Internal Faults In Power Transformers Research Articles

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%.

Graphical examination of dissolved gas analysis by ratio methods and Duval triangle method to investigate internal faults of power transformer

Power transformer is a pivotal high voltage electrical apparatus of power system network. It is robust, bulky and one of the costliest equipment’s among all other connected network elements. Dissolved gas analysis (DGA) is one of the most popular proven methods to detect internal faults of power transformer. This research article incorporates ratio based DGA method using fuzzy logic as well as Duval Triangle based DGA method to analyse the internal faults of power transformers. Comparison of two distinct methods of DGA analysis for prevention of catastrophic failure is presented. The industry based standard data set are used for analysis and validation.

A simple method to detect internal and external short-circuit faults, classify and locate different internal faults in transformers

In new power systems, transformer monitoring and on-time detection of internal faults in power transformers are of great importance because if these faults are not identified on-time, the lifetime of the transformer might be reduced, the transformer might be completely disconnected, and network reliability might decrease. In this paper, a new method is proposed to detect internal and external short-circuit faults. It also allows us to classify and locate different internal short-circuit faults offline in power transformers. The proposed method is based on creating a locus curve of voltage–current difference ( $$ \Delta V - I $$ ) for all three phases of the transformer. This method can be implemented simply without requiring special equipment and only through monitoring terminal quantities of the transformer. In order to investigate the behavior of transformers in the presence of internal short-circuit fault, the finite element method is used. By investigating variations of locus diagrams concerning to the normal state, fault type and fault location are determined. The proposed method is applied to a distribution transformer white Yzn5 connection successfully. Simulation results verify the capability of the proposed method in transformer monitoring.

A New Practical Approach for Discrimination between Inrush Currents and Internal Faults in Power Transformers

The power transformers are protected by differential relays. These relays use the second harmonic blocking to distinguish inrush current from internal fault current. In recent years, the performance of the second harmonic blocking has decreased and therefore new methods are needed to distinguish inrush current from internal fault current. The aim of this study is to present a new method for discrimination between magnetizing inrush currents and internal faults in differential protection of power transformers. The proposed scheme is based on calculating the dq0 transformation of current signals in the abc phases of transformer terminals for different inrush current and internal fault signals. Creating new waveforms using mathematical calculations, the signals are identifiable by their characteristics. The accurate diagnosis is based on creating a classification pattern for the discrimination algorithm. To substantiate the preciseness of proposed methodology, different states of inrush and fault currents are simulated in PSCADTM/EMTDCTM. Consequently, calculations and the distinction process are carried-out in MATLAB environment. The discrimination procedure needs only current signal data of less than a quarter of power frequency cycle and uses very simple classification rules. Eventually, both simulation and experimental results show that the accuracy of proposed method is high. The results of this study confirm that simple and accurate methods which use patterns can be developed for distinguishing the internal faulty current of transformer from inrush and healthy current.

A New Algorithm for Three-Phase Power Transformer Differential Protection Considering Effect of Ultra-Saturation Phenomenon Based on Discrete Wavelet Transform

Abstract One of transient phenomena that lead to the false trip of the power transformer differential protection during the energization of a loaded power transformer is the ultra-saturation phenomenon. This paper presents, at first, a new algorithm for three-phase power transformer differential protection considering effect of the ultra-saturation phenomenon based on Discrete Wavelet Transform (DWT). To model the ultra-saturation phenomenon, the nonlinear characteristic of the transformer core and the effect of the saturation of the current transformers are taken into account. It is assumed that the load of the transformer is a resistive and inductive load. In this algorithm, the ultra-saturation phenomenon, the external and internal faults of power transformer and the magnetic inrush current are simulated. To distinguish between these phenomena, appropriate criteria using DWT by the use of standard deviation of coefficients are presented. Also, one of the most important criteria for the digital relays is the time for making a decision. Thus, to determine the time of decision, the experimental results will be presented.

A reliable setting-free technique for power transformer protection based on wavelet transform

This paper presents a new methodology based on the analysis and monitoring of the discrete wavelet transform (DWT) detail coefficients (DetC) for internal fault conditions identification and discrimination in power transformers. The method extracts the characteristics and calculates the DetC signals levels variation through DWT decomposition for identification of transients. During internal faults the DetC present a sudden and high variation, different from what happens in other transients, allowing their discrimination. The proposed method was able to correctly identify the different types of internal faults in power transformers and additionally transient as inrush currents. The main novelty of the method are: (1) Intelligent data analysis and monitoring, without a threshold adjustment; (2) Fast identification of an internal fault conditions and high coverage of turn-to-turn faults; (3) Secutity during transients such as inrush currents, external faults and overexcitation; (4) Simplicity. The results show that the proposed method has potential for real application in power transformers protection.

Hot Spot Temperature and Grey Target Theory-Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study

This paper develops a novel dynamic correction method for the reliability assessment of large oil-immersed power transformers. First, with the transformer oil-paper insulation system (TOPIS) as the target of evaluation and the winding hot spot temperature (HST) as the core point, an HST-based static ageing failure model is built according to the Weibull distribution and Arrhenius reaction law, in order to describe the transformer ageing process and calculate the winding HST for obtaining the failure rate and life expectancy of TOPIS. A grey target theory based dynamic correction model is then developed, combined with the data of Dissolved Gas Analysis (DGA) in power transformer oil, in order to dynamically modify the life expectancy calculated by the built static model, such that the corresponding relationship between the state grade and life expectancy correction coefficient of TOPIS can be built. Furthermore, the life expectancy loss recovery factor is introduced to correct the life expectancy of TOPIS again. Lastly, a practical case study of an operating transformer has been undertaken, in which the failure rate curve after introducing dynamic corrections can be obtained for the reliability assessment of this transformer. The curve shows a better ability of tracking the actual reliability level of transformer, thus verifying the validity of the proposed method and providing a new way for transformer reliability assessment. This contribution presents a novel model for the reliability assessment of TOPIS, in which the DGA data, as a source of information for the dynamic correction, is processed based on the grey target theory, thus the internal faults of power transformer can be diagnosed accurately as well as its life expectancy updated in time, ensuring that the dynamic assessment values can commendably track and reflect the actual operation state of the power transformers.

Efficient method for discrimination between inrush current and internal faults in power transformers based on the non‐saturation zone

There are many different techniques for discrimination between inrush current and internal faults. However, due to their inherent limitations, the methods have not reached a practical level yet, and the conventional methods are still widely employed in practice despite of their well-known shortcomings. Current transformer saturation and large inrush currents are the most reported cause of the discrimination algorithms mal-operation. This paper deals with an efficient approach to discriminate internal faults from switching conditions in the power transformers which can solve these problems. The proposed method is based on utilising the feature of the non-saturation zone. The performance of the proposed method is tested using the simulations data besides to data collected from a prototype laboratory three-phase power transformer. Also, the proposed method is evaluated and compared with some well-known discrimination schemes including conventional and some new presented schemes by the same simulation and experimental data sets. The results validate the consistency and accuracy of the proposed method.

An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequency- based Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works.

Hyperbolic S-transform-based method for classification of external faults, incipient faults, inrush currents and internal faults in power transformers

In this study, a hyperbolic S-transform-based method is proposed to discriminate most important transient fault currents of transformers. First, the proposed method discriminates external faults from other disturbances. Then, the S-transform is applied to differential currents. An index is suggested by using absolute deviations of the S-matrix of differential currents to discriminate internal incipient faults in addition to inrush currents and internal faults. The relay issues an alarm signal in the case of the incipient fault but restrains during the magnetising inrush current. If an internal fault is recognised, the relay will issue a trip signal. To study the robustness of the suggested method, a program is developed in the MATLAB environment. The inputs of this program are differential current signals, derived from a system modelled by EMTP software. In order to simulate the internal incipient fault along with internal turn to turn and turn to earth faults, the transformer is modelled as 8×8 RL matrices, derived by using subroutine BCTRAN in EMTP. Also, differential currents are contaminated by noise and it is shown that the suggested method is not affected by noise and it can discriminate incipient faults, inrush currents, internal faults and external faults.

Decision Tree based discrimination between inrush currents and internal faults in power transformer

This paper presents a new approach for discriminating inrush currents from internal faults in case of power transformer protection using Decision Tree (DT). Differential current of one cycle inrush and internal faults are taken as inputs to the Decision Tree with corresponding target outputs as ‘0’ for inrush and ‘1’ for internal fault conditions. The DT is trained with inrush currents and internal faults with wide variations in operating parameters of the power network and provides accurate results in distinguishing inrush currents from internal faults. The proposed DT based algorithm is found to be accurate and robust, and thus highly suitable for power transformer protection.

A runs test‐based method for discrimination between internal faults and inrush currents in power transformers

AbstractA new method based on runs test is proposed to distinguish the inrush currents from the internal faults in power transformers, which is derived from the inherent differences between these waveforms. First, two statistical hypotheses that represent these differences are defined. Then by testing these hypotheses using runs test and by comparing the outputs of the hypotheses, a criterion to identify the internal fault is obtained. A total of 200 experimental cases are used to test the performance of the technique. In all of the tests, the proposed method accurately distinguished between fault and inrush currents without any errors. Also the suitable performance of the method is demonstrated by simulation of different faults and switching conditions on a power transformer. For this purpose, a small part of Iran power system involving a power transformer and the transmission lines on both sides of the transformer has been considered. To include effective factors on differential current components, the elements of this power system have been precisely modeled in PSCAD/EMTDC. Copyright © 2010 John Wiley & Sons, Ltd.

Power transformer differential protection using neural network Principal Component Analysis and Radial Basis Function Neural Network

Many methods have been used to discriminate magnetizing inrush from internal faults in power transformers. Most of them follow a deterministic approach, i.e. they rely on an index and fixed threshold. This article proposes two approaches (i.e. NNPCA and RBFNN) for power transformer differential protection and address the challenging task of detecting magnetizing inrush from internal fault. These approaches based on the pattern recognition technique. In the proposed algorithm, the Neural Network Principal Component Analysis (NNPCA) and Radial Basis Function Neural Network (RBFNN) are used as a classifier. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and over-excitation condition. The presented algorithm also makes use of ratio of voltage-to-frequency and amplitude of differential current for detection transformer operating condition. For both proposed cases, optimal number of neurons has been considered in the neural network architectures and the effect of hidden layer neurons on the classification accuracy is analyzed. A comparison among the performance of the FFBPNN (Feed Forward Back Propagation Neural Network), NNPCA, RBFNN based classifiers and with the conventional harmonic restraint method based on Discrete Fourier Transform (DFT) method is presented in distinguishing between magnetizing inrush and internal fault condition of power transformer. The algorithm is evaluated using simulation performed with PSCAD/EMTDC and MATLAB. The results confirm that the RBFNN is faster, stable and more reliable recognition of transformer inrush and internal fault condition.

A Novel Discriminative Approach Based on Hidden Markov Models and Wavelet Transform to Transformer Protection

In this paper we present a combinatorial scheme based on hidden Markov models (HMM) and wavelet transform (WT) to discriminate between magnetizing inrush currents and internal faults in power transformers. HMMs are powerful tools for transient classification which compute the maximum likelihood probability between training and testing data signals for identification. The WT is employed to extract certain features which reduce the computation burden of HMMs and enhance detection accuracy. The newly extracted feature efficiently discriminates between faults by different trends. The k-means clustering technique is applied to reduce the training procedure time investment. Since the discrimination method is based on the probabilistic characteristics of the signals without application of any deterministic index, more reliable and accurate classification is achieved. This method is independent of the selection thresholds. Based on the proposed algorithm a highspeed relay response (a quarter of a cycle) can be achieved. The suitable performance of this method is demonstrated by simulation of different faults and switching conditions on a power transformer using PSCAD/EMTDC software.

Internal faults in power transformers

The paper deals with internal faults in large power transformers. The explanation of such faults is followed by the description of an analytical method proposed for the calculation of the power flow during an interturn fault, including the current supplied by the power grid, the current in a group of short-circuited turns and the current in the spot of short-circuiting. The currents are calculated for cylindrical windings as a function of the fault area and its location with respect to the winding height for different structural configurations. An appropriate modification of Rogowski's coefficient is introduced. Computational results based on theoretical considerations are shown to be in good agreement with measurements carried out on a realistic model.

An Equivalent Instantaneous Inductance-Based Technique for Discrimination Between Inrush Current and Internal Faults in Power Transformers

A novel equivalent instantaneous inductance (EII)-based scheme is proposed to distinguish the inrush current from internal faults in power transformers, which is derived from the inherent difference of the magnetic permeability, due to the saturation and unsaturation, in the transformer iron core between the inrush current and an internal fault. First, the EII that represents these differences is defined, and its calculation methods and the criteria to identify the internal fault in the time and frequency domains are respectively developed. A total of 98 experimental cases are used to test the performance of the technique. In all of the tests, the inrush current is characterized by the drastic variation of the EII, but the EII of the faulty phase is kept almost constant. The experimental results verify the dependability, security, and fast operation of the proposed scheme. Besides, its easy implementation in real time is another advantage of the proposed method because of its simplicity.

Tracking space‐vector plot asymmetries in transformer differential relaying

PurposeDifferential relaying is one of the most widely used techniques for protecting power transformers. The purpose of this paper is to discuss and cover a developed methodology for analyzing the signals obtained from the differential protection of power transformers.Design/methodology/approachThe differential signal obtained from the protective relays of power transformers is analyzed in this paper, in order to establish a relation between time‐dependent symmetrical components and space vectors. As a result of the formulation of such a relation, specific patterns are obtained and classified for the plot of the space vector during fault and inrush conditions.FindingsWhat was found in the course of the work? This will refer to analysis, discussion, or results. It has been found that the discrimination between inrush and fault conditions is possible by observing a characteristic asymmetry in the plots of the space vector. A method for dealing with the said asymmetries based on the absolute value of the space vector as obtained from the differential signal is proposed and discussed. The theoretical approaches given in the paper are further validated through finite element simulations and laboratory tests, which include linear and non‐linear loads, in order to account for more severe exploitation conditions.Research limitations/implicationsA complete protective algorithm can be developed from the analysis of the methodology proposed, which avoids the spectral analysis, since the methodology is based in pattern analysis rather that in the latter technique.Practical implicationsThe methodology provides faster identification of the fault during inrush condition, since the spectral analysis is prevented.Originality/valueIt may be stated that the major contribution of the paper is the methodology proposed for identifying internal faults in power transformers using pattern characterization of the plot of the space vector.