Fault Detection In Power Transformers Research Articles

Transformer fault diagnosis based on DBO-BiLSTM algorithm and LIF technology

Abstract In response to the deficiencies of traditional power transformer fault detection techniques, such as low sensitivity and the inability for online monitoring, a novel transformer fault diagnosis model combining Laser-Induced Fluorescence (LIF) technology with deep learning is proposed. Initially, the spectral data of transformer insulation oil is acquired using LIF technology, yielding spectral data for various fault types. Subsequently, MinMaxScaler (MMS) and Standard Normalized Variate (SNV) methods are employed for denoising and preprocessing the spectral data. The preprocessed data is then subjected to dimensionality reduction using Linear Discriminant Analysis (LDA) and T-distributed Stochastic Neighbor Embedding (T-SNE) to ensure that the spectral data retains maximal feature information while minimizing its dimensionality. Following this, Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), Dung Beetle Optimizer-Bi-directional Long Short Term Memory (DBO-BiLSTM), Convolutional Neural Network (CNN), and Support Vector Machine (SVM) models are constructed. The reduced-dimensional data is fed into each of the five models for training to facilitate transformer fault diagnosis. Through comparative analysis among the five models, the optimal model is selected. Experimental results indicate that the DBO-BiLSTM model is the most suitable for transformer fault diagnosis in this experiment, underscoring its significant implications for ensuring the safety of power systems.

Liquid-solid interface diffusion behaviors of furfural in the insulation systems of natural ester-cellulose paper and mineral oil-cellulose paper

Furfural as an ageing product of cellulose paper can be detected by the furfural content in the oil. With the construction of green power grid, more and more power equipment adopts environmentally friendly insulating fluids. The investigation of the diffusion behaviors of furfural at different oil-paper interfaces can provide a basis for fault detection in power transformers. The liquid-solid interface diffusion behaviors of furfural in natural ester-cellulose paper and mineral oil-cellulose paper insulating systems are investigated by molecular dynamics simulation in this paper. Results show that the range of center-of-mass movement distances of furfural in mineral oil-cellulose paper is about 5 times that of natural ester-cellulose paper. The main reason for this is that the long-chain macromolecules composing the natural ester undergo curling in the extended flow. As polar molecules, natural ester can attract furfural, which is also a polar molecule. And the electrostatic potential mapping interval of furfural is about 4.4 times that of mineral oil, but 1.1 times that of natural ester, with which it overlaps more. These hinder the diffusion of furfural molecules in the natural ester-cellulose paper liquid-solid interface. This research provides theoretical support for the diffusion of furfural in the insulation systems of natural ester-cellulose paper and mineral oil-cellulose paper. It also provides useful guidance for fault detection in transformers using natural ester insulating fluids.

Deep Learning-Based Algorithm for Internal Fault Detection of Power Transformers during Inrush Current at Distribution Substations

The reliability and stability of differential protection in power transformers could be threatened by several types of inferences, including magnetizing inrush currents, current transformer saturation, and overexcitation from external faults. The robustness of deep learning applications employed for power system protection in recent years has offered solutions to deal with several disturbances. This paper presents a method for detecting internal faults in power transformers occurring simultaneously with inrush currents. It involves utilizing a data window (DW) and stacked denoising autoencoders. Unlike the conventional method, the proposed scheme requires no thresholds to discriminate internal faults and inrush currents. The performance of the algorithm was verified using fault data from a typical Korean 154 kV distribution substation. Inrush current variation and internal faults were simulated and generated in PSCAD/EMTDC, considering various parameters that affect an inrush current. The results indicate that the proposed scheme can detect the appearance of internal faults occurring simultaneously with an inrush current. Moreover, it shows promising results compared to the prevailing methods, ensuring the superiority of the proposed method. From sample N–3, the proposed DNN demonstrates accurate discrimination between internal faults and inrush currents, achieving accuracy, sensitivity, and precision values of 100%.

The Fault Detection of Power Transformers Based on Kernel Principal Component Analysis

The Fault Detection of Power Transformers Based on Kernel Principal Component Analysis

Online Sensitive Turn-to-Turn Fault Detection in Power Transformers

Power transformers are of the most critical and expensive equipment in the power system industry. Meanwhile, they are exposed to a variety of faulty and abnormal operating conditions. Therefore, to prevent extension of these events they are protected by different protective schemes. Among the various types of the transformer faults, detecting turn-to-turn faults is the most challenging one for the protection systems. Industrial experiences and standard documents reveal that the existing protection systems include severe shortcomings, and still more efforts are required to offer a reliable method for detecting such faults. To this end, this article puts forward the proposal of a novel turn-to-turn fault detection method to satisfy the basic protection requirements of dependability, security, and speed. This method properly detects the low-level turn-to-turn faults while is stable during system transients and external faults. Performance of the proposed method is evaluated through various experimental and simulation studies. The obtained results verify superior operation of the proposed method under different faults and conditions.

Power Transformer Fault Detection Based on Improved Capsule Coverage Algorithm

In the process of transformer fault detection, oil chromatographic analysis is usually used to observe the change of the dissolved gas content in the transformer oil, warn and judge the internal fault of the transformer. However, in the actual monitoring environment, there inevitably are the problems of missing data, redundancy, and uncertain dimensions. In order to solve the problem of low recognition efficiency or even incorrect recognition caused by the above problems in transformer fault detection, the idea of nuclear is introduced to the improved capsule coverage algorithm. The experimental results obtained by this improved algorithm indicate that the optimized capsule coverage algorithm can realize the accurate identification of transformer faults, it can not only improve the fault maintenance efficiency, but also ensure the transformer has a good power supply level, which is beneficial to improve the construction of national comprehensive power grid.

An innovative method of fault detection in power transformers

The Bayesian classifier is a priori the optimal solution for minimizing the total error in problems of statistical pattern recognition. The article suggests using the classifier as a regular tool to increase the reliability of defect recognition in power oil-filled transformers based on the results of the analysis of gases dissolved in oil. The wide application of the Bayesian method for solving tasks of technical diagnostics of electrical equipment is limited by the problem of the multidimensional distribution of random parameters (features) and the nonlinearity of classification. The application of a generalized feature of a defect in the form of a nonlinear function of the transformer state parameters is proposed. This simultaneously reduces the dimension of the initial space of the controlled parameters and significantly improves the stochastic properties of the random distribution of the generalized feature. A special algorithm has been developed to perform statistical calculations and the procedure for recognizing the current technical condition of the transformer using the generated decision rule. The presented research results illustrate the possibility of the practical application of the developed method in the conditions of real operation of power transformers.

Prognostication of Failures Using Signal-to-Noise Ratio to Determine Partial Discharges Activities in Power Transformers

The increase in number of power transformer necessitates increased awareness among power system engineers to monitor transformer health in order to avoid complete failures that may results in prolonged outages. The utilities, nowadays, are exploring different ways of detecting these faults in an early stage. This paper aims to present a real-time monitoring technique for a three-phase transformer using data analytics. Accordingly, the use of signal to noise ratio (SNR) for fault detection in power transformers has been described. This technique determines the partial discharge with the help of sensors needed for unique identification of failed transformers in different test systems. The SNR for different transformer coils installed in field conditions has been compared and discussed in detail. The technique is expected to enable power transformer operators for detection of incipient faults and thus can avoid catastrophic failures and the resultant losses.

A moving window average method for internal fault detection of power transformers

Power transformers are among the mos]t critical components of power systems and should receive reliable protection against faults. Differential relays are widely used in the protection of power transformers. However, these relays are susceptible to mistaking the inrush currents, which are generated during the turning on of transformers. This paper presents a new method for detecting inrush currents based on the moving window averaging of current. The study found that using the Blackman Harris window rather than other windows in this method significantly reduces fault detection time. The proposed method is robust to strong noises such as white Gaussian noise. Finally, the proposed method was compared with the methods commonly used in industrial protection relays. In this comparison, it was demonstrated that the method offers several advantages over conventional techniques, including the ability to detect faults with high second harmonic and under current transformer saturation and to function properly in modern transformers.

Interpretation of Frequency Response Analysis for Fault Detection in Power Transformers

Frequency response analysis (FRA) is a method of monitoring a power transformer’s mechanical integrity. However, identifying the type of fault and its severity by comparing measured responses is still challenging and mostly relies on personnel expertise. This paper is taking one step forward to standardize the FRA interpretation process by proposing guidelines based on various international standards and FRA case studies. In this study, the FRA signature is divided into three regions: low-, mid- and high-frequency regions. The deviation from the fingerprint signature for various faults is classified into small, large, and no variations, based on the calculation of the correlation coefficient. The proposed guidelines are developed based on the frequency regions, and the level of variation is represented using a simple arrow method to simplify the interpretation process. A case study is conducted on a three-phase 11/0.433 kV, 500 kVA distribution transformer with a short circuit winding fault to validate the proposed guidelines.

An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis

This research work put forward an intelligent method for diagnosis and classification of power transformers faults based on the instructive Dissolved Gas Analysis Method (DGAM) attributes and machine learning algorithms. In the proposed method, 14 attributes obtained through DGAM are utilized as the initial and unprocessed inputs of Adaptive Neuro-Fuzzy Inference System (ANFIS). In this method, attribute selection and improved learning algorithm are utilized to enhance fault detection and recognition precision. In the propounded fault detection and classification method, the most instructive attributes obtained by DGAM are selected by association rules learning technique (ARLT). Using efficient enlightening attributes and eliminating tautological attributes lead to higher accuracy and superior operation. Furthermore, appropriate training of ANFIS has significant effect on its precision and robustness. Therefore, Black Widow Optimization Algorithm (BWOA) is applied to train the ANFIS. Having excellent exploration and extraction capability, fast convergence speed and simplicity is the main reason for choosing the BWOA as the learning algorithm. Two industrial datasets are utilized to test and evaluate the performance of the put forward method. The results show that the propounded diagnosis system has high accuracy, robust performance and short run time. Selecting the most educative attributes of DGAM, training ANFIS optimally, improving the robustness of ANFIS and increasing the classification accuracy are the main contribution of this paper in the field of power transformer fault detection and classification.

Frequency response signature analysis for winding mechanical fault detection of power transformer using sensitivity method

Frequency response signature analysis for winding mechanical fault detection of power transformer using sensitivity method

Interpretation of frequency response analysis of power transformer based on evidence theory

Power transformers are one of the key components of power systems. Therefore, they must be operated efficiently to obtain satisfactory system performance, since any distortion or failure in power transformer structures may cause drastic damages. In order to detect deficiencies in power transformers, various methods are utilised. Frequency response analysis (FRA) is one of the most well-known methods used for fault detection in power transformers. However, the interpretation of FRA results is subjective, and depends primarily on the opinion of a human expert. In other words, there is no comprehensive, accepted and standardised method to interpret FRA results. Subsequently, fault detection based on FRA is prone to error. In this study, a new mathematical approach is used to interpret the frequency response of power transformers based on evidence theory method. This method is more accurate and less computationally burdensome compared to conventional methods. Simulations are implemented on an operating 125 MVA, 230/132/20 kV autotransformer using finite-element method and the results are compared to the corresponding FRA results of the same autotransformer in order to evaluate the proposed method. The obtained results indicate the efficiency of the proposed method in detecting probable faults of transformer windings through analysing the FRA results.

Immersion photoacoustic spectrometer (iPAS) for arcing fault detection in power transformers.

We report on the development of the first immersion photoacoustic spectrometer (iPAS) for arcing fault detection in power transformers. The spectrometer consists of a detection system and an all-optical photoacoustic sensing head mounted inside a small permeable chamber where dissolved C2H2 diffuses while the transformer oil is kept out. Our all-optical iPAS sensor can be placed directly inside an oil bath and measure dissolved C2H2 with the sensitivity and linearity needed for in situ arcing fault detection. Moreover, its fast response time holds great promise for extra-early fault diagnosis.

Fault detection in power transformers using random neural networks

This paper discuss the application of artificial neural network-based algorithms to identify different types of faults in a power transformer, particularly using DGA (Dissolved Gas Analysis) test. The analysis of Random Neural Network (RNN) using Levenberg-Marquardt (LM) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms has been done using the data of dissolved gases of power transformers collected from Punjab State Transmission Corporation Ltd.(PSTCL), Ludhiana, India. Sorting of the preprocessed data have been done using dimensionality reduction technique, i.e., principal component analysis. The sorted data is used as inputs to the Random Neural Networks (RNN) classifier. It has been seen from the results obtained that BFGS has better performance for the diagnosis of fault in transformer as compared to LM.

A Novel ANFIS based Algorithm for Fault Detection in Power Transformers

A Novel ANFIS based Algorithm for Fault Detection in Power Transformers

Study of approaches to incipient fault detection in power transformer by using dissolved gas analysis

To maintain the reliability of power transmission it is important to detect the incipient fault of power transformer as early as possible. If the fault of a power transformer is not detected promptly, it can evolve resulting in high repair costs or even failure of the power transformer and decreasing reliability of power transmission. The most commonly used method for power transformer fault detection is the dissolved gas analysis (DGA) of transformer oil. Various methods have been developed to interpret the data of dissolved gas analysis, but not many are applicable for the detection of the incipient fault. The detection of the incipient fault of a power transformer is included in both IEEE C57.104-2008 “Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers” and Standard of Latvian Electrotechnical Committee LEK 118 “Transformer Oil Inspection Standards”. In both standards, the limits of dissolved gases in transformer oil are divided into levels, each corresponding to different technical conditions of the power transformer including the level that indicates the incipient fault. However, these approaches vary to a great degree – one approach mostly indicates that transformers are in good condition with several cases that must be additionally evaluated, but the second approach mostly results in warning about the incipient fault, which must be confirmed by additional evaluation. The objective of this paper is to determine the most suitable approach to detect the incipient fault of power transformers. A case study is provided, which includes analysis of DGA data of 48 power transformers installed in the transmission network in Latvia with both methodologies mentioned above for detecting the incipient fault.

A multi-criteria computer package for power transformer fault detection and diagnosis

A package in Maple that helps users in power transformers fault detection and diagnosis has been developed. Transformers are required throughout modern interconnected power systems. Their range comprises from a few kVA to over a few hundred MVA, both in low voltage and in high voltage electrical network. As they are considered the key element in such systems, several maintenance methods have been reported in the literature: dissolved gas analysis (DGA) technique, short-circuit impedance (SCI) measurement, frequency response analysis (FRA) and power factor testing among others. All of them have as main goal to increase its useful life; normally reduced from aging process, stress conditions or electrical faults. Besides, they require special measurement devices and the experience of engineers, in order to make a proper diagnosis. This paper firstly determines the requirements of these tests to be applied and coordinate their input data and their output (diagnoses and recommendations). Afterwards, the package developed, that guides the users throughout the diagnosis processes, automatizes data processing and returns of the different tests (underlining if any contradiction between them arises) is summarized. The method is extensible/scalable by means of adding new techniques on this field of application.

A method of real-time fault diagnosis for power transformers based on vibration analysis

In this paper, a novel probability-based classification model is proposed for real-time fault detection of power transformers. First, the transformer vibration principle is introduced, and two effective feature extraction techniques are presented. Next, the details of the classification model based on support vector machine (SVM) are shown. The model also includes a binary decision tree (BDT) which divides transformers into different classes according to health state. The trained model produces posterior probabilities of membership to each predefined class for a tested vibration sample. During the experiments, the vibrations of transformers under different conditions are acquired, and the corresponding feature vectors are used to train the SVM classifiers. The effectiveness of this model is illustrated experimentally on typical in-service transformers. The consistency between the results of the proposed model and the actual condition of the test transformers indicates that the model can be used as a reliable method for transformer fault detection.

Expert System Based Fault Detection of Power Transformer

Expert System Based Fault Detection of Power Transformer