Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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 is described, 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.
Abstract
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.
A sophisticated method for determining the overall health condition of power transformers is presented and it is verified that the omission of four diagnostic tests can reduce the economic burden by 46.99%, yielding a cost saving of 259,000 PKR per transformer unit.
Jawad Amjad, A. Siddique, Waseem Aslam· Energies· 0 citations
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
Failures and guaranteed dependability of the electrical grid, early fault diagnosis in power transformers is essential. By examining gas ratios suggestive of faults, dissolved gas analysis (DGA) continues to be a vital component for transformer health monitoring. Using four preprocessing techniques raw data, min-max normalization, logarithmic transformation, and square root transformation; this study suggests a machine learning method for fault detection using DGA gas ratios (such as CH₄/H₂, C₂H₂/C₂H₄). Random forest (RF), support vector machines (SVM), gradient boosted trees (GBT), and a hybrid RF-GBT model that uses prediction fusion were the four supervised classifiers assessed. Performance was assessed using accuracy, precision, recall, f1-score, and Cohen's kappa. Experimental results show that the hybrid RF-GBT model with logarithmic transformation achieves the highest performance, with 94.93% accuracy and 92.37% Cohen's kappa, significantly outperforming individual classifiers. Data preprocessing, particularly logarithmic and square root transformations, enhances diagnostic robustness by mitigating feature skewness. This study underscores the importance of tailored preprocessing and ensemble methods for reliable transformer fault diagnosis.
F. Z. Boudjella, Souhila Boudjella, Nasiru Yahaya Ahmed et al.· International Journal of Pow...· 0 citations
Existing machine learning models for predicting transformer oil breakdown voltage (BDV) often depend on chemical properties, which require laboratory tests, thus making them time-consuming and costly. Furthermore, these models are typically developed using standalone methods, which may fail to achieve satisfactory prediction accuracy. This study introduces an interpretable ensemble prediction model for transformer oil BDV, leveraging physical properties instead of chemical properties. The ensemble model integrates four standalone models: multilinear regression (MLR), multilayer perceptron (MLP) neural network, support vector machine (SVM), and long short-term memory (LSTM) neural network, to enhance prediction accuracy. A Local Interpretable Model-Agnostic Explanation framework was used to provide insights into the model's predictions. The model's performance was assessed using key metrices, including the correlation coefficient (R), determination coefficient (R²), root mean squared error (RMSE), and mean absolute error (MAE). Results revealed that the ensemble model outperformed individual models, achieving R = 0.9796, R² = 0.9596 during training and R = 0.9781, R² = 0.9567 during testing, along with RMSE = 1.7678, MAE = 1.2972 during training, and RMSE = 1.7347, MAE = 1.2985 during testing. The proposed model reliably predicted transformer oil BDV using physical properties, eliminating complex experiments while offering transparency to enhance applicability in the power industry
Musbahu Garba Indabawa, Nouruddeen Bashir Umar, Rabiu Aliyu Abdulkadir· Jurnal Nasional Teknik Elekt...· 0 citations
: Accurate prediction of the Remaining Useful Life (RUL) of power transformers is essential for maintaining the reliability and stability of modern electrical energy systems. This paper proposes a data-driven RUL prediction method for oil-immersed power transformers by integrating an Improved Dhole Optimization Algorithm (IDOA) with a Bidirectional Gated Recurrent Unit (BiGRU) network. The proposed framework leverages multi-source condition-monitoring data, including electrical characteristics, dissolved gases in transformer oil, insulation aging indicators, oil-quality parameters, and mechanical condition indicators. Logistic chaotic mapping is introduced to improve the diversity of the initial IDOA population, while stage-dependent parameter scheduling is used to guide the transition from global exploration to local refinement during BiGRU hyperparameter optimization. The optimized BiGRU model is then used to capture temporal dependencies in transformer degradation sequences and predict RUL values. Experimental results obtained from 110 kV and 220 kV oil-immersed transformers show that the proposed IDOA-BiGRU model achieves improved prediction accuracy compared with the baseline models. For the decommissioned transformer samples with actual RUL labels, the proposed model achieves a mean absolute error of 0.8897 years. These results indicate that the proposed method can provide a data-driven auxiliary reference for condition-based maintenance
Xiao Liang, Yang Wang, Tianhang Long et al.· Energy Engineering· 0 citations
Condition assessment of a transformer provides information about the overall health status of the insulation. Accurate health index (HI) prediction at regular intervals prevents catastrophic failures. In this study, a novel multi-hierarchically weighted neural network is proposed to estimate the HI of power transformers. It is designed with 12 distinct features of an oil- and paper-insulation system. Initially, collected attributes are normalized using multi-criterion analysis (MCA). The correlation and appropriate weight prioritization of each attribute are determined using the analytical hierarchy process (AHP). The combined MCA-AHP facilitates the calculation of weighted scores and converts the 12 attributes into 3 quality grades. These grades are used as inputs to the hybrid artificial neural network (ANN), and the output is the overall HI. The model is trained and tested using 350 data samples collected from the Himachal Pradesh State Electricity Board, India. The performance of the proposed hybrid model is validated by root mean square error, mean absolute error, mean relative error, and correlation coefficient. Furthermore, a comprehensive comparison is conducted using 300 data samples with pre-known health conditions (HCs), and other expert models in the literature achieved 97% of accuracy. The proposed hybrid model effectively addresses the general issues raised by intelligent models, such as reliance on expert rule-based approaches in Fuzzy models, greater computational demand in ANN and ANFIS models, and increased complexity due to diagnostic attributes. Based on the predicted HCs, preventive maintenance actions are proposed to ensure effective maintenance of the asset.
M. Gopi, C. Ranga, K. Jagtap· Engineering Research Express· 0 citations
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