The proposed method’s robustness in challenging high-noise and missing-data scenarios reinforces its viability in industrial operations, where measurement infrastructure may be compromised by hardware malfunctions.
Abstract
This work presents an innovative approach for fault classification in power transformers, combining advanced wavelet transform with machine learning techniques. The proposed method stands out for its robustness against data missing conditions, which is a critical challenge in fault diagnosis for these systems. The approach employs machine learning algorithms for fault classification, even when data quality and availability are compromised due to transmission failures or possible interference in the connection circuit between the current transformers and the transformer protective relay. Its ability to adapt to data loss makes it highly suitable for real-world industrial applications, aligning with Industry 4.0 principles, particularly in environments where data integrity is essential for real-time analysis and decision-making. The approach’s effectiveness is validated through a comprehensive evaluation of a diverse dataset covering several critical faults. When compared with an existing threshold-based fault classificator, the proposed method demonstrated outperformance in fault classification both in scenarios with all data available and in scenarios with missing data, reaching expressive success rates of 100% and 92.9%, respectively, against 42.5% and 36.9% obtained by the conventional one for a signal-to-noise ratio of 40 dB. The proposed method’s robustness in challenging high-noise and missing-data scenarios reinforces its viability in industrial operations, where measurement infrastructure may be compromised by hardware malfunctions.
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.
This study proposes an artificial neural network-based approach for the detection and classification of faults occurring in high voltage alternating current (HVAC) power transmission lines. The study considers 12 classes comprising 11 fault types and one healthy state. Unlike traditional approaches that rely on extensive feature-extraction procedures, this study directly employs measured three-phase voltage, current, and phase-angle quantities as ANN inputs, thereby avoiding computationally intensive signal decomposition and handcrafted feature extraction stages. The model was evaluated using regression-oriented metrics, including mean squared error (MSE) and correlation coefficient (R). Furthermore, 5-fold cross-validation showed that the proposed ANN achieved better regression performance than GPR, SVR, and Kernel Regression models. Additional robustness analyses performed under different loading conditions and fault resistance values further demonstrated the generalization capability of the proposed ANN models under varying operating conditions. To evaluate the practical contribution of phase-angle information, a classification-based ablation study compared a 6-input ANN using only three-phase voltage and current measurements with a 12-input ANN including phase-angle measurements. Under identical test conditions, the 6-input and 12-input classifiers achieved accuracies of 88.51% and 84.73%, respectively, with macro F1-scores of 0.8789 and 0.8374. Repeated-training analysis further showed that the six-input configuration achieved higher mean performance and lower variability. The results indicate that phase-angle information provides supplementary and class dependent discriminative value, but does not consistently improve all fault classes. Conventional voltage and current measurements alone therefore represent a simpler and more stable alternative, whereas phase-angle measurements may be incorporated when synchronized phasor information is already available.
Zeynep Bala Duranay, İsmail Anıl Avcı, Mohammed Bushra Mohammed et al.· Symmetry· 0 citations
A new hybrid approach to fault detection in power systems based on S-Transform feature extraction and SVM-based intelligent classification is proposed that can clearly distinguish between power swings and actual faults, including symmetrical three-phase faults that have characteristics similar to power swings.
P. Sharma, M. Silas, P. Roy et al.· African Journal Of Applied R...· 0 citations
Experimental results on stator inter-turn faults across multiple low-load operating conditions demonstrate superior reconstruction performance compared with representative recurrent, transformer-based, and graph-based autoencoder models while maintaining computational efficiency suitable for low-latency deployment.
Chibuzo Nwabufo Okwuosa, J. Hur· IEEE Access· 0 citations
A hybrid two-stage machine learning pipeline that decouples detection from classification is proposed, and the direction of the zero-sequence signature is found to be system-dependent, motivating a learned decision boundary in place of a fixed relay threshold.
Sahil Manikshete, A. Gujarathi, Thanh Long Vu et al.· 0 citations