

n

 q i 1

 pi 

2

i

(2)

where n is the dimension of the feature vector. As described previously, the coordinates p and q represent the normalized spectral energy in each of the defined frequency ranges. In this algorithm, a sample is assigned to the class which is common among its K closest neighbors measured by the distance function. C. Support vector Machine (SVM ) SVM is defined by a hyperplane which classifies the data

into different sets [30]. The algorithm relies on finding the optimal hyperplane defined by  T x   0  1 which gives the largest minimum distance to the training set x. In other words, it tries to maximize the margin between the training data. The optimization problem can be mathematically represented as ||  || 2 subject to y  T x    1i (3) min i i 0 0 , 2 where yi represents each of the labels of the training samples xi. As discussed before, the feature used for classification in our work is the relative fraction of spectral energy in the defined frequency ranges. The acoustic spectrum under study is divided into three suitably selected frequency ranges. The energy in each frequency range is evaluated and normalized with respect to the total energy. Thus for each training data, we have a set of three parameters which represent the normalized spectral energy in each of the defined frequency ranges. The classifier models are trained using these parameters.



IV.



DESCRIPTION OF TESTBED FOR PARTIAL DISCHARGE STUDIES

As discussed in the previous section, our approach is aligned towards capturing the acoustic emissions from partial discharges using FBG sensors. For the purpose of demonstrating classification of PD using FBG sensors, three different commonly occurring PD configurations were considered for the study namely corona, surface discharge and discharge due to particle movement. Corona usually occurs from sharp metallic protrusions on the windings. The pressboard insulation layers between the windings are prone to the occurrence of surface discharge type PD. Small metallic particles which are suspended in the oil can be sources of discharge due to particle movement [10]. Particle movement causing PD activity is highly random but can cause charge transfer between particle and the electrodes. It has been reported previously that the particle movement under AC and DC voltages are different [31]. In this study, we have made an attempt to obtain the spectral fingerprint of all the above discharge configurations. The above configurations were realized in a test cell as shown in Fig. 2. The test cell used for our experiments consists of a hollow cylinder (5 cm diameter and 7 cm height) made of Perspex and two metal discs at the top and bottom. The FBG sensor which is 3 mm long is installed in the inner surface of the cylindrical structure, 2.5 cm radially away from the central electrode position. The facility for placing the suitable electrode configuration is provided at the center of the test cell. The optical fiber with the sensor FBG is held under tension by pulling it from both ends and then placed over the inner wall of the test cell. A room temperature curable epoxybased adhesive is applied over the region where the sensor is present. The fiber is held under tension for another 10 - 15 minutes till the adhesive gets cured and the fiber is taken out through a hole bored on the top metal disc of the test cell. The specific insulant oil is filled through the hole bored on the top metal disc.

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(a)

(b)

Trimethyl Ammonium bromide (CTAB) surfactant was added to the transformer oil and mixed using a magnetic stirrer for 30 minutes. Further, the heated nanoparticles were added to the oil mixture and mixed using the stirrer. To disperse the nanoparticles, the colloidal solution was sonicated for 3 hours using Sonics Vibra-cell sonicator (500 W, 20 kHz). After this process, the nanoparticle dispersed transformer oil was left idle for 2 hours to remove any micro bubbles that could have Transformer Oil Surfactant

Nanoparticles

(c) Fig. 2. Test cell showing the configurations of (a) corona (b) surface discharge and (c) discharge due to particle movement

For generating corona activity, a needle-plane configuration is used. The needle electrode is connected to high voltage and the bottom electrode is connected to the ground. To obtain sustained corona, a thin oil impregnated paper insulation is placed on bottom electrode to avoid corona discharge followed with breakdown. The electrode gap is maintained at 10 mm. The needle electrode tip had a radius of curvature of 50 µm. The ground bottom electrode is made of stainless steel material with 5 cm diameter. To generate surface discharges, a 1.2 mm thick pressboard layer was kept sandwiched between the IEC(b) high voltage electrode and the bottom ground electrode. The electrodes are placed on top of pressboard material, to enhance tangential electric field. For generating particle movement type discharge, a sphere electrode of 4 cm diameter is used along with a spherical particle of 1.5 mm diameter in the electrode gap. The gap distance between two electrodes maintained at 10 mm. The bottom electrode is made with a slight curvature (concave) to confine the conducting metal particle. The top spherical electrode was connected to high voltage and the bottom electrode to the ground to generate quasi-uniform electric field. An important factor to be considered in such studies is that the test cell size can potentially provide resonances that corrupt the spectral signatures due to the partial discharges. For our test cell size of 5 cm diameter and 7 cm height and the acoustic velocity of ~1400 m/s in the insulation oil, the resonance frequency corresponds to ~10 kHz. The spectral region considered in our analysis is 30-350 kHz i.e., much higher than the acoustic resonances of our test cell. The FBG sensor is pasted on the inner wall of the test cell and it directly picks the strain variations that are induced on the wall due to the acoustic wave. The PD configurations mentioned above were studied in four types of oils namely mineral oil, nanoparticle dispersed mineral oil, ester oil and nanoparticle dispersed ester oil. The schematic illustrating the preparation of nanoparticle dispersed oil is shown in Fig. 3. Titania and silica nanoparticles were used in mineral and ester oils respectively. The nanoparticles were initially heated at 150C for 8 hours in a hot air oven to remove moisture content. The required quantity of Cetyl

Magnetic Stirrer (30 min)

Magnetic Stirrer (30 min)

Sonication (3 hrs)

Nanoparticle dispersed Transformer oil

Hot air oven 1500C (8 hrs)

Fig. 3. Test cell showing the configurations of (a) corona (b) surface discharge and (c) discharge due to particle movement

Fig. 4. Experimental setup for detection of partial discharges

been formed during the ultrasonication process [32]. The experimental setup for capturing discharges is shown in Fig. 4. The FBG sensor is pasted on the wall of the test cell. A tunable laser source (Optilab, TWL-C-M) is biased at the reflection slope of the grating. The optical power reflected from the FBG sensor is directed towards an APD-based inhouse designed detector board through a circulator. The high AC voltages were produced by using high voltage amplifier (Trek, 20/20C) with input to it from a function generator (Agilent, 33250A). The generated voltages were measured using capacitance divider. The voltage was applied to the test at the rate of 200V/s. A UHF sensor is also included in the setup to be used as a trigger for data acquisition. V. RESULTS AND DISCUSSION In the present study, the corona discharge activity, surface discharges and particle movement initiated discharges under AC voltages are identified through fiber Bragg gratings. The detection sensitivity of the FBG sensor is sub-hundred pC. The actual charge magnitude for each PD configuration has not been measured due to unavailability of IEC 60270 test facility. However, it has been verified that the acoustic signal amplitude is larger for higher charge magnitude using separate experiments. The typical time domain signal acquired by FBG sensors for each type of discharge and its corresponding FFT are shown in Fig. 5. The graphs indicate a time delay between the signals detected by the FBG and UHF sensors. The delay may be attributed to the finite time taken by the acoustic

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Sensors-23167-2018 emission to travel from the PD source to the wall of the test cell compared to the instantaneous detection of UHF signals that travel at the speed of light. It is observed that the energy content of the signal is high in the band 250-350 kHz for corona discharge generated signal. For the surface discharge generated signal, the dominant frequency occurs at 150 kHz. The FFT analysis of AE signal generated due to particle movement have energy content in the range of 50-350 kHz. In this type of discharge, the acquired charge relies on the phase

(a)

to test the applicability of ALE for wideband signals that are typical in partial discharges. A. Simulation studies on ALE A detailed simulation study was adopted for evaluating the performance of ALE technique for filtering wideband signals. The time domain signal and its corresponding frequency spectrum chosen for the study is shown in Fig. 6. Two sets of signals with SNR 3 dB and 9 dB before ALE processing were considered. The filter order (N) and the step size (µ) were varied and the output SNR of the processed signal was evaluated. The variation of SNR with filter order and step size are shown in Fig. 7 and 8 respectively. It may be observed from Fig. 7 that the highest SNR improvement was obtained for a filter order of 32. At lower orders, the filtering process does not have enough terms to reconstruct the given frequency range, leading to lower SNR in the output. At higher order values, the SNR drops down due to inclusion of more high

(b)

Fig. 6. Simulated signal chosen for verifying the suitability of ALE along with its frequency spectrum

(c) Fig. 5. Time domain and frequency domain traces of the signals captured by FBG sensor for (a) corona (b) surface discharge and (c) discharge due to particle movement.

at which the particle is in contact with the electrode and the magnitude of discharge is decided by the size of the particle which collides to the upper or to the bottom electrode. In the present study, it is observed that the particle initiated discharge have significant spectral components in frequencies as low as 50 kHz. The sampling rate of the oscilloscope used for data acquisition (25 MHz) is not sufficient enough to resolve the exact structure of the short electromagnetic pulse (~ few ns) detected by the UHF sensor. Adaptive Line Enhancement (ALE) has been shown to be a potential technique to improve the SNR of the captured signals. ALE technique attempts to enhance sinusoidal signals submerged in noise by de-correlating and filtering the noise through an adaptive filter. The weights of the N-order adaptive filter h[n] are recursively updated using the Least Mean Square algorithm as shown below h[n  1]  h[n]  e[n]x[n] (4) Here µ represents the step size, x[n] represents the filter input and e[n] represents the error signal. However, we need

Fig. 7. Variation of SNR with filter order

Fig. 8. Variation of SNR with step size

frequency terms which actually correspond to noise. Hence an optimum value of filter order of 32 is chosen for further investigations. Fig. 8 indicates that the SNR improvement is irrespective of the step size considered. Smaller value of step size ensures better accuracy. Hence a step size value of 0.01 was chosen for further studies.

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(a)

(b)

(a)

(b)

(c) Fig. 9. Simulated signals in time domain and frequency domain in different spectral ranges of (a) 0 - 200 kHz (b) 200 - 400 kHz and (c) 400 - 600 kHz.

Since the performance of the ALE technique was satisfactory over the narrowband frequency range, further efforts were directed towards testing the performance of the technique over the wide band of frequency in which PD occurs. For this purpose, three different narrowband signals in different spectral ranges were considered for the study. Each of the signals are defined to have spectral content in 0 – 200 kHz, 200 – 400 kHz and 400 – 600 kHz respectively, as shown in Fig. 9. The signals were added with additive white Gaussian noise and then filtered using ALE technique. Ternary diagrams are constructed by considering the above three frequency ranges as the axes and the coordinates of signals are calculated based on the normalized relative spectral energy in each of the three axes. The signals before and after ALE processing are visualized in the ternary diagrams shown in Fig. 10. It may be observed from Fig. 10 that ALE processing clearly improves the SNR of the signal thereby pushing it to the corners of the ternary plots. A sample time domain signal obtained from corona

(c) Fig. 10. Ternary diagrams showing signals before (red) and after (blue) ALE processing for each frequency range (a) 0 – 200 kHz (b) 200 – 400 kHz and (c) 400 – 600 kHz.

Fig. 11. Ternary diagrams showing signals before (red) and after (blue) ALE processing for each frequency range (a) 0 – 200 kHz (b) 200 – 400 kHz and (c) 400 – 600 kHz.

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Sensors-23167-2018 discharge experiment along with the ALE processed data are shown in Fig. 11. It is clear from Fig. 11 (b) that the ALE clearly enhances the SNR while preserving the spectral features. B. Visualization of signals in ternary plot The ALE filtered traces for all types of discharges are further analyzed through ternary diagrams as shown in Fig. 12 (a). It was found that there is a significant overlap between two types of discharges - surface discharge and discharge due to particle movement. This is attributed to the fact that the major share of the energy in the spectral domain lies in the 0200 kHz frequency range. In order to separate out the two types of PD, we chose a smaller frequency step size and results are as shown in Fig. 12 (b). We have considered only the spectral range from 30 kHz for our analysis since there is negligible spectral content of interest below 30 kHz and is implemented in our receiver unit through a high pass filter with cutoff frequency at 10 kHz. The results indicate that the multiple trials of discharges are quite repeatable and they form separate clusters which suggest possibilities of classification. An interesting aspect to observe is the larger spread in the signals obtained in PD due to particle movement. We suspect that this is due to the variations in the force with which the particle makes contact with the electrode thereby generating a slightly different set of frequencies during each trial. In order to verify whether the spectral features have any dependence on the distance between the PD source and the FBG sensor, the experiments were repeated by placing the electrode 1 cm away from the center of the test cell. This distance is comparable to the wavelength of the acoustic waves in oil for the given frequency range. The results of the

(a)

same are presented in Fig. 13. It may be observed that the spectral characteristics do not change with a change in the distance between the PD source and the sensor.

Fig. 13. Ternary diagram showing the characteristics of different PD in mineral oil with electrode 1 cm offset from the center of the test cell.

Recent studies have shown that the addition of Titania (TiO2) nanoparticles to transformer oil can significantly improve the breakdown strength and thermal conductivity [33]. Swati et. al. have shown that optimized performance of nano-mineral oil is obtained using 10 mg of TiO2 and 0.1 mg CTAB (Cetyl Trimethyl Ammonium Bromide) surfactant in 200 ml of oil sample [32]. We investigated the acoustic PD characteristics in such nano transformer oil prepared using the procedure outlined in [32]. The results obtained for the different types of discharges is shown in Fig. 14 (a) (ii). Another environment friendly alternative to the petroleum based mineral oil to be used in transformers is natural ester oil [34]. We have explored the acoustic PD characteristics in ester oil as well as nanoparticle dispersed ester oil. Nano-ester oil is prepared using 5 mg of SiO2 and 0.1 mg CTAB in 200 ml of oil sample. The results obtained are plotted in Fig. 14 (a) (iii) and (iv) respectively. It is observed that regardless of the oil media used for the experiments, the spectral features of each of the discharges cluster around specific regions. The PD inception voltage for each of the configurations is mentioned in Table 1. The experiments were also repeated at an excitation voltage 20% higher than the inception voltage and the results were similar are shown in Fig. 14 (b) (i) - (iv). This confirms that our approach of classification based on spectral features is valid over multiple platforms. TABLE I INCEPTION VOLTAGES FOR DIFFERENT PD CONFIGURATIONS (IN KV) Configuration Mineral NanoEster Nanooil mineral oil oil ester oil Corona Surface discharge Discharge due to particle movement

(b) Fig. 12. Ternary diagram showing the characteristics of different PD in mineral oil with a step size of (a) 200 kHz (b) 100 kHz.

10.6 9.2

12.3 10.4

11.4 10.6

13.1 11.3

8.6

10.8

9.8

11.6

C. Studies on Classifiers Although the results presented above clearly show clustering of signals corresponding to each type of discharge, a quantitative information regarding the effectiveness of classification needs to be specified. In order to quantify the ability to classify the different types of PD, we explored the

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Sensors-23167-2018 use of classifiers [30],[31]. Three sets of data points of 100 samples each belonging to three different classes were generated as shown in Fig. 15. We investigated the performance of three classes of classifiers - decision trees, K-

(i)

analyzed and the accuracy of classification obtained is summarized in Table 2. SVM based classifier is found to perform slightly better than the other two classifier models. It is also observed that the classification accuracies are slightly

(ii)

(iii)

(iv)

(a)

(i)

(iii) (iv) (b) Fig. 14. Ternary diagram showing the characteristics of different PD in (i) mineral oil (ii) nano-mineral oil (iii) ester oil (iv) nano-ester oil at (a) inception voltage and (b) voltage 20% higher than inception voltage.

Configuration Decision tree KNN SVM

(ii)

TABLE II ACCURACY OF CLASSIFIERS OBTAINED USING ANALYSIS OF EXPERIMENTAL DATA Mineral oil Nano-mineral oil Ester oil Vinc 1.2 Vinc Vinc 1.2 Vinc Vinc 1.2 Vinc 95.38 97.27 97.02 98.01 96.62 98.53 95.38 97.56 97.68 98.34 96.33 98.53

Nano-ester oil Vinc 1.2 Vinc 96.27 97.99 96.56 98.33

95.71

96.71

97.89

97.72

nearest neighbour (KNN) and Support Vector Machine (SVM). For the KNN classifier, the number of nearest neighbours is set to 10 and the distance metric used is Euclidian. The classifiers mentioned above were trained using the data through the cross-validation approach with 5 folds. The spectral data obtained through experiments was then processed through the classifier model. The overall accuracy of each of the classifier model was calculated as the average of the ratio of the number of true positives (TP) to total number of samples over all three classes. The data obtained in mineral oil, nano-mineral oil, ester oil and nano-ester oil were

Fig. 15. Simulation data belonging to three classes used to train the classifiers.

98.34

96.67

98.56

98.52

higher for the signals obtained at 1.2 times the inception voltage. This may be attributed to the comparatively higher SNR for the signals generated at the higher excitation voltages. The acoustic signals emitted due to the PD configurations are shaped by the immediate environment around which they are emitted [4]. In fact, this is the basis for all our experiments which have been performed in simplified test cell models at a laboratory scale – although the test cell is small, the local environment is not very different from the real conditions. Therefore, we believe that the spectral signatures we obtain are not very different from the real environment. Of course, we need to prove this with the help of actual experiments. But, the present work is focused only on the methodology of classification. The important part of our work is to understand the feasibility of the study and to obtain typical spectral characteristics of PD initiated pattern in liquid insulation obtained from different feedstock. Admittedly, further work need to be carried out in assessing the variation of spectral characteristics due to occurrence of PD in the presence of core, winding, barrier and oil as in real transformer, which is one of the fundamental limitation at this stage. The above results suggest that the proposed FBG-based acoustic emission sensor system is capable of distinguishing

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Sensors-23167-2018 the different types of PD events based on their frequency content. The ability to withstand harsh environment coupled with having smaller footprint and wide bandwidth make these sensors suitable for use in real transformers. Although the sensitivity of acoustic measurement is not as good as the electrical/UHF measurement, the technique presented here is found to be quite robust. Even though ageing of the insulation may create some changes in the discharge characteristics, the technique of classification will still hold good provided there are no drastic changes in the spectral characteristics. As such, the time-invariance nature of the spectral features is an aspect which could be explored further.

[5]

[6] [7]

[8]

[9]

VI. CONCLUSION In this paper, we demonstrate a robust technique for classifying partial discharges (PD) in a controlled environment based on acoustic emissions captured using FBG sensors. Further efforts are underway to test the proposed technique in a real transformer environment. Frequency domain analysis of acoustic signals reveal that there is a characteristic spectral signature associated with different types of PD including corona discharge, particle movement and surface discharge. Adaptive line enhancement (ALE) based noise filtering technique with carefully optimized filter order and step size has been demonstrated to improve the SNR of such spectral signatures. Ternary diagrams used to analyze the ALE filtered acoustic emission traces clearly indicate the possibility of classifying discharges, which is further confirmed through appropriate classifiers. Investigation of discharges in different insulant media such as ester oil and nanoparticle dispersed oils in addition to mineral oil indicate that the discharge characteristics are similar and the corresponding classification shows consistent behavior across all the insulants. Such a result along with our previous work demonstrating the localization of partial discharges [37] using a similar transduction mechanism go a long way in establishing FBGbased acoustic emission sensing as a strong candidate for realtime condition monitoring of transformers.

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

ACKNOWLEDGMENT The authors would like to acknowledge the financial assistance received from Central Power Research Institute, Bengaluru, India and Ministry of Human Resource Development, Government of India. One of the authors (S.K.) would like to acknowledge Bhuvaneswari C, Project Technician, IIT Madras for assisting the experimental studies.

[20]

[21] [22]

[23]

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10 Srijith Kanakambaran obtained his Ph.D. in 2018 from the Indian Institute of Technology Madras. He received his B.Tech degree in Electronics and Communication Engineering from Rajagiri School of Engineering and Technology, Kerala and M.Tech degree in Communication Systems from National Institute of Technology, Tiruchirappalli in 2009 and 2011 respectively. He served as an Assistant Professor in the Department of Electronics and Communication at Federal Institute of Science and Technology, Kerala from 2011 to 2013. His research interests include fiber Bragg gratings and optical fiber sensors.

R. Sarathi is a Professor and Head of High Voltage Laboratory, Department of Electrical Engineering, IIT Madras, Chennai, India. He obtained his PhD from Indian Institute of Science, Bangalore in 1994. His research areas include condition monitoring of power apparatus and nano materials.

Balaji Srinivasan obtained his Ph.D. in 2000 from the University of New Mexico, USA. He has been with the Indian Institute of Technology, Madras as a faculty in the Department of Electrical Engineering since 2004, currently as a Professor. His research interests span the development of active and passive optical components/subsystems for distributed fiber optic sensors and fiber lasers. He is a frequent reviewer for journal published by the Optical Society of America and IEEE. He presently serves in the Editorial Board of Optics Express and is one of the Directors of Unilumen Photonics Pvt Ltd, a fiber laser company incubated at the IIT Madras Research Park.