Power Transformer Fault Diagnosis Research Articles

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.

Research on transformer fault diagnosis based on active learning with imbalanced data of dissolved gas in oil.

The power transformer is the core equipment of the power system, a sudden failure of which will seriously endanger the safety of the power system. In recent years, artificial intelligence techniques have been applied to the dissolved gas analysis evaluation of power transformers to improve the accuracy and efficiency of power transformer fault diagnosis. However, most of the artificial intelligence techniques are data-driven algorithms whose performance decreases when the data are limited or significantly imbalanced. In this paper, we propose an active learning framework for power transformer dissolved gas analysis, in which the model can be dynamically trained based on the characteristics of the data and the training process. In addition, this paper also improves the original active learning spatial search strategy and uses the product of sample feature differences instead of the original sum of differences as a measure of sample difference. Compared to passive learning algorithms, the novel approach could significantly reduce the data labeling effort while improving prediction accuracy.

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

Privacy-Preserving Federated Learning for Power Transformer Fault Diagnosis With Unbalanced Data

Privacy-Preserving Federated Learning for Power Transformer Fault Diagnosis With Unbalanced Data

Power transformer fault diagnosis using dynamic multiscale graph modeling and M2SGCN network based on statistical fusion

Power equipment fault diagnostics hold significant importance for the stability of power grid systems. In pursuit of this objective, this paper proposes a fault diagnosis method that utilizes dynamic multiscale graph (DMG) modeling and the multiscale multi-stream GCN(M2SGCN) network, incorporating statistical fusion. Specifically, a novel DMG modeling method is proposed to derive visibility graph data and horizontal visibility graph data from vibration signals across multiple scales. Next, a comprehensive neural network architecture named M2SGCN is established to learn global and local features simultaneously, providing a more precise representation. Subsequently, a Dempster Shafer evidence theory statistical fusion technique combined with an adaptive threshold model (DSTFusion) is utilized to integrate primary decision results for enhanced fault diagnosis accuracy. In addition, two datasets obtained from single-phase and three-phase power transformers are analyzed to demonstrate the evolution process. When compared to state-of-the-art indicators such as accuracy, precision, recall, and F1 scores, the method proposed excels in multiple aspects, successfully detecting fault states before their occurrence and achieving outstanding performance.

Machine learning based multi-method interpretation to enhance dissolved gas analysis for power transformer fault diagnosis

Accurate interpretation of dissolved gas analysis (DGA) measurements for power transformers is essential to ensure overall power system reliability. Various DGA interpretation techniques have been proposed in the literature, including the Doernenburg Ratio Method (DRM), Roger Ratio Method (RRM), IEC Ratio Method (IRM), Duval Triangle Method (DTM), and Duval Pentagon Method (DPM). While these techniques are well documented and widely used by industry, they may lead to different conclusions for the same oil sample. Additionally, the ratio-based methods may result in an out-of-code condition if any of the used gases fall outside the specified limits. Incorrect interpretation of DGA measurements can lead to mismanagement and may lead to catastrophic consequences for operating power transformers. This paper presents a new interpretation technique for DGA aimed at improving its accuracy and consistency. The proposed multi-method approach employs s scoring index and random forest machine learning principles to integrate existing interpretation methods into one comprehensive technique. The robustness of the proposed method is assessed using DGA data collected from several transformers under various health conditions. Results indicate that the proposed multi-method, based on the scoring index and random forest; offers greater accuracy and consistency than individual conventional interpretation methods alone. Furthermore, the multi-method based on random forest demonstrated higher accuracy than employing the scoring index only.

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

Research on fault diagnosis for power transformer based on random forests and wavelet transform

Research on fault diagnosis for power transformer based on random forests and wavelet transform

Research on fault diagnosis for power transformer based on random forests and wavelet transform

Research on fault diagnosis for power transformer based on random forests and wavelet transform

IGSA-PNN-based Methods for Power Transformer Fault Diagnosis

To enhance the precision of power transformer fault diagnosis, it is necessary to make improvements. Aiming at the shortcomings of Probabilistic Neural Network (PNN) network experience selection smoothing factor and avoiding the shortcomings of traditional Gravitational Search Algorithm (GSA) easy 0to fall into local optimum and convergence speed slow, a Probabilistic Neural Network (PNN) model using chaos sequence to improved GSA for power transformer fault diagnosis is proposed. Firstly, chaos sequence is used to increase the diversity of gravitational particles to avoid falling into local optimum during the training process. Then, the improved GSA algorithm is used to optimize the parameters of the PNN model itself to improve the prediction accuracy of the model. Finally, the prediction results are compared with the prediction results of other traditional diagnostic models. The results show that IGSA-PNN fault diagnosis model performs better in generalization ability and classification accuracy.

Dissolved Gas Analysis for Power Transformer Fault Diagnosis Combining Domain Knowledge and Capsule Network

Dissolved Gas Analysis for Power Transformer Fault Diagnosis Combining Domain Knowledge and Capsule Network

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%.

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. The IEC-599 standard Ratio Method is an accurate method that evaluates the DGA. All the classical approaches have limitations because they cannot diagnose all faults accurately. Precisely diagnosing defects in power transformers is a significant challenge due to their extensive quantity and dispersed placement within the power network. To deal with this concern and to improve the reliability and precision of fault diagnosis, different Artificial Intelligence techniques are presented. In this manuscript, an artificial neural network (ANN) is implemented to enhance the accuracy of the Rogers Ratio Method. On the other hand, it should be noted that the complexity of an ANN demands a large amount of storage and computing power. In order to address this issue, an optimization technique is implemented with the objective of maximizing the accuracy and minimizing the architectural complexity of an ANN. All the procedures are simulated using the MATLAB R2023a software. Firstly, the authors choose the most effective classification model by automatically training five classifiers in the Classification Learner app (CLA). After selecting the artificial neural network (ANN) as the sufficient classification model, we trained 30 ANNs with different parameters and determined the 5 models with the best accuracy. We then tested these five ANNs using the Experiment Manager app and ultimately selected the ANN with the best performance. The network structure is determined to consist of three layers, taking into consideration both diagnostic accuracy and computing efficiency. Ultimately, a (100-50-5) layered ANN was selected to optimize its hyperparameters. As a result, following the implementation of the optimization techniques, the suggested ANN exhibited a high level of accuracy, up to 90.7%. The conclusion of the proposed model indicates that the optimization of hyperparameters and the increase in the number of data samples enhance the accuracy while minimizing the complexity of the ANN. The optimized ANN is simulated and tested in MATLAB R2023a—Deep Network Designer, resulting in an accuracy of almost 90%. Moreover, compared to the Rogers Ratio Method, which exhibits an accuracy rate of just 63.3%, this approach successfully addresses the constraints associated with the conventional Rogers Ratio Method. So, the ANN has evolved a supremacy diagnostic method in the realm of power transformer fault diagnosis.

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.

Transformer fault diagnosis based on MPA-RF algorithm and LIF technology

Power transformers are essential components in power systems used to regulate voltage, transmit electrical energy, provide isolation, and match loads. They contribute to efficient and reliable electricity transmission and distribution. However, traditional methods for diagnosing transformer faults are time-consuming, not suitable for online monitoring, and greatly affected by environmental conditions. In this experiment, we propose the use of laser-induced fluorescence (LIF) technology for transformer fault detection. LIF technology is a method for analyzing and detecting specific molecules or atoms in samples. It combines laser technology with fluorescence measurements, making it a powerful analytical tool. It achieves high sensitivity and selectivity in analyzing molecules and atoms by exciting and detecting fluorescence in the sample. This makes it an important technology in scientific research and practical applications. Furthermore, LIF technology has not been previously applied to power transformer fault diagnosis. Therefore, this experiment introduces a transformer fault diagnosis model based on the marine predators algorithm (MPA) optimized random forest (RF) algorithm and LIF spectroscopy technology. Four different oil samples were selected for experimentation: crude oil, thermally faulty oil, partially moist oil, and electrically faulty oil. First, LIF technology for collect spectral images and data from the different fault oil samples. The obtained spectral data was preprocessed using two methods, multivariate scatter correction (MSC) and standardization method (SNV). Then, principal component analysis (PCA) and kernel principal component analysis (KPCA) for reducing the dimensionality of the preprocessed spectral data. Finally, the RF model, MPA-RF model, and PSO-RF model were established; and the reduced data was input into the model for training. Through comparisons of the predictions on the test set, evaluation metrics of the algorithm (including fitting coefficient, MSE, RMSE, and RMSE), and iteration convergence curves, the best transformer fault diagnosis model was identified. The results show that the MSC-KPCA-MPA-RF model has the best matching resule, with a fitting coefficient of 0.9963 and a mean square error of 0.0047. The SNV-PCA-MPA-RF model has the worst fitting effect, with a fitting coefficient of 0.9840 and a mean square error of 0.0199. Through comparisons of the convergence of different models, the MSC-KPCA-MPA-RF model has the best convergence and is the most applicable model for transformer fault diagnosis in this experiment. This model has significant implications for ensuring the safety of the power system.

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.

Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

Investigation of the Effect of Data Preprocessing Methods on the Classifier Performance in Power Transformer Fault Diagnosis

Investigation of the Effect of Data Preprocessing Methods on the Classifier Performance in Power Transformer Fault Diagnosis

Filter-wrapper combined feature selection and adaboost-weighted broad learning system for transformer fault diagnosis under imbalanced samples

Intelligent fault diagnosis is a rapidly evolving field within power engineering. Using gas-in-oil data is a reliable method for transformer fault diagnosis that has been widely adopted in the power industry. However, traditional machine learning methods often suffer from low diagnostic accuracy due to the lack of a clear and effective feature set for gas-in-oil data as well as an imbalance between classes of sample size. To overcome this challenge, this paper proposes a novel transformer fault diagnosis model that utilizes a Filter-Wrapper Combined Feature Selection method and an AdaBoost integrated weighted broad learning system (AdaBoost-WBLS). More specifically, the original data is expanded to extract meaningful features, and the Filter-Wrapper combined feature selection method is used to eliminate preliminary redundancy, relevance, and significance of current features. The Wrapper algorithm is then used for precise screening to obtain the optimal feature subset, which effectively improves the quality of transformer features. Furthermore, to address the issue of imbalanced transformer samples, an improved BLS and AdaBoost integration method is introduced, and a fault diagnosis model based on AdaBoost-WBLS is proposed. Compared with existing power transformer fault diagnosis methods, the proposed method has a more accurate and balanced effect on fault classification. Overall, this paper provides a comprehensive and effective approach to transformer fault diagnosis, which has important implications for the reliability and safety of power systems.