FRA interpretation techniques

The FRA is a well-established and widely used monitoring technique with

standardized measurement setup. However, till now the interpretation guidelines of the test

results are quite general and non-informative, since the fault type and its severity level has become essential information for qualified maintenance procedure [121], [122]. Over the past decades, different techniques have been applied for the progress of the FRA interpretation [3], [123]. These techniques mainly include simulation models, statistical analysis, artificial intelligence (AI), and digital image processing [18], [112], [122]-[129].

The simulation model is usually used to emulate different winding mechanical faults and investigate their influence on the distributed parameters of the winding, such as resistance, inductance, and capacitance [130]. Afterwards, the database of the faulty scenario FRA signatures is applied to classify the given frequency response.

3.3.1 Visual inspection

Conventionally, the measured FRA signatures are interpreted through a visual

comparison of the reference spectrum with the current measurement. Commonly, this type

of the inspection is carried out in the field by maintenance personnel and requires a solid

background and expertise in FRA testing [131], [132]. Depending on the magnitude of the

deviation and affected frequency range, an on-site operator can report which part of the

transformer is undergoing a certain fault and to what extent. The lower frequencies are

usually influenced by the core structure, the mid-frequencies are affected by winding

internal faults, and the higher frequencies are the function of lead connections and

clamping [133]. Having this at hand, it is observed that the accuracy and quality of the

FRA diagnostic test highly rely on the experience of operator and, thus, it is prone to a

human error and biased decision [134], [135].

Figure 3.4. FRA signatures of phases A, B, and C with frequency sub-bands illustrated, taken from [133]

3.3.2 Machine learning tools

One of the challenges of accurate prognosis algorithm and fault localization technique is interpretation of FRA output data. In this regard, a number of researchers have been working on application of computing techniques that train and improve a neural network (back propagation, radial basis function, and self-organizing feature map) in analysis of FRA data. Other than that, various AI techniques, such as machine learning (neural networks (NN), support vector machines (SVM), regression, etc.) and fuzzy logic have been extensively used on FRA data to detect, classify, and localize the fault in a test object. In particular, SVM, deep neural networks (DNN), convolutional neural networks (CNN), multilayer perceptron (MLP), etc. are among mostly applied techniques borrowed from AI [136]-[139]. The transfer function magnitude and phase response are used as an input for the image recognition algorithm where digital image processing (DIP) techniques are used to process the raw frequency response spectra and compare reference and recent FRA signatures based on their geometric properties and features, and define the fault level [122]-[129].

Ma and Ren [140] applied SVM classification algorithm for transformer winding

deformation detection and prognosis. A training data set for supervised machine learning

(SML) was collected from the off-line Impulse Frequency Response Analysis (IFRA) results [140]. The detection system demonstrated a sufficient level of accuracy during industrial tests. However, it is designed for analysis on an isolated and grounded transformer, hence further studies should be conducted to adjust the approach for on-line diagnosis. Zhao et al. [141] claimed feasibility of IFRA data based SVM classification algorithm on energized transformer. Two statistical indicators as resonant frequency variation (RFV) and mean square error (MSE) are extracted from IFRA data and used for further training and testing set to diagnose radial deformation, short circuit and disk space variation. The diagnostic results of ten groups show the average accuracy of 78.1% and 83.3% for RFV and MSE, respectively [141].

Moreover, Bigdeli et al. [142] utilized measurements taken in time domain and employed FFT for SVM classification algorithm. Additionally, along with SVM indices of frequency and amplitude ratio and vector fitting have been applied for detection of transformer winding fault, such as radial and axial deformation, disc displacement. It has been stated that SVM method is more accurate compared to artificial neural network for identification of winding fault type.

In [143], the learning vector quantization (LVQ) borrowed from artificial neural networks (ANN) is used for classification of the transformer insulation damage. The minor and major axis parameters from voltage-current (V-I) characteristic diagram are utilized for the feature extraction of the classification model. The proposed classification algorithm resulted in 98.2% accuracy of identifying different levels of the insulation fault.

3.3.3 Statistical analysis

In contrast to conventional visual interpretation, the method of statistical analysis reports the numerical difference between fingerprint and new FRA spectra, and the magnitude of the calculated Statistical Indicator (SI) basically defines the severity of the transformer fault. For instance, various statistical methods were applied to localize the partial discharge inside the transformer winding. The studies by [144]-[147] provided a thorough review on different numerical indices used for interpretation of FRA results. For instance, in [144], a detailed review on statistical indices discussed in literature is provided.

Moreover, according to Samimi and Tenbohlen [144], SIs can be categorized into three

groups based on whether retrieved from FRA signatures, from resonance and anti- resonance peaks, or from rational functions. The first group proposes SIs to be expressed in linear or logarithmic scale, yet there is no standard or methodology agreed upon between researchers and industry. The second group of indices are usually expressed in logarithmic scale and demonstrate the behavior of the resonating peaks in case of any internal fault.

However, it is claimed that this method requires additional noise mitigation and standardization. The third group, also referred to as the vector or rational function fitting, is the most challenging among all three methods. This approach implies estimation of the SIs from the pre-defined rational functions fitted into the original FRA signature. The order of the rational function determines the amount functions fitted into the spectrum, which, in turn, might lead to different results. The study conducted by Banaszak and Szoka [145]

proposed SI grouping and used 14 indices widely used in literature. The grouping approach implies diving the SIs into four main groups depending on the behavior of each index towards mechanical deformation fault. Hence, via considering at least one index from each group, this method facilitates to distinguish frequency sub-bands more sensitive to mechanical deformation and mitigate the possibility of the information loss. The study by Nurmanova et al. [146] analyzed the behavior of several SIs, namely, Correlation Coefficient (CC), Root Mean Square Error (RMSE), Absolute Sum of Logarithmic Error (ASLE), and Euclidean distance (ED) when distribution transformers are exposed to different external resistive and capacitive impedances. According to [147], where statistical analysis was applied to evaluate the numerical difference of frequency response for intact and faulty conditions (short-circuit and mechanical displacement), all five used indices were able to detect FRA signature deviations, however Comparative Standard Deviation (CSD) reported highest sensitivity to fault and linear relationship towards the severity of the fault. Jeyabalan and Usa [148] implemented the correlation of winding response for reference partial discharge impulse across transformer sections with response for impulse of different width. Since time domain method was unsuccessful to distinguish the location of partial discharge when the width of excitation pulse is different form reference pulse, it was suggested to conduct correlation of winding response in frequency domain.

Although IEEE Standard [49], [50] suggests to use CC for FRA results interpretation,

in [149] alternative indices such as ASLE, SSE, and SD are utilized to distinguish among

different fault types, namely, clamping and bushing tap fault, disk-to-disk fault, mechanical deformation of the winding structure, and short circuit fault. Moreover, in [149] the statistical indices are calculated for three sub-bands individually, since frequency response may be deviated differently according to the type of the fault. According to numerical comparison of the response spectra, SD reported higher sensitivity towards bushing and short circuit fault, SSE is mostly affected by disk-to-disk and short circuit fault, whereas ASLE reported sensitivity towards all three of the mentioned fault types.

In addition, in [106] and [150] the decision boundaries for cross-correlation factor (CCF) and relative factor (RF) for classification of the transformer condition has been determined. Specifically, as per Kennedy et al. [150] the critical CCF values were used to classify between good, marginal, and need-to-investigate working conditions. Other than that, in DL/T911-2004 Standard [19] critical values for RF have been established to distinguish amongst normal condition, slight, obvious, and severe winding deformations.

According to practical results in [151] and [152], CCF and RF demonstrated significantly low sensitivity to define the transformer’s faulty condition.

On the contrary, Kim et al. [153] stated that ASLE and SSE proved quite effective in defining the mechanical deformation of the transformer winding. It should be noted, that in some studies a collaborative decision of several indices were also taken into account.

For example, in study by Samimi et al. [154] the collective result of both CC and ED were reported, since CC was insensitive to mechanical deformation of the winding structure.

Moreover, in [155] a standardized difference area (SDA), correlation factor (ρ), index of frequency deviation (IFD), index of amplitude deviation (IAD), frequency weight function (Wf), and the amplitude weight function (Wa) were applied to classify the type of the winding deformation and also to define its severity.

3.3.4 Digital image processing

The transfer function magnitude and phase response are used as an input for the

image recognition algorithm where DIP techniques are used to process the frequency

response spectra and compare the reference and the recent measured FRA signatures based

on their geometric properties and features, and define the fault level [7].

The study conducted by Aljohani and Abu-Siada [112] proposed a consideration of both transfer function magnitude and phase plot in one combined 2-D image for successive detection and classification of transformer fault. After elimination of the noise and background from the pictorial data, it was further used for extraction of the features for oil and bushing fault classification.

Zhou et al. [156] proposed an innovative approach to determine transformer winding faults from FRA signature. Frequency response magnitude plot is transformed into a binary image and went through the data erosion in order to reduce some minor disturbances.

Afterwards, features extracted from the processed images were used to determine the deviation between reference (fingerprint) and new FRA measurement, which helped to diagnose any occurring faults in the winding structure. Furthermore, the SVM based classification model was utilized to classify the results of practical case studies among axial deformation, series capacitance deviation, and short circuit fault.

Zhau et al. [157] applied DIP technique to magnitude and phase plot of the transfer function and estimated similarity indicator for two dimensional images representing intact condition and mechanical deformation; and observations reported higher sensitivity of the proposed method compared to conventional statistical analysis engaged into standards.

The study by [158] introduced a new FRA results interpretation technique, where the phase response of the transformer winding is converted into 2D image with real and imaginary parts. Comparison of the FRA spectra is conducted via calculation of the Sum square max-min ratio error (ISSMMRE) and further used to define condition of the winding under the test. However, the practical results revealed that the proposed method is insufficient to define both type and severity of the applied mechanical fault.

3.4 FRA Standards