Diagnosis Of Power Transformers Research Articles

Adsorption sensitivity and sensing performance of Au-doped Ti3C2O2 gas sensor for fault characteristic gases in power transformer

By analyzing the compositions and concentration of dissolved gases in power transformer oil, it is possible to effectively identify and predict the types and degrees of internal faults in oil-immersed transformers. This technology is currently an important method for fault diagnosis and prevention of large oil-immersed power transformers. This study established two different adsorption systems, namely Ti3C2O2 and Ti3C2O2 doped with Au atom, for the detection of four fault characteristic gases (CO, CO2, C2H4, CH4) in power transformer oil. The research results indicate that Ti3C2O2 exhibits inadequate adsorption capabilities for the above gases. The maximum adsorption energy is only −0.440 eV, which belongs to physical adsorption. After doping with Au atoms, the adsorption sensitivity of Ti3C2O2 for the above gases was significantly improved, with a significant increase in adsorption energy and charge transfer. The average adsorption energy has increased by 435%, and the minimum adsorption energy is also −0.660 eV. The adsorption process changed from physical adsorption to chemical adsorption. These research results provide a theoretical basis for Au-doped Ti3C2O2 gas sensor in detecting fault characteristic gases in power transformer.

Analysis of interturn short circuit in regulating winding of power transformer based on field-circuit coupling

The interturn short circuit fault is one of the common faults in power transformers. At present, research on interturn short circuit faults focuses on high, medium, and low voltage windings, while there is relatively little research on interturn short circuit in regulating windings. Specifically, there is a lack of reported studies on the transient electromagnetic processes, magnetic field distribution, and electromagnetic force characteristics of interturn short circuits in regulating windings unconnected to the circuit. This study presents an actual fault scenario involving interturn short circuits occurring in the untapped portion of the regulating windings of a specific power transformer. A field-circuit coupled model was established to analyze the transient electromagnetic processes during the fault, and the model’s effectiveness was validated by comparing its results with actual fault recording data. Additionally, the magnetic field distribution and electromagnetic force characteristics during the fault were analyzed, and discussions were carried out regarding various ratios of short-circuit turns in the regulating windings. The results indicate that even when an interturn short circuit occurs in the portion of the regulating winding that is not connected to the circuit, the current in the short-circuited turns can reach several tens of times the rated value. Additionally, the leakage magnetic field and the electromagnetic force experienced by the short-circuited ring also increase significantly. The short-circuit ratio has a significant impact on the current of the short-circuited ring, leakage magnetic field intensity, and electromagnetic force. This study contributes to a better understanding of the impact of interturn short-circuit faults in the untapped portion of the regulating windings, offering crucial technical support for fault diagnosis and prevention of power transformers.

New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers

The dissolved gas analysis of insulating oil in power transformers can provide valuable information about fault diagnosis. Power transformer datasets are often imbalanced, worsening the performance of machine learning-based fault classifiers. A critical step is choosing the proper evaluation metric to select features, models, and oversampling techniques. However, no clear-cut, thorough guidance on that choice is available to date. In this work, we shed light on this subject by introducing new tailored evaluation metrics. Our results and discussions bring fresh insights into which learning setups are more effective for imbalanced datasets.

Fault diagnosis of power transformers based on t-SNE and ECOC-TEWSO-SVM

Support Vector Machines (SVMs) have achieved significant success in the field of power transformer fault diagnosis. However, challenges such as determining SVM hyperparameters and their suitability for binary classification still exist. This paper proposes a novel method for power transformer fault diagnosis, called ECOC-WSO-SVM, which utilizes a White Shark Optimizer (WSO) and error correcting output codes to optimize SVMs. First, t-distributed Stochastic Neighbor Embedding (t-SNE) is employed to reduce the dimensionality of Dissolved Gas Analysis (DGA) features constructed using the correlation ratio method, from 26 dimensions. In addition, to effectively solve the hyperparameters of SVMs, a multi-strategy fusion method is proposed to improve the WSO, incorporating tent chaos initialization, elite opposite learning, and selection strategies, forming TEWSO, and its superior optimization performance is validated using IEEE CEC2021 test functions. Furthermore, to address the limitations of SVMs as a binary classifier, an error correcting output code fusion SVM is introduced, thus constructing a multi-class SVM model. Finally, the diagnostic performance of the ECOC-TEWSO-SVM model is validated using real-world data. Results demonstrate that the proposed model exhibits the best diagnostic performance compared to traditional models and those in the literature, thereby proving the significance and effectiveness of the proposed model.

Fault Diagnosis of Power Transformer in One-Key Sequential Control System of Intelligent Substation Based on a Transformer Neural Network Model

Fault Diagnosis of Power Transformer in One-Key Sequential Control System of Intelligent Substation Based on a Transformer Neural Network Model

Improving the accuracy of diagnostic predictions for power transformers by employing a hybrid approach combining SMOTE and DNN

In power transformers, accurate diagnosis prediction plays a crucial role in ensuring the reliability and efficiency of electrical systems. This paper presents an enhanced diagnostic prediction approach for power transformers by combining the Synthetic Minority Over-sampling Technique (SMOTE) and Deep Neural Networks (DNN). Our approach addresses the challenge of imbalanced data in power transformer diagnosis prediction by leveraging SMOTE to generate synthetic samples and balance the dataset. By effectively handling rare events through the augmentation of minority class instances, our predictive model becomes more robust. Additionally, using deep neural networks enables the exploration of complex patterns and relationships within the transformer dataset. The DNN architecture integrates multiple layers of learnable features, enabling the prediction model to extract high-level representations and achieve improved accuracy and performance. To evaluate the effectiveness of our proposed approach, we conducted extensive experiments using real-world power transformer datasets. Comparative analysis of the results demonstrates that the combination of SMOTE and DNN outperforms traditional classification techniques applied to imbalanced datasets. The proposed model achieves a higher training accuracy of 98.22 % and a testing accuracy of 94.6 % and mitigates the risks associated with misdiagnosis, contributing to power systems' overall reliability and maintenance efficiency.

Multichannel consecutive data cross-extraction with 1DCNN-attention for diagnosis of power transformer

Power transformer plays a critical role in grid infrastructure, and its diagnosis is paramount for maintaining stable operation. However, the current methods for transformer diagnosis focus on discrete dissolved gas analysis, neglecting deep feature extraction of multichannel consecutive data. The unutilized sequential data contains the significant temporal information reflecting the transformer condition. In light of this, the structure of multichannel consecutive data cross-extraction (MCDC) is proposed in this article in order to comprehensively exploit the intrinsic characteristic and evaluate the states of transformer. Moreover, for the better accommodation in scenario of transformer diagnosis, one dimensional convolution neural network attention (1DCNN-attention) mechanism is introduced and offers a more efficient solution given the simplified spatial complexity. Finally, the effectiveness of MCDC and the superior generalization ability, compared with other algorithms, are validated in experiments conducted on a dataset collected from real operation cases of power transformer. Additionally, the better stability of 1DCNN-attention has also been certified.

Fault diagnosis of power transformer based on improved particle swarm optimization OS-ELM

Fault diagnosis of power transformer based on improved particle swarm optimization OS-ELM

A two-step image segmentation based on clone selection multi-object emperor penguin optimizer for fault diagnosis of power transformer

Power transformer is one of the critical and valuableness apparatus in the secure operation of the power system. The infrared image fault diagnosis can reflect the status of the power transformer. However, how to detect the fault point of the diverse infrared images is a difficult problem. We present a two-stage multilevel threshold image segmentation method to finish the task of power transformers fault diagnosis. The multi-objective emperor penguin optimizer based on the clone selection strategy optimizes the multi-level image segmentation method for discovering the optimal threshold. The 3DOtsu and determination of optimal target number function as the fitness function and we convert them into multi-objective optimization problems. The proposed method is compared with the multi-objective optimization algorithms and novel fault diagnosis methods under the classic images and infrared images of the power transformer. The experiment results show that MOEPO/C has a good performance than others compared algorithms in Feature Similarity Index, Uniformity measure, Peak Signal‑to‑Noise Ratio, and CPU time. Especially, the proposed method obtains high accuracy in power transformer fault diagnosis.

Machine Learning Algorithms Fusion Based on DGA Data for Improving Fault Diagnosis of Electrical Power Transformer

Abstract Dissolved Gas Analysis (DGA) continues to be widely recognized as a valuable method in recent times for the early identification of issues in oil-filled power transformers. It has gained extensive adoption as a primary approach for the early discovery of these issues, relying on the analysis of dissolved gases. This contributes to enhancing the dependability of electrical systems. This paper proposes an efficient fusion method based on DGA data using the two best Machine Learning algorithms, the neural network (MLP), the naïve Bayes (NB) throughdata input vector ppm, a percentage input vector, and an Logarithmic input vector. The fusion method predictively combined the two classifiers and obtained a statistical evaluation: accuracy, recall, precision, and F-measure higher than both classifiers separately. The proposed fusion method was evaluated for performance using a test database and compared with conventional and smart methods. Results showed that the proposed model outperformed both traditional and intelligent methods in terms of diagnostic accuracy when using percentage and logarithmic input vectors. The Prediction Based Fusion (PBF) vector Percentages achieved an accuracy rate of 97.22%, while PBF vector Logarithmic achieved an accuracy rate of 95.83%. These rates were higher than those achieved by traditional methods, such as the Modified RRM/CEGB method 91.67% and Modified RRM/IEC method 90.28%. Additionally, the proposed model surpassed the accuracy rates of intelligent methods, such as CSUS ANN 88.89% and Conditional Probability 93.06%.

Hierarchical dynamic aggregation graph modeling of vibration for semi-supervised fault diagnosis of power transformer

Power equipment fault diagnostics is a critical aspect of ensuring the stability of the power grid system. However, it presents substantial challenges in obtaining labeled data that is spatial-temporal, multi-scale, and multi-domain, low noise for effective fault analysis and diagnosis. To address this issue, we propose a novel approach called hierarchical dynamic aggregation graph (HDAG) modeling for self-supervised fault diagnosis of power transformers using vibration data. Firstly, HDAG focuses on modeling the spatial and temporal correlations within the fault vectors, before converting them into time–frequency images for visualization. Secondly, our proposed fault diagnosis approach, (comprising the ST-sparse swin-transformer and multi-domain transformer fusion module), is integrated into the methodology. The ST-sparse swin transformer incorporates soft threshold modules, enabling the retention of relevant information while discarding irrelevant information. The multi-domain transformer fusion is designed to utilize the intra-domain and inter-domain signal characteristics to achieve a comprehensive feature representation. Finally, we present case studies based on experimental data that demonstrate the feasibility and effectiveness of our approach. Comparative evaluations against eight state-of-the-art techniques validate the improved information representation and diagnostic capabilities of our recommended strategy.

Application of improved support vector machine model in fault diagnosis and prediction of power transformers

AbstractPower transformers undertake the task of transforming voltage and transmitting electrical energy. Its operating status is directly connected with the stability and safety of the whole power system, and it is very important to judge the operating conditions of power transformers and diagnose fault types. The use of dissolved gas analysis technology in oil can provide preliminary fault diagnosis for transformers. However, with the increasing demand for fault diagnosis accuracy in modern electrical equipment, relying only on dissolved gas analysis technology in oil cannot satisfy the demands. To lift the transformer fault diagnosis accuracy, this study introduces the K‐means algorithm into the model and constructs a high‐precision and fast convergence diagnosis method and a power transformer fault location recognition model. In the example analysis, kernel functions were selected for training five typical gases to obtain the optimal parameters, and their prediction curves and errors were analyzed. Its diagnostic accuracy is 98.4%, and the error in all five gases is within 1 (uL/L). The average error of the improved support vector machine intelligent algorithm is lower than that of the previous model and other prediction methods. By testing the same sample data, the correctness of this method was verified. The significance of improving support vector machines lies in further improving the performance and applicability of the original support vector machine algorithm, providing a basis for future transformer maintenance and contributing to social development and continuous improvement of economic benefits.

Programming code using the Duval method for diagnosing power transformers and direct way

A transformer is a piece of equipment of great value in a public or private power substation in that it allows power to be supplied to the production processes. Thus, as it is used, the equipment is exposed to different environmental conditions such as level of contamination, salinity. , height, humidity, among others, as well as energy demand and quality, causing different types of electrical and thermal stress inside. That is why the objective of this study is to implement a programming code using the Duval method for the diagnosis of power transformers. For this, programming based on the supervised learning algorithm is used as a methodology to process the gas concentration levels contained in the dielectric oil obtained from the analysis of dissolved gases and for its interpretation the Duval method is used, which is presented in IEEE C57.104™-2019 standard. The designed code individually processes the dissolved gas analysis reports as well as large quantities of reports from the same transformer or from different ones, allowing the comparison of historical data of the equipment or equipment and the evaluation of its status according to the electrical stress to which it is subjected.

Experimental design and data analysis and optimization of mechanical condition diagnosis for transformer sets

Abstract The typical power transformer diagnosis approach is imprecise and unstable. A support vector machine classification algorithm is proposed, by designing an algorithm program that can improve the accuracy and speed of energy transformer diagnosis, the vibration signals of the surface twisting in different states are extracted by wavelet packet energy spectrum signal processing method, it is verified that the curve similarity between the vibration simulation model and the measured data is greater than 0.98, proving the simulation model’s validity. The calculation technique of online short circuit inductance is developed from the equivalent transformer model, and the variation error of simulation results is less than 0.05% when compared to the real transformer characteristics. The suggested state diagnostic technique successfully compensates for the drawbacks of the reactance method, which is incapable of detecting and judging the slightly loose or faulty winding. The method’s accuracy and superiority, as well as the practicability of the state diagnosis system, are demonstrated.

Data augmentation for fault diagnosis of oil-immersed power transformer

110 kV oil immersed transformer is a key part of the power transmission and transformation system, which determines the power quality and transmission efficiency. Its fault diagnosis can greatly reduce the maintenance cost and improve the economy. At present, the methods of transformer fault diagnosis have a strong dependence on the original data, and the size of the original data directly affects the effect of fault diagnosis. In order to change this situation and achieve higher accuracy of transformer fault diagnosis, this paper firstly uses the Conditional Variational Automatic Encoder (CVAE) composed of full connection layers to expand the original samples under each fault category. After data augmentation, the convolutional neural network (CNN) with strong feature extraction ability is selected as the classifier. Finally, the CVAE-CNN model is validated using public dataset and the result is compared to other machine learning algorithms.

Improved Support Vector Machine for Voiceprint Diagnosis of Typical Faults in Power Transformers

The traditional power transformer diagnosis method relies on a lot of experience knowledge and a complex sampling process, which brings great difficulties to the fault diagnosis work. To solve this problem, a fault feature extraction method based on fully adaptive noise set empirical mode decomposition (CEEMDAN) is proposed, and the hunter–prey optimization (HPO) algorithm is used to optimize the support vector machine (SVM) to identify and classify the voice print faults of power transformers. Firstly, the CEEMDAN algorithm is used to decompose the voicemarks into several IMF components. IMF components containing fault information are selected according to the envelope kurtosis index and reconstructed to generate new signal sequences. PCA dimensionality reduction is performed on the reconstructed signal, and the principal components are extracted with a high cumulative contribution rate as input to SVM. Then, the HPO-SVM algorithm is used to classify and identify transformer faults. Apply the proposed method to the diagnosis of typical faults in power transformers. The results show that the accuracy of this method in identifying various fault states of power transformers can reach 98.5%, and it has better classification performance than other similar methods.

Research on Transformer Partial Discharge Feature Extraction Based on Empirical Wavelet Transform and Multiscale Permutation Entropy

Aiming to extract efficiently the fault features of partial discharge in the process of fault diagnosis of power transformer, a method of combining Empirical Wavelet Transform (EWT) with Multiscale Permutation Entropy (MPE) is advanced to extract fault features of transformers partial discharge. Firstly, four different partial discharge pulse signals are analyzed by EWT method, and the fault signal is decomposed according to different frequency domain characteristics of the signal to obtain the intrinsic mode function (IMF) of the signal. Then, the calculated multi-scale permutation entropy of different IMFs to complete the fault feature extraction. Finally, the multi-scale entropy of the fault semaphore is used as the eigenvector of the Support Vector Machine (SVM) for glitch diagnosis, and the accurate systematization of the partial discharge semaphore of the transformer is realized. semaphore Compared with the Continuous Wavelet Transform (CWT), Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD) feature extraction way, it shows that the raised EWT-MPE is more valid and accurate in diagnosing and analyzing transformer partial discharge faults, and the accuracy of fault classification 96.43%.

A multi-stream multi-scale lightweight SwinMLP network with an adaptive channel-spatial soft threshold for online fault diagnosis of power transformers

Fault diagnosis of power equipment is extremely crucial to the stability of power grid systems. However, complex operating environments, high costs and limitations of single-modal signals are the biggest bottlenecks. To this end,a multi-tream, multi-scale lightweight Swin multilayer perceptron (MLP) network (MLSNet) with an adaptive channel-spatial soft threshold is proposed in this paper. First, a Res2net-based feature-enhanced method is used to learn the correlated features of vibration and voltage multi-modal signals. Second, a novel MLSNet is designed to combine the benefits of Swin transformers with an MLP with a lightweight convolutional neural network and employs a staged model to extract various scale features. Third, an adaptive deep fusion approach employing a channel-spatial soft threshold module is used to integrate and recalibrate staged information at different scales. The overall accuracy of the proposed method can reach 98.73% in various experiments, potentially making it an effective method for online fault diagnosis of power transformers.

Fault diagnosis of oil-immersed transformer based on MGTO-BSCN

In order to improve the accuracy and reliability of fault diagnosis of oil-immersed power transformers, a fault diagnosis method based on the Modified Artificial Gorilla Troops Optimizer (MGTO) and the Stochastic Configuration Networks with Block Increments (BSCN) is proposed. First, the original artificial gorilla troop optimization algorithm is improved, which effectively improves the convergence speed and optimization accuracy of the algorithm. Secondly, the conventional Stochastic Configuration Networks (SCN) learning methodology is modified when the fault diagnosis model is constructed. The original SCN adopts point incremental approach to gradually add hidden nodes, while BSCN adopts block increment approach to learn features. It significantly accelerates training. MGTO algorithm is used to jointly optimize regularization parameter and scale factor in BSCN model, and the fault diagnosis model with the highest accuracy is constructed. The experimental results show that the accuracy of MGTO-BSCN for transformer fault diagnosis reaches 95.9%, which is 3.5%, 9.9% and 11.7% higher than BSCN fault diagnosis models optimized by GTO, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) respectively, reflecting the superiority of MGTO algorithm. Meanwhile, the comparison with the traditional model shows that the proposed method has obvious advantages in diagnostic effect.

Research on Transformer Voiceprint Anomaly Detection Based on Data-Driven

Condition diagnosis of power transformers using acoustic signals is a nonstop, contactless method of equipment maintenance that can diagnose the transformer’s type of abnormal condition. To heighten the accuracy and efficiency of the abnormal method of diagnosing abnormalities by sound, a method for abnormal diagnosis of power transformers based on the Attention-CNN-LSTM hybrid model is proposed. This collects the sound signals emitted by the real power transformer in the normal state, overload, and the discharge condition. It preprocesses the sound signals to obtain the MFCC characteristics of the sound signals. It is then grouped into a set of sound feature vectors by the first- and second-order differences, and enters the Attention-CNN-LSTM hybrid model for training. The training results show that the Attention-CNN-LSTM hybrid model can be used for the status sound detection of power transformers, and the recognition of the three states can achieve an accuracy rate of more than 99%.