Identification Of Faults In Transformers Research Articles

A Method for Identifying External Short-Circuit Faults in Power Transformers Based on Support Vector Machines

Being a vital component of electrical power systems, transformers significantly influence the system stability and reliability of power supplies. Damage to transformers may lead to significant economic losses. The efficient identification of transformer faults holds paramount importance for the stability and security of power grids. The existing methods for identifying transformer faults include oil chromatography analysis, temperature assessment, frequency response analysis, vibration characteristic examination, and leakage magnetic field analysis. These methods suffer from limitations such as limited sensitivity, complexity in operation, and a high demand for specialized skills. In this paper, we propose a method to identify external short-circuit faults of power transformers based on fault recording data on short-circuit currents. It involves analyzing the current signals of various windings during faults, extracting appropriate features, and utilizing a classification algorithm based on a support vector machine (SVM) to determine fault types and locations. The influence of different kernel functions on the classification accuracy of SVM is discussed. The results indicate that this method can proficiently identify the type and location of external short-circuit faults in transformers, achieving an accuracy rate of 98.3%.

Identification method for inter-turn faults in transformers based on digital twin concept

A transformer inter-turn fault identification method is proposed based on the digital twin concept to tackle the challenges of high operational complexity and low accuracy associated with traditional transformer fault identification methods. Initially, the Bald Eagle Search algorithm is employed to optimize the critical parameters of the Extreme Learning Machine (ELM), determining the optimal input layer weight and hidden layer threshold of the Extreme Learning Machine. Subsequently, leveraging the digital twin concept, a digital replica of the physical transformer is established, enabling multi-physical field coupling simulation encompassing electrical, thermal, and acoustic domains to elucidate the variation patterns of various physical parameters across different operational scenarios and fault scenarios. Furthermore, key physical characteristic parameters such as sound pressure and winding hot spot temperature are carefully selected to drive a fault identification model tailored to inter-turn faults within the framework of the digital twin concept. Through a detailed investigation using 630 kV A/10 kV transformers as a case study, the results exhibit an impressive fault identification accuracy of 95.24% for the proposed method. Comparative analysis reveals notable enhancements in fault identification accuracy of 12.22%, 7.85%, and 3.73% for ELM, Support Vector Machine and Tuna Swarm Optimization—ELM models, respectively. These findings underscore the effectiveness and practicality of the transformer inter-turn fault identification method based on the digital twin concept, offering valuable insights for the real-time monitoring and diagnosis of inter-turn faults in transformers.

Transformer faults identification via fuzzy logic approach

The need for a constant electricity supply is at an alarming rate especially in the 21st century due to the high rate of increase in industrialization across the globe. Conventional protection schemes such as differential relays, Buchholz relay, and other techniques such as genetic algorithms and artificial neural networks, do not match the precision and reliability needed for transformer fault indentification, due to their complexity in computation, tedious training system, time consumption, and need the of human experts. The method proposed in this research is the use of a fuzzy inference system in detecting potential faults in power system transformers. The faults in the transformer were observed and analyzed using a simulation system of MATLAB/Simulink software. The suggested approach ensures swift identification of faults as it relies on if-then rules and only uses current and voltage measurements with 100% independence toward the power flow direction, making it highly reliable and simple to implement compared to other techniques for transformer fault identification.

A novel approach for oil-based transformer fault identification in electrical secondary distribution networks

Oil-immersed transformers are mostly installed equipment in electrical power networks. Thus, ensuring the reliability of these equipment is paramount. Fault identification is one of the measures taken to ensure the reliability. Concerning fault identification mechanisms, several methods exist; one of which is the dissolved gas analysis (DGA) tool that has been widely used purposefully for oil-immersed transformers. The tool is used to capture and analyze the gases dissolved in the oil. Studies have proposed various fault identification methods based on this tool such as the Dornenburg, key gas, and International Electrotechnical Commission (IEC) standards methods using DGA data. However, the accuracy of these methods seems to be limited by the context, such as the type and number of parameters used. In this study, the novel hybrid fault identification method based on multilayer neural networks (MLANN) and expert knowledge is proposed to improve the accuracy fault identification process by considering the specific characteristic parameters extracted from the historical DGA dataset. The proposed hybrid method improves accuracy by 14.46% when compared to 12 benchmark methods. This improvement portrays that our method can be integrated into a scheduling platform for maintenance decision support in an electrical power network with accuracy. Furthermore, the promising accuracy in transformer fault identification is expected to increase safety and power reliability.

An Efficient Noise Reduction Method for Power Transformer Voiceprint Detection Based on Poly-Phase Filtering and Complex Variational Modal Decomposition

The transformer is a core component in power systems, and its reliable operation is crucial for the safety and stability of the power grid. Transformer faults can be diagnosed early using acoustic signals. However, effective acoustic features are often affected by complex environmental noise, which reduces the accuracy of fault identification. As a solution, this study proposes a poly-phase filtering (PF)-based noise reduction algorithm for complex variational mode decomposition (CVMD) of multiple acoustic sources in power transformers. The algorithm dissects the received signal from the power transformer into subbands, downsizing their sampling rates via PF. Subsequently, it independently targets noise reduction within these subbands, focusing on specific acoustic sources. Leveraging complex signal transformations, we extend the variational mode decomposition (VMD) to mitigate the field of complex signals and utilize the CVMD to reduce the noise of each acoustic source within each subband for every acoustic source. The experimental results reveal that the proposed method effectively separates and denoises the sound signal of transformer operation under the interference of multiple sound sources in the substation. Its powerful noise reduction ability, combined with minimal computational complexity, greatly improves the accuracy of transformer fault identification and the reliability of the system.

Fault identification for power transformer based on dissolved gas in oil data using sparse convolutional neural networks

AbstractThis paper addressed the challenges associated with the complexity, numerous parameters, computational resource demands, and slow processing speed of transformer fault identification models based on deep learning technologies. Sparse convolutional neural network (CNN) approach is proposed for identifying faults related to dissolved gases in oil. Leveraging an improved Gramian angular field, one‐dimensional fault samples are converted into two‐dimensional feature images and data augmentation is implemented to meet the input requirements of deep learning models. Building upon visual geometry group (VGG)19 and residual networks (ResNet)50 networks for fault diagnosis, sparsity techniques are introduced through pruning, the fusion of convolution layers and batch normalization layers, and parameter quantization. Numerical experiments and performance evaluations on dissolved gas in transformer oil fault data demonstrate that the proposed method effectively reduced model complexity, minimized parameter count, conserved computational resources, and improved processing speed while maintaining a considerable level of fault identification accuracy. This made it applicable to edge computing platforms characterized by small form factors and low power consumption in the power industry.

Accurate Identification of Transformer Faults From Dissolved Gas Data Using Recursive Feature Elimination Method

Dissolved gas analysis (DGA) of insulating oils is one of the most popular methods to detect incipient faults in power transformers. However, appropriate feature selection is crucial for accurately detecting incipient faults using DGA data. Another issue is the unavailability of a balanced DGA dataset, which can hamper the fault classification accuracy. Considering these two issues, this article proposes a novel and accurate fault classification framework using gas ratios as features obtained from the DGA data of power transformers. The obtained unbalanced DGA data was initially balanced using the synthetic minority oversampling technique (SMOTE) in the data pre-processing stage. Following this, an efficient feature selection algorithm, namely, recursive feature elimination (RFE) was used to select the best possible features prior to the fault classification using three benchmark machine learning (ML) classifiers, namely, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor (KNN), multiclass support vector machines (SVMs), and extreme gradient boost (XGBoost). The proposed classification model was tested on the DGA data obtained from the local power utility and on the benchmark IEC TC-10 database. Investigations revealed that the proposed classification model delivered detection accuracy of 98.84% and 97.43%, respectively. The proposed method may be reliably used to diagnose incipient faults in power transformers.

Detection technology of partial discharge in transformer based on optical signal

The detection of partial discharge (PD) in transformers is of great significance for preventing insulation defects from developing into insulation failures. Transformer partial discharge detection technology includes pulse current method, radio frequency measurement method, UHF method, ultrasonic detection method, gas chromatography, and optical detection method. Among them, the optical detection method has the characteristics of strong anti-electromagnetic interference ability, and the signal can be transmitted and received without loss. The optical detection method has unique advantages. In this paper, an optical signal measuring device for partial discharge inside the transformer is designed and fabricated, the typical insulation defect inside the transformer is simulated, and the optical signal characteristics of typical partial discharge are obtained. Compared with the pulse current method, it is found that the magnitude of partial discharge can be characterized by the integral of the optical signal. The characteristic of the optical signal phase resolved partial discharge (PRPD) spectrum of typical insulation defects is obtained. The PRPD spectrum of the optical signals of different defects is obviously different. It is feasible to draw the PRPD spectrum and identify the defect types according to the optical signal, which can be further applied to the identification of internal faults in transformers.

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.

Research on transformer fault diagnosis based on GWO-RF algorithm

Efficient fault diagnosis of power transformer can effectively ensure the safe and stable operation of power system. Considering that the effect of traditional Random Forest (RF) is seriously affected by the initial parameters, this paper proposes a RF algorithm based on Grey Wolf Optimization (GWO-RF) to improve the accuracy of transformer fault identification. The algorithm uses GWO to optimize the total number of decision trees and the depth of the maximum decision tree, effectively balancing the accuracy of the RF model with the ability to generalize. In this paper, the Dissolved Gas in oil is taken as the fault characteristic quantity, and 335 sets of data are used to form the fault set. Three different data sets are verified by GWO-RF, traditional RF and IEC three ratio method. The experimental results show the effectiveness of GWO-RF method.

Transformer Fault Identification with an IF-1DCNN Based on Informative Integration of Heterogeneous Sources

Only using single feature information as input feature cannot fully reflect the transformer fault classification and improve the accuracy of transformer fault diagnosis. To address the above problem, the convolution neural networks’ model is applied for transformer fault assessment designed to implement an end-to-end “different space feature extraction + transformer state diagnosis classification” to enable information from possibly heterogeneous sources to be integrated. This method integrates various feature information of the power transformer operation state to form the isomeric feature, and the model can be used to automatically extract different feature spaces’ information from isomeric feature quantity using its unique one-dimensional convolution and pooling operations. The performance of the proposed approach is compared with that of other models, such as a support vector machine (SVM), backpropagation neural network (BPNN), deep belief network (DBNs), and others. The experimental results show that the proposed one-dimensional convolution neural networks based on an isomeric feature (IF-1DCNN) can accurately classify the fault state of transformer and reduce the adverse interaction between different feature space information in the mixed feature, which has a good engineering application prospect.

An Intelligent Genetic Fuzzy Classifier for Transformer Faults

ABSTRACT Classification of faults in transformers with high accuracy is fundamental to ensuring good power quality with least interruptions. Our current work develops an intelligent genetic algorithm (GA)-tuned fuzzy classifier for transformer fault identification. The proposed classifier is able to segregate all fault types using dissolved gas analysis (DGA) samples from real power transformers of HPSEB (India) and other sources. DGA samples have been pre-processed using the J48 algorithm. We propose to replace the conventional action selection procedure of reinforcement learning by a GA-based optimizer. The classifier is able to garner very high classification accuracy which is higher than the one obtained with benchmark fuzzy Q learning (FQL) and other conventional classifiers. With our approach, the average fault classification rate achieved is 91.85% (FQL) and 97.51% genetic algorithm fuzzy Q-learning (GAFQL) though with a slightly higher computational complexity over the FQL. Our proposed classifier could serve as an important tool in ensuring the healthy operation of power transformers.

Novel Transformer Fault Identification Optimization Method Based on Mathematical Statistics

Most power transformer faults are caused by iron core and winding faults. At present, the method that is most widely used for transformer iron core and winding faults identification is the vibration analysis method. The vibration analysis method generally determines the degree of fault by analyzing the energy spectrum of the transformer vibration signal. However, the noise reduction step in this method is complicated and costly, and the effect of denoising needs to be further improved to make the fault identification results more accurate. In addition, it is difficult to perform an accurate determination of the early mild failure of the transformer due to the effect of noise on the results. This paper presents a novel mathematical statistics method based on the vibration signal to optimize the vibration analysis method for the short-circuit failure of the transformer winding. The proposed method was used for linear analysis of the transformer vibration signal with different degrees of short-circuit failure of the transformer winding. By comparing the slope value of the transformer vibration signal cumulative probability distribution curve and analyzing the energy spectrum of the signal, the degree of short-circuit failure of the transformer winding was identified quickly and accurately. This method also simplified the signal denoising process in transformer fault detection, improved the accuracy of fault detection, reduced the time of fault detection, and provided good predictability for early mild faults of the transformer, thereby reducing the hidden hazards of operating the power transformer. The proposed optimization procedure offers a new research idea in transformer fault identification.

Identification of interturn faults in power transformers by means of generalized symmetrical components analysis

The paper deals with experimental identification of transformer internal faults, an important factor in reliability and sustainability of power supply systems. Task of identification of transformer internal faults requires increasing sensitivity of relay protection by calculation of components most sensitive to interwire faults from transformer current. In order to study internal faults in transformer, the model in Simulink MATLAB was developed on the basis of transformer constitutive equations. Transformer with short circuited wires was simulated as a multiwinding transformer. We provide the calculation of transformer parameters. Model was applied for analysis of transients in power transformers, such as interwire fault, transformer inrush, and fault in transformer connections. Analysis of power transformer internal faults by means of time-dependent symmetrical components of currents is provided. These symmetrical components were calculated for the first harmonic of current by means of discrimination of firs harmonic by low-pass filter and compensating elements implementing phase shift. Described method allows calculation of symmetrical components during transient and under non-sinusoidal conditions. Simulation results showed the advantage of instantaneous symmetrical components of other direct values. Those components were implemented in relay protection algorithms for identification of internal faults in transformers.

Proposed diagnostic methodology using the cross‐correlation coefficient factor technique for power transformer fault identification

This study investigates the impact of electrical parameter variation of a high-frequency transformer model on its sweep frequency response analysis (SFRA) signature to help in classification and interpretation. The simulations have been done using MATLAB and compared with the reference data. The results of SFRA measurements are repeatable up to and beyond 1MHz. The proposed diagnostic methodology using the Cross-Correlation Coefficient Factor (CCF) is used to identify the transformer faults. CCF used to measure the degree of relationship between two variables that establish a relation between the predicted and actual data set. The results of this proposed methodology using the CCF compared with existing Chinese Standard factor (CSF) indicate that, the proposed method is valid to identify the transformer faults. Characteristics of the proposed scheme are fully analyzed by extensive MATLAB simulation studies that clearly reveal that this method can accurately identify the transformer faults compared with CSF. And also does not affected by different fault conditions such as transformer normal condition, Turn to Turn Fault for both HV, LV sides, Axial Fault and/or Radial Faults on both sides, Short Circuit Fault between H.V and L.V Sides, Short Circuit to Ground Fault for both HV, LV sides.

Research on Transformer Fault Diagnosis Method Based on Artificial Immune Network and Fuzzy C-Means Clustering Algorithm

The transformer is one of the indispensable equipment in transformer substation, it is of great significance for fault diagnosis. In order to accurately judge the transformer fault types, an algorithm is proposed based on artificial immune network combined with fuzzy c-means clustering to study on transformer fault samples. Focus on the introduction of data processing of transformer faults based on artificial immune network, the identification of transformer faults based on fuzzy c-means clustering, and the simulation process. The experimental results show that the proposed algorithm can classify power transformer fault types effectively, and the algorithm has a good application prospect in the transformer fault diagnosis.

Adaptive neuro-fuzzy inference system approach for simultaneous diagnosis of the type and location of faults in power transformers

Electrical, mechanical, and thermal stresses can degrade the quality of the insulation in power transformers, causing faults [1]. Several methods are used for fault diagnosis in transformers, e.g., dissolved gas analysis (DGA), measurement of breakdown voltage, and tan ��, pollution, sludge, and interfacial tension tests [2]. Of these, DGA is the most frequently used. Thermal and electrical stresses result in fracture of the insulating materials and the release of several gases. Analysis of these gases may provide information on the type of fault. Various standards have been suggested for the identification of transformer faults based on the ratio of dissolved gases in the transformer oil, e.g., International Electrotechnical Commission (IEC) standards [3]���[7], and these standards has been quoted in many papers, e.g., [8]���[15]. However, they are incomplete in the sense that, in some cases, the fault cannot be diagnosed or located accurately. Intelligent algorithms, e.g., wavelet networks [16], neuro-fuzzy networks [17], [18], fuzzy logic [8], [12], and artificial neural networks (ANN) [2], [9], [10], [19], [20] have been used to improve the reliability of the diagnosis. In these algorithms, the type of fault is diagnosed first, and the fault is then located using the ratio of the concentrations of CO2 and CO dissolved in the transformer oil [21], [22]. The algorithms are not entirely satisfactory. The wavelet network has high efficiency but low convergence, the fuzzy logic method has a limited number of inputs and, in some cases, it is very difficult to derive the logic rules, and the ANN need reliable training patterns to improve their fault diagnosis performance. In this paper, we present a new method for simultaneous diagnosis of fault type and fault location. It uses an adaptive neuro- fuzzy inference system (ANFIS) [23]���[27], based on DGA. The ANFIS is first ���trained��� in accordance with IEC 599 [3], so that it acquires some fault determination ability. The CO2/CO ratios are then considered additional input data, enabling simultaneous diagnosis of the type and location of the fault. The results obtained by applying it to six transformers are presented and compared with the corresponding results obtained using ANN and some other standards and methods.