Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods

12 Nov.,2022

 

Low Frequency Transformer

Abstract

This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels generated during operation by transformers in a real environment. The spectra frequency interval and its resolution are automatically optimized for the selected machine learning algorithm. Various machine learning algorithms, optimization techniques, and transformer types were researched: two indoor type transformers from Schneider Electric and two overhead type transformers manufactured by ABB. As a result, a method was proposed that provides a way in which inspections of working transformers (from background) and their type can be performed with an accuracy of over 97%, based on the generated low-frequency noise. The application of the proposed preprocessing stage increased the accuracy of this method by 10%. Additionally, machine learning algorithms were selected which offer robust solutions (with the highest accuracy) for noise classification.

Keywords:

low-frequency sensor, power transformer, machine learning, low-frequency noise, classification

1. Introduction

The operation of medium- and high-power transformers is associated with low frequency noise emission into the environment, the main source of which, among other things, include: cooler fans of the induced air circulation, insulation oil circulating pumps, and the magneto-strictive vibrations of the core. In very simple terms, magnetostriction means that if a piece of magnetic sheet steel is magnetized, it extends. When the source of magnetization is removed, it goes back to its original state. A transformer is magnetically excited by alternating voltage and current so that it extends and contracts twice during a full cycle of magnetization.

Identifying the problem of low frequency noise generated by new and worn out power transformers requires a wide range of tests. The reference measurement methodologies, methods of analysis, and the assessment of the nuisance caused by the generated noise should be clearly defined. Power transformers are considered as strong sources of low-frequency noise, with the most important spectrum components, in view of the noise level, being in the frequency range below 400 Hz. The initial results obtained in the experimental tests and measurements on real units can be referenced to the maximum allowable low frequency noise levels, which are defined in the standards relating to the working environment. Therefore, it is vital to be able to detect the low frequencies of power transformers and their levels (specificity).

The aim of this paper is to propose a method for the automatic detection of noise generated by a power transformer. The method makes it possible not only to detect the presence of noise from a transformer in the area, but also to determine the transformer type, its internal construction, and apparent power. Furthermore, the type of misclassification (i.e. detection of a transformer with higher apparent power) can be used as an initial diagnostic tool that detects the changing parameters of the device. It is worth noting that low-noise measurements can be made during the normal operation of the transformer (online).

The proposed method involves data acquisition using a dedicated low frequency sensor, a preprocessing stage (applying frequency analysis), and a classification stage using machine learning algorithms.

The scope of the analysis reported here includes the determination of waveforms showing changes in the sound pressure level as a function of frequency (amplitude spectra). For comparative purposes, the characteristics were determined separately for the examined transformers and background noise at the selected measurement points.

The paper is organized as follows. Section 2 describes previous works related to the low frequency sound analysis for transformers and machine learning methods. The proposed method for transformer detection based on sound pressure is presented in Section 3. Section 4 includes the experimental results and a comparison of the introduced method with state-of-the-art approaches. The conclusions and future research directions are given in Section 5.

2. Related Works

Infrasound noise is taken to be called noise, in which the spectrum is dominated by low frequencies, i.e., up to 20 Hz, in accordance with the PN-ISO 7196: 2002 standard [1]. There is no standardization of low frequency sounds; however, most scientists consider such frequencies to include the range between 10 and 200 Hz [2].

As part of the initial research, an attempt was made to determine the emission level of low-frequency signals generated by power transformers at rated conditions. The distribution transformers reducing the voltage from 15 kV to 0.4 kV of various types (indoor and overhead) and apparent powers (400 kVA and 2000 kVA) were tested [3].

The research conducted to date has demonstrated that power transformers are a source of low frequency signals. Our studies showed that they are characterized by similar waveforms of averaged amplitude spectra in terms of shape, as well as a similar character of time-frequency changes. The waveforms characteristic of the recorded sound pressure level has dynamically decreasing values, which occur within a frequency range of 10 Hz to 100 Hz [3,4]. In the conducted research, the Brüel & Kjær measuring equipment was applied, focusing only on the measurement of the sound pressure level without converting it to the sound power level. This approach can be effectively applied to determine the range of potential impact of low-frequency sound directly at the location of the measuring point.

Similar research was conducted for wind turbines, which are also covered by a wide range of research on infrasound and low-frequency noise [2]. Usually, the scope of the analysis includes the development of curves such as hysteresis that present variations in parameters over time, and the designation of the frequency spectra of the recorded low frequency signals for different meteorological conditions, often including wind speeds and direction. Usually, the sound pressure level of infrasonic noise for a given wind speed is determined as an arithmetic mean from all recorded sound pressure levels for the speed. A commonly used method for spectrogram analysis is the short time Fourier transform (STFT). The next step in the research study involved the analysis of the frequency spectra of sound pressure levels corrected with G frequency characteristics, which can be employed in demonstrating noise levels in conditions that are audible to the human ear [5,6]. Other studies present the effect of infrasound noise related to everyday human activities [7].

To date, measurements and signal analyses of power transformers were conducted by experts. This paper proposes the automation of a process involving the detection of operational transformers in the vicinity and their classification using Machine Learning algorithms (ML), which makes it possible to find complex relations and rules via data mining techniques. ML roots can be found in pattern recognition and computational learning theory. The method uses learning algorithms and example data (training set) to build the model, which can be adopted for classification or prediction purposes. There are three main categories of learning algorithms: supervised, unsupervised, and reinforced. In the case of supervised learning [8], a training set is given with the correct target values. In case of unsupervised learning [9], we tend to find the relation in some given data without knowing its original (correct) classification. Reinforcement learning [10] solves optimization problems, learning the optimal actions for a given situation. This paper is focused on supervised learning, due to the characteristics of the processed data. The classification algorithms commonly found in the literature includes: k-Nearest Neighbors [11], Naive Bayes Classification [12], Support Vector Machine [13,14], Random Forests [15], Bagging [16], and various types of Neural networks [17,18,19,20].

One of the major objectives in ML is to identify the useful internal representation of the input data by means of preprocessing methods, which transfer them into a new variable space. Preprocessing can simplify the model and improve its accuracy. However, in this case, a deeper understanding of the researched phenomenon is needed (i.e., a heuristics-based approach) to find the optimal feature representation [9] corresponding to the analyzed data. Commonly applied preprocessing methods include Principal Component Analysis [21,22,23,24] and Canonical Correlation Analysis [25,26].

It is worth noting that Neural Networks (especially deep ones), due to their specificity, find applications in both classification and pattern recognition. This approach overcomes the shortcomings of linear models by allowing a defined number of basic function parameters to learn within a defined network. There are many different types of neural networks (distinct by neurons construction and organization). They are easily adaptable to regression and classification problems [17,18]. Feed Forward Neural Networks (FFNN), also known as Multilayer Perceptron (MLP), are the most common type of neural networks. Based on the number of hidden layers, the network can solve more complex, non-linear problems. Unfortunately, they require a large amount of computation for their training (based on network complexity). Finding the optimum set of weights in cases of multiple hidden layer structures is a NP-complete problem [27]. Therefore, an alternative for more than one hidden layer MLP networks was proposed (deep neural network), in which layers have their functions, i.e., analyses of higher-level features based on the low-level features of previous layers [17,20]. Significant results using deep neural networks have led them becoming the most common classifiers in machine learning [27,28].

Previously presented ML algorithms have been proposed for sets of data that are independent. However, in the considered case, the measured time series contains sound pressure values of a defined period of time which is characterized by significant sequential correlations [9,29], and should be represented as a temporal feature. This task can be performed by sequence classification, in which the sequences of time series are taken into consideration [29]. There are a variety of machine learning models and methods that can perform these tasks. Examples of these models and methods appear under the names of Markov models [30], sliding-window methods [31], Kalman filters [32], conditional random fields [33], recurrent neural networks [34], graph transformer networks [17], the Welch method, and maximum entropy Markov models [35]. Further analysis can be found in [36]. The Welch method [37] makes it possible to determine the estimated spectral power density of the signal. As research shows [37], this method can be employed to minimize the effect of external noise, by averaging/smoothing the instantaneous spectrum. Additionally, it can be applied to identify the frequencies that could contain useful information for classification purposes. Therefore, in the proposed model, the Welsh method will be used in the preprocessing stage with the proposed feature discrimination method and various ML algorithms.

3. Methodology and Characteristics of the Analyzed Power Transformers

Based on previous research concerned with the transformer infrasound noise [1,3], it was observed that noise could be detected in all frequency spectra, but that some frequencies were found to be dominant. On the basis of this observation, a method was proposed to find the optimal frequency intervals and automatically detect noise using a selected machine learning algorithm. An algorithm providing the overview of this method is presented in .

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In a first step, the low frequency sound generated by the transformer or background is registered using a dedicated sensor device. The data are represented as time series X = [x1, x2, …, xn], where n is the number of samples. To reduce the volume of data in time series X, it is transformed into frequency domain vector F by the application of the Welch method. Then, using parameters P = [l, h, s, m1, m2, …, mk], the model is tuned. The parameters l = [2, 100], h = [2, 100], and l < h defines respectively lower and upper frequency bands, s∈N influences sample resolution, and mi, where i = 1, …, k, defines the ML algorithm parameters. In the first phase of the method ( a), initial parameters P’ are selected. The vector F size is reduced using l, h, and s parameters, and then it is used (together with class labels) to train the ML (tuned by mx parameters) using ten-fold cross-validation to increase the soundness of the result. The obtained classification accuracy (acc) is used to calculate fitness function ff (Equation (1)).

ff=acc−h−ls×10000,

(1)

where acc = TP+TNTP+TN+FP+FN, TP is the true positive (correct classification), TN is the true negative (correct rejection), FP is false positive (type I error), and FN is false negative (type II error).

The proposed fitness function ensures that the result with highest accuracy will be selected, while results with fewer samples will be favored in cases when a comparable level of accuracy level is gained.

The number of iterations depends on the chosen optimization method. This research involved exhaustive search and heuristic methods. The parameters for the iteration with the highest ff function value and generated model M are used in the verification phase to estimate the model’s robustness.

Several optimization algorithms and ML methods were analyzed to find the optimal solution. In this section, all the used algorithms are described and discussed.

3.1. The Data Acquisition Method

The sensor device comprises a microphone type 4190, designed for accurate free field measurements, connected to a preamplifier type 2669L from Brüel & Kjær (Nærum, Denmark) and a digital signal meter with registration function LAN-XI type 3050-A-60, also from Brüel & Kjær. It was used to register low frequency signals, as shown in . It is a professional tool used to measure sound pressure, intensity, and vibrations. Its implementation possibilities are wide ranging, i.e., from typical acoustic tests, such as noise measurements, the determination of sound power levels, noise mapping using beamforming techniques, testing the acoustic properties of materials, and determining the acoustic parameters of rooms, to specialist acoustic tests, such as machine diagnostics, modal analyses, and electroacoustic tests of acoustic transducers [2]. In the case of the used set, the range of the measured frequencies varied from 0.7 Hz to 20 kHz.

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Before starting the measurements, the system was calibrated using the Brüel & Kjær type 4231 acoustic calibrator.

To operate the meter, the computer was used with dedicated software which was connected to a LAN cable measuring system. All operating parameters were defined using the PULSE LabShop application version 15.1.0, which forms an integral part of the setup shown in . Apart from the option of the precise configuration of the device, this software provides tools with which to record measured signals and preprocess and visualize them in offline mode. This is a dedicated software package developed by Brüel & Kjær Sound & Vibration Measurement A/S.

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The measurement was carried out through a continuous, multi-hour process using a sampling frequency of 51.2 kHz (full registration with listening capability). In addition, all analyzed power transformers were located away from major roads and motorways.

3.2. Feature Extraction and Optimization Methods

The feature extraction from time series X was performed using fast Fourier transform with a Hamming window. The procedure is called the Welch method [37]; it allowed us to determine the estimated spectral power density of the signal. The method aims to minimize the influence of external noise by averaging/smoothing the instantaneous spectrum. The parameters of the method were adapted to suit the characteristics of the test apparatus and the analyzed frequency range [2].

As a result, the vector F = [f1, f2, …, fn] was generated, which defines the values of sound pressure for a given frequency, in this case, a low frequency (2–100 Hz). The n value depends on the resolution of transformation. In this case, df = 0.125 Hz, yielding n = 784 values. To decrease quantity of input data, its resolution can be modified by the s, s∈N. parameter. The F’ vector was defined using the s parameter value (Equation (2)):

F′=[f1′,f2′,…,fi′,…,f[ns]′],fi′=f(i−1)*s+1,fi∈F.

(2)

For instance, for s = 8, the reduced vector stores only values representing data for a 1 Hz resolution (99 features). Finally, the vector F’ is further reduced, and it contains only values for a given frequency interval. The operation is performed using two filters tuned by the lower (l ∈N) and upper band (h ∈N) parameters. As a result, the final vector of features is generated (F″) (Equation (3)):

F″=[f1″,f2″,…,fj″,…,f[h−ldf*s]+1″],fj″=f[l−2df*s+j]′,f′∈F′

(3)

which is used as an input for the ML algorithm. It is worth noting that l, h, and s are part of P parameter vector.

The search for optimal P vector values was performed using various optimization methods. Due to the small intervals of the analyzed frequency, it was possible to use an exhaustive method (EM) that made it possible to search through all the available parameter regions to find the optimal one. Nevertheless, several additional methods were also researched. The first is called Hill Climbing (HC), and starts with random parameter values. Then, the optimal solution was searched for among the surrounding values. If no neighbors improved the result, the optimization procedure was terminated. The second one, called random search (RS), also selects the initial parameters randomly, then the next position is selected randomly within the search space. The algorithm ends after the definition of 1000 steps or if the result does not improve over 20 steps. Finally, the Bayesian optimization (TPE) strategy was used which consists of two phases; the first is the warm-up, in which parameter combinations are randomly selected and evaluated. Based on the scores of the warm-up rounds, the second phase tries to find promising parameter combinations, which are then evaluated [38].

3.3. Machine Learning Model (ML)

The machine learning model M was designed and developed using reduced training data set F″ and parameters mx. The various ML algorithms were researched to check their applicability for the purposes of this task. They include k-Nearest Neighbors (KNN), Multilayer Perceptrons network (MLP), Classical Support Vector Machines (SVMs), and the Bayes approach. The implementation of these methods was based on the Weka library [39]. The KNN method classifies a new data vector by looking at the k given vectors that are closest to it in the feature space. In the proposed method, Euclidean distance, with k = m1 as a parameter, was selected. For the case of probabilistic classifiers, we can apply those that identify the naive Bayes family on the basis of Bayes’ theorem with the assumption of independence among the features; thus, no parameters are required. The SVMs non-probabilistic approach aims to identify hyperplanes that separate search classes. In this research, linear SVM was used that finds a hyperplane that is a linear function of the input features. Several parameters, like normal vector to the hyperplane by w and the parameter for controlling the offset b, as well as variable ξi, were preset based on research reported in [13,14]. For the random forests method, where instead of training a single tree, many trees are trained, the number of trees was defined as t = m1 parameter. Based on the initial research, the maximal number of trees was set to 10. Above this level, no improvement in accuracy was recorded. The MLP was selected to be representative of an artificial neural network. It proved adequate for the purposes of classification tasks. In this case, ≤ 2 hidden-layer (L = m1) and ≤ 20-neurons-per-layer (m2) structures were considered.

5. Conclusions

The proposed method, using a dedicated sensor, makes it possible to detect the state of transformers (on/off) based on the emitted low frequency with an accuracy of 99%. The best results were obtained using the Random Forest, KNN, and Naïve Bayes methods. The exhaustive search and Bayesian Optimization proved to be the best optimization methods for the transformer low frequency classification problem; however, the heuristic approaches gave better results in cases of separate transformers.

Further research may be applied to distinguish the type of transformer with a 97% level of accuracy using the KNN method; however, most of the classifiers made it possible to obtain accuracy levels of above 95%. The proposed preprocessing method makes it possible to significantly reduce the number of attributes and increase the detection accuracy by 10% on average. The method, with the proposed preprocessing module, could be adapted for edge computing in nodes due to its simplicity.

The present study has demonstrated that most low frequency noise (information) could be found near the following frequencies: 1Hz, 50Hz, and 100Hz. Therefore, future work will focus on the use multiple frequency ranges to train the classifiers. Furthermore, spectra analyses should be extended to over 100 Hz, due to the fact that vital information can be found near this frequency. This is a first step in future research; in the next stages, we also want to be able to distinguish the power of the transformer and its operating time. This set of parameters can be used for more precise diagnostics.

Finally, the method could be extended to detect noise form unknown transformers and to estimate their type and power level.

Author Contributions

Conceptualization, D.J.; Methodology, M.B.; Software, M.B., D.J., T.B.; Validation, T.B., D.J., M.B.; Formal Analysis, T.B.; Investigation, D.J., M.B., T.B.; Writing-Original Draft Preparation, M.B. and D.J.; Writing-Review & Editing, M.B., T.B. and D.J.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.