Jul 2026· international journal of engineering trends and technology· 0 citations
Abstract
Transformers play an indispensable role in any power system. The health condition of these devices should be the top priority. Early fault detection of these devices is essential to have sustainable power flow. There are many routinary transformer tests like winding test, furan analysis, insulation resistance tests, but dissolved gas analysis stands to be one of the most critical tests among others. This is to the fact that the DGA test can evaluate the major condition of the transformer. Dissolved Gas Analysis (DGA) methods, while widely used, often struggle with accuracy and scalability under complex fault scenarios. This paper proposed a novel ML-based DGA framework that integrates the IEEE standard with Principal Component Analysis (PCA) and Gradient Boosting Machine (GBM) to enhance transformer fault diagnosis. PCA captures 95% of the variance with five principal components. The framework showed a test accuracy of 87.5% and a cross-validation accuracy of 86.05%, outperforming traditional methods such as the Duval Triangle (83.08%) and IEC Ratio Method (82.05%), as well as other machine learning models, including Random Forest (77%) and Support Vector Machines (37%). These findings demonstrate the effectiveness of the framework as a soft sensor in Transformer diagnostics.
This article explores machine learning techniques (MLTs) as a modern alternative to enhance the interpretation of DGA data for early-stage fault detection in service transformers, and demonstrates that random forest and gradient boosting outperform others, achieving up to 98% accuracy.
Rupali Balabantaraya, A. Chatterjee, A. Sahoo et al.· Electrica· 0 citations
The diagnosis of faults in power transformers (PT) plays a crucial role in ensuring the reliability and stability of power systems. However, traditional fault detection techniques are prone to low detection accuracy and are not stable enough under complex operating conditions. To address these challenges, this paper proposes a novel hybrid Variational Mode Decomposition (VMD), Graph Attention Network (GAN) with Adaptive Extreme Learning Machine (AELM) method for a transformer fault diagnosis framework. VMD can efficiently extract discriminative frequency bands of the transformer signals, GAN can dynamically learn the importance and relationship of the extracted feature, and AELM can classify rapidly and accurately with low complexity. The dataset was obtained from simulations of various fault conditions in the Matlab/Simulink. The experimental results demonstrate that the proposed method has an accuracy of 99.5%, a precision of 99.66%, a recall of 99.33%, and an F1-score of 99.5% compared to existing methods. The proposed hybrid framework enables efficient classification capability, feature learning with attention, and adaptive feature extraction, which contributes to improved performance.
Power transformers are important components of electrical power systems and it is essential that they operate reliably if continuous power supply is to be maintained. Because they are constantly subjected to electrical, thermal, mechanical, and environmental stresses, the insulation suffers degradation, a fact that can be seen in the physicochemical properties and in the characteristics of the dissolved gases in the transformer oil. Traditional methods of assessing condition depend on periodic testing of the oil and on the judgement of experts, a procedure which is time-consuming and open to personal bias. The present paper describes a machine learning-based approach for predicting the Transformer Health Index (HI) and the Remaining Life (RL) by means of the diagnostic parameters of transformer oil. The model used in this study is based on a dataset consisting of 470 samples of transformer oil and including 14 diagnostic features, namely the concentrations of dissolved gases (H₂, O₂, N₂, CH₄, CO, CO₂, C₂H₄, C₂H₆ and C₂H₂), Dibenzyl Disulfide (DBDS), power factor, interfacial voltage, dielectric rigidity and water content. Following data preprocessing and feature engineering, Random Forest Regression is applied in order to identify the complex nonlinear relationships between the oil parameters and the condition of the transformer. The model's performance is assessed using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the coefficient of determination (R²). The results of the experiments show that the proposed framework is able to make accurate predictions of both the Health Index and the Remaining Life, thus allowing a reliable evaluation of the transformer's condition. The method developed supports predictive maintenance by allowing for timely planning of maintenance, reducing the occurrence of unexpected transformer failures, extending the service life of the assets and improving the overall efficiency of asset management for transformers in modern power systems.
S. V. Satyanarayana, P. S. Kumar· International journal of com...· 0 citations
An enhanced diagnostic approach integrating the Sparrow Search Algorithm with PNN is developed, which confirms that SSA can substantially strengthen the anti-disturbance capability of PNN, thereby providing reliable technical support for power transformer fault diagnosis and offering certain reference value for enhancing the operational reliability of power grid equipment.
Bo Liu, Jianghong Dong, Shuyu Ren et al.· Journal of Physics, Conferen...· 0 citations
While Paper Insulated Lead Covered (PILC) cables are a legacy technology, they remain a critical fixture in medium-voltage power grids. However, as these cables are continuously used, they present growing risks to grid stability and public safety due to electrical, thermal, and environmental factors. As replacing entire networks is cost-prohibitive, utility providers need a smarter way to predict when and where a cable might fail. This paper presents an explainable, data-driven framework for multi-class health assessment of 20 kV PILC cables, aligning with the industry 4.0 standards of autonomous asset management. Using a real-world dataset of 999 inspection records from European utilities, four supervised learning models; Random Forest, AdaBoost, XGBoost, and a Multilayer Perceptron deep neural network were trained. The models categorize cable health into five standard IEC/IEEE health bands, achieving high classification accuracies between 97.0% and 99.5%. To ensure transparency in the decision-making process, SHapley Additive exPlanations (SHAP) were employed, identifying Partial Discharge (PD) and Thermal Difference (TD) Stability as the primary predictors of insulation degradation. This framework provides an interpretable tool for power utilities to transition from reactive to predictive maintenance scheduling.
A. Oyedeji, Peter O. Olukanmi· E3S Web of Conferences· 0 citations
Power transformers are an integral part of electrical power system infrastructure and play a vital role in the efficient and reliable transmission of bulk electrical power to distribution networks. The careful use of these assets enables the optimization of transmission voltages in power networks, a crucial step for reducing electrical energy losses, enhancing grid reliability, and ensuring uninterrupted electricity supply to end-users in interconnected power networks. Operational reliability of power transformers is a critical aspect in ensuring a continuous power supply. The health index (HI) is a crucial diagnostic tool to determine their real condition. Historically, HI assessments were based on scoring and weighting. Lately, however, there has been a significant change in the attitude towards the use of artificial intelligence (AI) and machine learning (ML) to predict the health of high-voltage power transformers. Although developments are taking place, the existing studies on ML-based HI prediction models for power transformers largely rely on an incomplete dataset containing improper parameters. Moreover, dependency on traditional ML models is a significant limitation when it comes to achieving a higher degree of predictive accuracy. This article presents a sophisticated method for determining the overall health condition of power transformers. A total of twenty of the most appropriate and highly relevant input parameters were selected to effectively evaluate the transformer condition. The dataset for these parameters was collected from real-time testing in accordance with international industry standards (i.e., IEC, IEEE, and ASTM), conducted at 220 kV and 500 kV grid stations in the Multan and Lahore regions, operated by the National Grid Company (NGC) in Pakistan. This comprehensive dataset was fed to five state-of-the-art ML models. The Categorical Boosting Regression (CatBoost Regressor) model demonstrated superior performance, achieving the highest accuracy (R2 Score) of 97.2% and the lowest mean absolute error (MAE) of 1.73. The best-performing model was then employed to predict the health index of the power transformers at the 500 kV grid station, Rahim Yar Khan, and the 500 kV grid station, Multan, as a practical case study. To demonstrate the economic importance of the proposed framework, an economic analysis was conducted via an iterative, parameter-skipping imputation strategy for maintenance cost optimization of the electrical power grid. The results verify that the omission of four diagnostic tests (i.e., Dissipation Factor, Capacitance, Insulation Resistance, and Transformer Turn Ratio) can reduce the economic burden by 46.99%, yielding a cost saving of 259,000 PKR per transformer unit. The implementation of this data-driven framework in the national grid can significantly reduce maintenance costs and facilitate an operational shift from traditional preventive maintenance to advanced predictive maintenance.
Jawad Amjad, A. Siddique, Waseem Aslam· Energies· 0 citations