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A key component for the performance, availability, and reliability of power grids is the power transformer. Although power transformers are very reliable assets, the early detection of incipient degradation mechanisms is very important to preventing failures that may shorten their residual life. In this work, a comparative analysis of standard machine learning (ML) algorithms (such as single and ensemble classification algorithms) and automatic machine learning (autoML) classifiers is presented for the fault diagnosis of power transformers. The goal of this research is to determine whether fully automated ML approaches are better or worse than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from dissolved gas analysis results. The methodology uses a transformer fault database (TDB) gathered from specialized databases and technical literature. Fault data were processed using the Duval pentagon diagnosis approach and user–expert knowledge. Parameters from both single and ensemble classifiers were optimized through standard machine learning procedures. The results showed that the best-suited algorithm to tackle the problem is a robust, automatic machine learning classifier model, followed by standard algorithms, such as neural networks and stacking ensembles. These results highlight the ability of a robust, automatic machine learning model to handle unbalanced power transformer fault datasets with high accuracy, requiring minimum tuning effort by electrical experts. We also emphasize that identifying the most probable transformer fault condition will reduce the time required to find and solve a fault.

Dissolved gas analysis (DGA) is considered to be the most convenient and effective approach for transformer fault diagnosis. Due to their excellent performance and development potential, chemiresistive gas sensors are anticipated to supersede the traditional gas chromatography analysis in the dissolved gas analysis of transformers. However, their high operating temperature and high power consumption restrict their deployment in battery-powered devices. This review examines the underlying principles of chemiresistive gas sensors. It comprehensively summarizes recent advances in low-power gas sensors for the detection of dissolved fault characteristic gases (H2, C2H2, CH4, C2H6, C2H4, CO, and CO2). Emphasis is placed on the synthesis methods of sensitive materials and their properties. The investigations have yielded substantial experimental data, indicating that adjusting the particle size and morphology structure of the sensitive materials and combining them with noble metal doping are the principal methods for enhancing the sensitivity performance and reducing the power consumption of chemiresistive gas sensors. Additionally, strategies to overcome the significant challenge of cross-sensitivity encountered in applications are provided. Finally, the future development direction of chemiresistive gas sensors for DGA is envisioned, offering guidance for developing and applying novel gas-sensitive sensors in transformer fault diagnosis.

To address the problems of low accuracy in fault diagnosis of oil-immersed transformers, poor state perception ability and real-time collaboration during diagnosis feedback, a fault diagnosis method for transformers based on the integration of digital twins is proposed. Firstly, fault sample balance is achieved through Iterative Nearest Neighbor Oversampling (INNOS), Secondly, nine-dimensional ratio features are extracted, and the correlation between dissolved gases in oil and fault types is established. Then, sparse principal component analysis (SPCA) is used for feature fusion and dimensionality reduction. Finally, the Aquila Optimizer (AO) is introduced to optimize the parameters of the Kernel Extreme Learning Machine (KELM), establishing the optimal AO-KELM diagnosis model. The final fault diagnosis accuracy reaches 98.1013%. Combining transformer digital twin models, real-time interaction mapping between physical entities and virtual space is achieved, enabling online diagnosis of transformer faults. Experimental results show that the method proposed in this paper has high diagnostic accuracy and strong stability, providing reference for the intelligent operation and maintenance of transformers.

This paper proposes a transformer fault diagnosis method based on ACGAN and CGWO-LSSVM to address the problem of misjudgment and low diagnostic accuracy caused by the small number and uneven distribution of some fault samples in transformer fault diagnosis. Firstly, generate adversarial networks through auxiliary classification conditions, The ACGAN method expands a small and imbalanced number of samples to obtain balanced and expanded data; Secondly, the non coding ratio method is used to construct the characteristics of dissolved gases in oil, and kernel principal component analysis is used, KPCA method for feature fusion; Finally, using the improved cubic gray wolf optimization algorithm, CGWO for least square support vector machines, optimize the parameters of the LSSVM model and construct a transformer fault diagnosis model. The results show that the proposed method has a low false alarm rate and a diagnostic accuracy of 97.66%, compared to IGOA-LSSVM the IChOA-LSSVM and PSO-LSSVM methods improved accuracy by 0.12, 1.76, and 2.58%, respectively. This method has been proven to solve the problems of misjudgment and low diagnostic accuracy caused by small sample sizes and uneven distribution. It is suitable for multi classification fault diagnosis of transformer imbalanced datasets and is superior to other methods.

Accurate fault diagnosis of transformers is crucial for preventing power system failures and ensuring the continued reliability of electrical grids. To address the challenge of low accuracy in transformer fault diagnosis using support vector machines (SVMs), an enhanced fault diagnosis model is proposed, which utilizes an improved dung beetle optimization algorithm (IDBO) to optimize an SVM. First, based on dissolved gas analysis (DGA), five characteristic quantities are selected as input features. Second, improvements to the DBO algorithm are made by incorporating Chebyshev chaotic mapping, a golden sine strategy, and dynamic weight coefficients for position updates. The performance of the IDBO is validated using four benchmark test functions, demonstrating faster convergence. Subsequently, the IDBO optimizes the SVM’s penalty factor C and kernel function parameter g, which are then input into the SVM for training, yielding an efficient fault diagnosis model. Finally, comparisons with other methods confirm the usefulness of the proposed model. Experimental results demonstrate that the IDBO–SVM model attains accuracy improvements of 1.69%, 8.47%, and 10.17% over dung beetle optimization–SVM (DBO–SVM), sparrow search algorithm–SVM (SSA–SVM), and grey wolf optimization–SVM (GWO–SVM) models, respectively. In addition to higher accuracy, the IDBO–SVM model also delivers a faster runtime, further highlighting its superior performance in transformer fault diagnosis. The proposed model has practical significance for enhancing the stability of transformer operation.

Accurate fault diagnosis of power transformers is critical for maintaining grid reliability, yet conventional dissolved gas analysis (DGA) methods face challenges in feature representation and high-dimensional data processing. This paper presents an intelligent diagnostic framework that synergistically integrates systematic feature engineering, tensor decomposition-based feature selection, and a sparrow search algorithm (SSA)-optimized multi-kernel support vector machine (MKSVM) for transformer fault classification. The proposed approach first expands the original five-dimensional gas concentration measurements to a twelve-dimensional feature space by incorporating domain-driven IEC 60599 ratio indicators and statistical aggregation descriptors, effectively capturing nonlinear interactions among gas components. Subsequently, a novel Tucker decomposition framework is developed to construct a three-way tensor encoding sample-feature-class relationships, where feature importance is quantified through both discriminative power and structural significance in low-rank representations, successfully reducing dimensionality from twelve to seven critical features while retaining 95% of discriminative information. The multi-kernel SVM architecture combines radial basis function, polynomial, and sigmoid kernels with optimized weights and hyperparameters configured through SSA's hierarchical producer-scrounger search mechanism. Experimental validation on DGA samples across seven fault categories demonstrates that the proposed method achieves 98.33% classification accuracy, significantly outperforming existing methods, including kernel PCA-based approaches, deep learning models, and ensemble techniques. The framework establishes a reliable and accurate solution for transformer condition monitoring in power systems.

Transformers insulated with mineral oil tend to form gases, which might be caused by system faults or extended use. Based on an evaluation of the main failure analysis techniques using combustible gases, this study reviewed the conventional techniques for Dissolved Gas Analysis (DGA), present in the norms IEC 60599 and IEEE Std C57.104, and their failure analysis tendency. Furthermore, to illustrate distinct technique performances and failures, the performance of the following techniques was analyzed based on the IEC TC10 database: Dornenburg, Duval Triangle, Duval Pentagon, IEC ratio method, Key Gas, and Rogers. The objective of this work was to present relevant information to support students and professionals who work in failure analysis and/or assist in the development of new tools in the DGA field.

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 (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

This paper proposes a novel approach for transformer fault diagnosis. Initially, a high-dimensional feature set comprising 19 features related to five gas concentrations is constructed to reflect the gas-fault relationship. Subsequently, the Shapley Additive Explanations (SHAP) method is employed to evaluate feature importance and select a subset that significantly influences model predictions, thereby simplifying the model and enhancing its interpretability. Following this, the bald eagle search (BES) intelligent optimization algorithm is utilized to optimize the hyperparameters of the light gradient boosting machine (LGBM) model, further improving its predictive capability. Comparative experiments with various traditional machine learning models validate the effectiveness of the proposed method. The SHAP-BES-LGBM model achieves the highest accuracy of 0.9509 and an f1 score of 0.9606 on the test set, with only 11 samples misclassified, demonstrating superior classification performance and underscoring the advantages of this integrated approach in transformer fault diagnosis.