Preventive maintenance in the transformer is performed through a differential relay protection system, and it protects the transformer from internal and external faults. However, the Current Transformer (CT) in the differential protection system mal-operates during inrush currents. CT saturates due to magnetizing inrush currents and causes false tripping of the differential relays. Moreover, identification of tripping in protection relay either due to inrush current or internal faults needs to be diagnosed. For the above problem, continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the tripping in relay due to inrush or internal fault. The transformer’s internal fault leads to high breathing process in the transformer breather, never for inrush currents. During inrush currents, CT temperature is increased. Continuous monitoring of breather and CT of the transformer through thermal imaging and radiometric pixels detect the causes of CT saturation and differentiates maloperation. Hybrid wavelet threshold image analytics (HWT-IA) based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95% for identification of the false tripping of differential protection system of transformer.
The power transformer is an electrical device that works with an alternating current for long-distance power transmission. Transformers are used for several applications, such as increasing the voltage level of the secondary side output, distribution of power in industrial and residential areas, etc. The transformers require preventive and predictive maintenance to deliver an uninterrupted power supply. Faults in the transformers occur due to internal or external factors. Internal factors are composed of both electrical and mechanical faults. Electrical faults are due to winding failure and phase faults. Mechanical faults occur due to the breakdown in the cooling medium’s properties or while changing taps. Certain faults deteriorate the system with time causing insulation failure. In a transformer, oil is heated up to a high temperature in the internal part of the windings and causes the oil to expand, leading to a build-up of pressure in the conservator tank of the transformer. The transformer breather extracts moisture and dry air through the vent pipes of the conservator tank with silica gel crystals, which otherwise reduces the oil’s dielectric strength [
The breathing process intensifies during an overload or when an internal fault occurs since the rise of temperature in the conservator tank causes the oil to expand and contract faster. Under such conditions, the volume of air in-take increases which might eventually lead to early degradation of the silica gel crystals in the breather since there is comparatively a higher moisture ingress. Therefore, monitoring the temperature of the breather is also considered an essential step toward transformer maintenance. The protection system for the transformer is incorporated using differential relays [
Furthermore, excessive differential currents between the primary and the secondary of the transformer affect transformer operation and differential protection relays that perform the protection task of the transformer. The relays mal operate when false differential currents occur during CT saturation [
A higher magnitude of the second harmonic component present in the current waveform sampled by CT is generally used to differentiate inrush current from internal faults [
Hybrid Wavelet Thresholding based image analytics (HWT-IA) method for studying the false tripping of differential protection relay system due to internal faults and inrush current is proposed. Thermal image during tripping of differential protection system is analyzed based on CT saturation and different loading conditions and is used: To differentiate internal fault and in-rush in the transformer during runtime, under various loads. Continuous monitoring through the thermal image of CT in normal and saturation conditions. To detect tripping of differential protection relay system due to inrush or internal fault using the continuous monitoring of transformer breather and CT, through a thermal camera. Image analytics of acquired thermal images predict the reason for the tripping of the relay system. To predict the reason for tripping of protection relay through thermal image analytics, which in prediction needs various data such as line voltage, load current, CT, room and breather temperature, humidity, On-load tap changer position etc.,
Histogram analysis of the current signal is used for detection of maloperations in differential protection systems. Detecting the time difference between sudden changes occurring in the transformer wye-side current and differential current is proposed and avoids maloperation of transformer in substations. Degradation of transformer oil due to moisture ingress is protected through parallel plate capacitive moisture sensor fabrication in transformer breather and avoids moisture [
A supervised machine learning approach considering wind speed and signal sampled from current transformers in wind farms to distinguish normal, internal and external faults is proposed [
A 500 KVA, oil-cooled distribution transformer with a full load current of 666.68 A and 433 V on the low volt side is shown in
The thermal images of the breather and current transformer are acquired from a thermal camera and processed using Fluke SmartView software. The thresholding technique during discrete wavelet transform (DWT) de-noising plays a significant role in extracting the Region of Interest from the thermal images of the current transformer and breather. Moreover, DWT based adaptive thresholding is suitable for real-time applications. Ability to vary the threshold parameters is achieved by soft thresholding for removing noise. The residuals of the de-noised image at different decomposition levels are the target measurements, and hybridization of the thresholding techniques leads to improvised residuals in the de-noised image of the breather and CT terminal. Statistical parameters of the residuals of the decomposed image after wavelet transformation are considered for differentiation of inrush current and internal fault. The combination of wavelet transforms for hybrid thresholding discriminates the transformer breather image under fault conditions and the current transformer image during inrush current using a soft thresholding mechanism.
For the differentiation of the internal faults and inrush current, thermal images from the thermal camera are processed with different thresholding methods, and hybridization of thresholding leads to differentiation of the faults using statical pixel values.
In this paper, DWT based hybrid thresholding method is proposed. DWT is a frequency domain transformation technique where the process is initiated with a mother wavelet belonging to a family of wavelets such as Haar, Daubechies and Bior. The transformed image is a scaled and shifted version of the mother wavelet. The image is divided into sub-components named approximations that represent smoother sections of the image called low-frequency components and details; high-frequency components represent the edges in the image. The image is said to be decomposed when it is processed through successive low pass and high pass filters. After decomposition, thresholding is applied to enhance and de-noise the thermal images. In the proposed system, after DWT-based image decomposition, hybrid thresholding is applied for image enhancement.
Non-decimated DWT (NDWT) enhances the translation invariance by up-sampling the filter coefficients. NDWT is suitable for de-noising of images. The image’s resolution is reduced by half at each level of discrete wavelet transform and never in NDWT. In NDWT, spatial resolution becomes coarse at higher levels while the size is retained as in DWT. In the proposed system, after NDWT decomposition of the image, hybrid thresholding is applied for image enhancement.
Wavelet transform smoothens the thermal image [
In
Daubechies wavelet filter shows the scaling function and represents the greatest number of complex functions with few wavelet coefficients. Daubechies has a high degree of linearity in phase and smoothness in the decomposed image. Symlet filter is a modified form of the Daubechies wavelets, where there is an inclination towards symmetry of the wavelets. Coiflet filters increase the pixel averaging, differencing computations and vanishes the moment property [
Thresholding plays a vital role in de-noising the decomposed images. The choice of an optimal threshold value is considered the major decision in the process. They are classified as soft and hard thresholding techniques where hard thresholding sets the wavelet decomposition coefficients to zero when the value is lesser than a specified threshold and retains the old value, if the value is greater than the threshold, as per
In soft thresholding, a part of the high-frequency decomposition coefficients is lost beyond the threshold.
In
The type of threshold to be adopted depends on the signal to noise ratio and the noise distribution. In the proposed work, un-scaled white noise in the thermal image is reduced. The thresholding function of hard type abruptly falls to zero when the image is identified with noise and never suits for roughness estimation. Penalize high, medium and low thresholding is of hard type, and soft thresholding can also be applied for temperature-based analysis. Soft type thresholding gradually reduces the magnitude of the transform coefficients beyond the threshold; for the values lesser than the threshold, the function falls to zero. The expected output image magnitude is lesser than the source and undesirable at certain times. The Soft type thresholding gives a smoother surface. The residuals of the de-noised thermal images at various decomposition levels, thresholding levels and histograms are analyzed with statistical values. The original thermal image, when processed by the wavelet filters, does not yield significant results, and the analysis appears to be normal, but the residuals from the decomposed image capture the variations based on the operating condition of the transformer in CT and Thermal images.
Norm is a specific function used to measure the size of non-zero elements in a vector. It is an empirical method where the results are better verified through observation rather than relying on theoretical concepts. Three different methodologies followed for empirical implementation of thresholding strategies: Equal balance sparsity-norm, obtaining the square root of the threshold associated with Equal balance sparsity-norm or remove near 0. Sparsity implies that fewer number of large coefficients can optimally represent the information [
Balance sparsity norm thresholding of transformer breather thermal images produces a range of mean and standard deviation values for various wavelet transforms, and the best is observed for Daubechies wavelet level 3 decomposition, as shown in
Wavelet type/Level | Mean/Range | Mean absolute deviation | Standard deviation | L1 norm | L2 norm |
---|---|---|---|---|---|
Haar | 4 × 10−2/4 | 16.6 × 10−2 | 37.6 × 10−2 | 4.18e4 | 209.2 |
Sym (8)/3 | 10 × 10−2/10 | 51 × 10−2 | 81.7 × 10−2 | 1.56e5 | 453 |
Coif (5)/4 | 12 × 10−2/12 | 53.1 × 10−2 | 83.8 × 10−2 | 1.62e5 | 464.3 |
Bior (6.8)/2 | 9 × 10−2/10 | 48.4 × 10−2 | 71.9 × 10−2 | 1.21e5 | 398.7 |
Rbio (6.8)/3 | 7 × 10−2/11 | 49.5 × 10−2 | 79.2 × 10−2 | 1.52e5 | 438.8 |
dmey/3 | 11 × 10−2/13 | 53.5 × 10−2 | 85.4 × 10−2 | 1.63e5 | 473.2 |
fk (22)/4 | 9 × 10−2/15 | 53.96 × 10−2 | 88.7 × 10−2 | 1.64e5 | 491.6 |
The strategy suggested by the Donoho-Johnstone method is that when the noise ratio is small, then fixed form thresholding needs to be applied [
In
Wavelet Type/Level | Mean/Range | Mean Absolute Deviation | Standard Deviation | L1 Norm | L2 Norm |
---|---|---|---|---|---|
Haar/1 | 16 × 10−2/16 | 23.13 × 10−2 | 60.41 × 10−2 | 6.04e4 | 335.7 |
DB (10)/3 | 35 × 10−2/59 | 96.51 × 10−2 | 1.973 | 2.81e5 | 1094 |
Sym (8)/4 | 38 × 10−2/38 | 99.44 × 10−2 | 1.678 | 3.03e5 | 930.3 |
Coif (5)/2 | 28 × 10−2/37 | 56.9 × 10−2 | 1.345 | 1.72e5 | 745.6 |
Rbio(6.8)/5 | 15 × 10−2/35 | 90.93 × 10−2 | 1.47 | 2.77e5 | 813.8 |
dmey/3 | 25 × 10−2/55 | 1.074 | 2.064 | 3.24e5 | 1145 |
fk (22)/3 | 47 × 10−2/57 | 78.029 × 10−2 | 2.141 | 3.11e5 | 1187 |
The mean value of residuals of the de-noised image shows a sharp peak while using bi-orthogonal wavelets for transformation, which indicates a significant change in the de-noised image at the third level of decomposition. No other wavelets show such variation when the decomposition levels are changed. Therefore, bi-orthogonal wavelets can be used for de-noising CT terminal thermal images and the de-noised image can be further processed to differentiate the cause of protective system tripping that was either due to in-rush current or internal fault with CT saturation.
The mean value of residuals of the de-noised image never has a significant variation for any wavelet, as shown in
Wavelet type/Level | Mean/Range | Mean absolute deviation | Standard deviation | L1 Norm | L2 Norm |
---|---|---|---|---|---|
Haar/1 | 2 × 10−2/2 | 8.78 × 10−2 | 26.6 × 10−2 | 2.18e4 | 147.8 |
DB (10)/1 | 6 × 10−2/6 | 18.8 × 10−2 | 43.8 × 10−2 | 5.69e4 | 243.2 |
Sym (8)/5 | 1 × 10−2/3 | 23.5 × 10−2 | 48.2 × 10−2 | 7.13e4 | 267 |
Coif (5)/3 | 4 × 10−2/4 | 23.03 × 10−2 | 47.6 × 10−2 | 6.97e4 | 263.9 |
Bior (6.8)/3 | 3 × 10−2/3 | 22.2 × 10−2 | 46.7 × 10−2 | 6.71e4 | 259 |
Rbio(6.8)/5 | 4 × 10−2/4 | 23.3 × 10−2 | 47.9 × 10−2 | 7.04e4 | 265.4 |
dmey/5 | 4 × 10−2/4 | 23.1 × 10−2 | 47.8 × 10−2 | 7.01e4 | 265 |
fk (22)/4 | 4 × 10−2/4 | 22.2 × 10−2 | 47.03 × 10−2 | 6.78e4 | 260.7 |
The mean value of residual thermal images never significantly varied for different wavelet types, as shown in
Wavelet type/Level | Mean/Range | Mean absolute deviation | Standard deviation | L1 Norm | L2 Norm |
---|---|---|---|---|---|
Haar/5 | 26 × 10−2/6 | 48.06 × 10−2 | 53.2 × 10−2 | 8.06e4 | 295.1 |
DB (10)/1 | 26 × 10−2/6 | 58.8 × 10−2 | 43.8 × 10−2 | 5.69e4 | 243.2 |
Sym (8)/4 | 28 × 10−2/5 | 35.2 × 10−2 | 61.2 × 10−2 | 1.08e5 | 339 |
Coif (5)/4 | 16 × 10−2/6 | 34.5 × 10−2 | 60.5 × 10−2 | 1.05e5 | 335.2 |
Bior (6.8)/5 | 15 × 10−2/5 | 33.9 × 10−2 | 59.8 × 10−2 | 1.04e5 | 331.2 |
Rbio (6.8)/1 | 29 × 10−2/4 | 52.8 × 10−2 | 42.1 × 10−2 | 5.4e4 | 233 |
dmey/5 | 11 × 10−2/7 | 34.91 × 10−2 | 61.4 × 10−2 | 1.07e5 | 340.2 |
fk (22)/5 | 21 × 10−2/7 | 34.08 × 10−2 | 61.14 × 10−2 | 1.05e5 | 338.9 |
The hybrid wavelet threshold image analytics (HWT-IA) method is proposed by hybridizing the thresholding techniques such as balance sparsity norm and fixed form for de-noising CT and breather thermal images. The statistical values show significant changes in the mean and absolute deviation for a certain level of decomposition, and acquired thermal images show peaks of two different magnitudes in the residual mean value both during in-rush current occurrence using CT Terminal image and internal fault condition with CT saturation using breather image. Histogram peaks of thermal images show the differentiation of faults in the thermal images. The original and the enhanced images are shown in
Wavelet type/Level | Input image | Mean/Range | Mean absolute deviation | Standard deviation | L1 Norm | L2 Norm |
---|---|---|---|---|---|---|
DWT-(DB 10–level 3) (Balance sparsity norm and fixed form- HWT-IA) | Breather image (Internal fault with CT saturation] | 15 × 10−2/15 | 47.5 × 10−2 | 77.4 × 10−2 | 5.48e4 | 473.1 |
NDWT-(Bior 6.8–level 3) (Balance sparsity norm and fixed form- HWT-IA) | CT terminal [during inrush occurrence] | 48 × 10−2/48 | 91.25 × 10−2 | 1.751 | 2.65e5 | 1015 |
Line Voltage | CT–I (Amps) | Hz | Cos φ | OLTC Tap Position | Load % | Breather image denoised mean value (DWT) & HWT-IA | Breather image denoised–SD (DWT) & HWT-IA | Breather denoised entropy (DWT) & HWT-IA | CT Image denoised mean value (NDWT) & HWT-IA | CT image denoised–SD (NDWT) & HWT-IA | CT denoised entropy (NDWT) & HWT-IA | Breather temp | CT Temp | Condition | Operating condition | Error | Accuracy |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
236.5 | 216.6 | 50.0 | 0.99 | 9 | 20.5 | 119.1 | 67.0 | 7.36 | 108.0 | 79.0 | 7.75 | 131.4 | 123.8 | 0.457 | |||
236.4 | 1604.8 | 50.0 | 1 | 10 | 28.4 | 88.5 | 58.7 | 7.42 | 137.2 | 80.0 | 7.81 | 123.1 | 153.3 | Inrush | 3 | 0.006 | |
233.9 | 87.4 | 50.0 | 1 | 5 | 12.3 | 97.1 | 62.6 | 7.12 | 100.9 | 60.3 | 7.28 | 112.9 | 120.7 | Normal | 1 | 0.413 | |
236.7 | 252.0 | 50.0 | 1 | 4 | 23.9 | 119.1 | 61.9 | 7.21 | 103.9 | 63.3 | 7.51 | 144.9 | 114.9 | 0.129 | |||
237.2 | 5910.0 | 50.0 | 0.98 | 11 | 42.0 | 88.5 | 61.9 | 7.13 | 142.0 | 60.9 | 7.76 | 117.7 | 185.5 | Inrush | 3 | 0.828 | |
237.6 | 4304.4 | 50.0 | 1 | 8 | 36.1 | 90.0 | 63.7 | 7.14 | 127.7 | 61.5 | 7.58 | 114.4 | 180.7 | Inrush | 3 | 0.518 | |
239.0 | 3541.5 | 50.0 | 0.98 | 7 | 33.9 | 97.2 | 56.9 | 7.28 | 117.6 | 62.3 | 7.54 | 116.3 | 175.6 | Inrush | 3 | 0.624 | |
235.2 | 4923.0 | 50.1 | 0.97 | 0 | 38.6 | 87.0 | 62.9 | 7.03 | 137.5 | 57.8 | 7.66 | 104.9 | 182.6 | Inrush | 3 | 0.498 | |
236.2 | 2917.2 | 49.9 | 0.98 | 4 | 31.8 | 96.7 | 59.6 | 7.08 | 117.8 | 58.0 | 7.53 | 109.8 | 164.0 | Inrush | 3 | 0.014 | |
235.0 | 162.6 | 50.0 | 1 | 4 | 15.3 | 110.3 | 61.8 | 7.25 | 104.3 | 80.0 | 7.52 | 117.8 | 119.9 | 0.020 | |||
233.9 | 55.9 | 50.0 | 1 | 5 | 7.8 | 94.6 | 60.2 | 7.41 | 101.5 | 60.5 | 7.51 | 103.0 | 107.7 | Normal | 1 | 0.011 | |
236.5 | 2346.3 | 50.0 | 0.96 | 9 | 30.3 | 90.7 | 61.2 | 7.16 | 114.9 | 57.6 | 7.41 | 107.5 | 156.1 | Inrush | 3 | 0.411 | |
239.7 | 92.9 | 50.0 | 1 | 0 | 13.4 | 94.9 | 61.0 | 7.24 | 113.2 | 58.9 | 7.49 | 105.6 | 123.7 | Normal | 1 | 0.674 | |
240.8 | 100.4 | 50.0 | 0.99 | 0 | 14.5 | 95.8 | 62.0 | 7.28 | 102.5 | 81.8 | 7.32 | 109.6 | 118.3 | Normal | 1 | 0.509 | |
240.0 | 282.8 | 50.1 | 0.98 | 0 | 27.1 | 118.2 | 89.1 | 7.24 | 104.0 | 80.9 | 7.67 | 138.2 | 121.9 | 0.092 | |||
233.6 | 71.8 | 50.1 | 1 | 5 | 10.1 | 94.8 | 62.4 | 7.42 | 105.2 | 61.1 | 7.43 | 106.8 | 120.0 | Normal | 1 | 0.600 | |
239.9 | 117.4 | 50.0 | 1 | 9 | 16.9 | 94.1 | 55.7 | 7.29 | 103.0 | 58.7 | 7.48 | 110.2 | 111.2 | Normal | 1 | 0.072 | |
237.3 | 1880.1 | 49.9 | 0.98 | 0 | 29.7 | 90.0 | 61.3 | 7.33 | 128.6 | 59.0 | 7.66 | 101.3 | 154.6 | Inrush | 3 | 0.191 | |
234.0 | 129.4 | 50.0 | 0.98 | 4 | 18.2 | 94.8 | 62.5 | 7.37 | 111.6 | 60.0 | 7.71 | 102.5 | 121.7 | Normal | 1 | 0.493 | |
236.0 | 278.9 | 50.0 | 0.96 | 0 | 26.3 | 118.9 | 64.8 | 7.26 | 99.8 | 79.9 | 7.56 | 132.1 | 123.4 | 0.306 |
Explanatory variables, which include mean, standard deviation and entropy of the denoised images and the temperature values of the breather and current transformer under various conditions such as in-rush occurrence, fault and normal conditions, are considered for transformer fault prediction through multiple linear regression model. The model assumes that observations are chosen randomly from the existing database, and the distribution of residuals is normal with a mean value of zero. The r2 value, which is the coefficient of determination, is about 86.68% for statistical parameters obtained due to continuous monitoring and thermal image analysis of the breather and current transformer acquired using a thermal camera. The
Predictor | Coefficient | Estimate | Standard error | t-statistic | |
---|---|---|---|---|---|
Constant | β0 | −10.1695 | 9.2609 | −1.0981 | 0.2956 |
Breather Mean | β1 | 0.0397 | 0.0339 | 1.1718 | 0.266 |
Breather SD | β2 | −0.006 | 0.0195 | −0.3072 | 0.7644 |
Breather Entropy | β3 | 1.0828 | 1.5373 | 0.7043 | 0.4959 |
CT mean | β4 | 0.0249 | 0.0349 | 0.7135 | 0.4904 |
CT SD | β5 | 0.0024 | 0.0148 | 0.1603 | 0.8755 |
CT entropy | β6 | −0.8796 | 1.8915 | −0.465 | 0.651 |
Breather Temp | β7 | 0.0016 | 0.0185 | 0.0889 | 0.9308 |
CT temp | β8 | 0.0308 | 0.0094 | 3.26 | 0.0076 |
The prediction accuracy of the proposed method (HWT-IA) and its characteristics are compared with that of light-gated recurrent unit (LGRU), gated recurrent unit (GRU), convolutional light-gated neural network (CLGNN) and conventional neural network (CNN) as shown in
Classifier | Data acquisition technique | Prediction accuracy (%) |
---|---|---|
LGRU | Contact | 98.59 |
GRU | Contact | 98.37 |
CLGNN | Contact | 99.63 |
CNN | Contact | 98.44 |
HWT-IA | Non-contact | 99.81 |
New transformer rating & company | Breather image denoised Mean Value (DWT) & HWT-IA | Breather image denoised–SD (DWT) & HWT-IA | Breather denoised entropy (DWT) & HWT-IA | CT image denoised mean value (NDWT) & HWT-IA | CT image denoised–SD (NDWT) & HWT-IA | CT denoised entropy (NDWT) & HWT-IA | Condition | Prediction |
---|---|---|---|---|---|---|---|---|
T1/500KVA/666.6 A, 433 V, Indo-Tech Transformers, Palakkad | 118.5 | 66.0 | 7.48 | 110.0 | 81.0 | 7.71 | Inter-turn fault | Correct |
T2/800KVA/1066.69 A, 433 V, Current Electrical Ltd., Thirumudivakkam | 86.3 | 51.7 | 7.38 | 142.2 | 79.0 | 7.84 | Inrush | In-correct |
T3/1000KVA/1333.51 A, 433 V, Current Electrical Ltd., Thirumudivakkam | 91.1 | 61.9 | 7.19 | 99.9 | 63.3 | 7.31 | Normal | Correct |
T4/750KVA/1000.05 A, 433 V, Current Electrical Ltd., Thirumudivakkam | 117.1 | 62.1 | 7.31 | 111.9 | 65.6 | 7.68 | Core fault | Correct |
T5/300KVA/417.37 A, 415 V, Current Electrical Ltd., Thirumudivakkam | 85.5 | 61.9 | 7.18 | 148.0 | 60.9 | 7.79 | Inrush | Correct |
T5/300KVA/417.37 A, 415 V, Current Electrical Ltd., Thirumudivakkam | 101.5 | 61.3 | 7.42 | 146.8 | 78.9 | 7.39 | Transient fault | Correct |
Automation of certain processes which otherwise require personnel for continuous monitoring has reduced the risk to human lives. Transformer breathers have silica gel that varies its colour to indicate the presence of moisture in the air intake for the transformer breathing mechanism. Visual inspection of the breather is required for the replacement of the gel. A novel hybrid thresholding methodology is proposed to differentiate the root cause of false tripping in the transformer’s differential protection relay system and thus contributing to preventive maintenance of power and distribution transformers. The residuals of the de-noised thermal image of the breather in a distribution transformer are compared by applying wavelet transformation with a varied choice of wavelets. The maximum contribution of the intensity of each pixel towards the total image intensity in the de-noised thermal image of the breather was identified, and a modified form of thresholding is proposed by combining balance sparsity norm and fixed form thresholding to effectively remove the unscaled white noise in the thermal images of transformer breather and CT. Significant improvement in the mean and absolute deviation is observed in the stated residual parameters. The maximum prediction accuracy achieved is 99.81%, and the test data of five different live transformers from the operating field is applied to the model for prediction, where five out six predictions match the actual operating condition. The probability that the prediction varies from the null hypothesis prediction is 7 × 10−4, which indicates that the image parameters analyzed are representative features of transformer operating condition. Fuzzy clustering and deep learning could be applied to the de-noised images to probe into transformer performance analysis under varying load conditions.
We thank the Managements of Loyola-ICAM College of Engineering and Technology (LICET), Loyola College, Loyola Institute of Business Administration (LIBA), Nazareth College and Aalim Muhammed Salegh college of Engineering for permitting us to utilize their facilities such as transformers to pursue the research work.