Jun 2026· Jurnal Teknik Elektro· Vol 18, pp. 19-33· 0 citations
TL;DR
A machine learning-based fault detection model that is accurate, robust, and possesses high generalisation capabilities is developed using an Artificial Neural Network model as a binary classifier (normal and fault).
Abstract
The reliability of three-phase power systems depends heavily on the ability to detect faults quickly and accurately. However, much previous research has faced challenges such as data leakage, biased evaluations due to random data splitting, and limitations in the utilisation of system dynamics-based features. This study aims to develop a machine learning-based fault detection model that is accurate, robust, and possesses high generalisation capabilities. The method employed is a quantitative approach using an Artificial Neural Network (ANN) model as a binary classifier (normal and fault). The research process includes data preprocessing, elimination of features that could potentially cause data leakage, feature engineering based on current, voltage, energy, and system imbalance, as well as scenario-based data splitting to ensure the validity of the evaluation. The model was trained using repeated cross-validation and optimised based on the Area Under the Curve (AUC) metric. The results show that the ANN model achieved high performance with an AUC of 0.987 and an accuracy of approximately 98.5% and remained stable under varying conditions such as noise and load changes. Compared to other models, Random Forest delivered the best performance, followed by Support Vector Machine (SVM) and ANN. This study addresses the gap regarding more realistic evaluation methods that are free from data leakage. Its novelty lies in the use of scenario-based splitting, comprehensive evaluation, and the finding that raw signal features make a dominant contribution to fault detection.
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
An ensemble learning-based technique has been proposed for fault detection in power transmission lines by using Deep Q-Networks (DQN) in conjunction with standard classifiers such as Naive Bayes, Multilayer Perceptron (MLP), Logistic Regression, and Deep Forest.
Sandeep Godhade, Jayendra Kumar· International Journal of Ele...· 0 citations
Power grid engineering is necessary for the distribution of power, but problem detection and categorization are made extremely difficult by their growing complexity, particularly with the incorporation of renewable energy sources. Decision trees and support vector machines are two examples of fault detection techniques that frequently fail to handle the dynamic and non-stationary character of power grid data. These techniques’ efficacy in real-time defect identification is limited because they are unable to capture the complex relationships and temporal dependencies present in time-series data. Furthermore, a lot of conventional models are unable to generalize to different kinds of problems, which results in errors and delays in fault identification. For improved fault detection and classification in power grids, this research suggests a hybrid approach that combines Temporal Fusion Transformers (TFT) with Long Short-Term Memory (LSTM) Neural networks. While the LSTM neural network is used to represent sequential data, the TFT model is particularly good at capturing complicated linkages in time-series data. By combining these two models, the suggested approach can improve grid efficiency and dependability by offering precise fault forecasts in real-time. The findings show that the suggested hybrid model works better than conventional techniques, with a fault classification accuracy of 98.66% as opposed to decision trees’ 97.42% and SE-CDAE’s 97.98% accuracy. The performance of the model demonstrates its potential for real-time implementation in contemporary power grids, guaranteeing improved fault identification and prompt reactions to avert system breakdowns.
Qinghua Chen, Tao Xu, Cheng Zhou et al.· Distributed Generation &...· 0 citations
Abstract. The explosive growth of grid-connected renewable energy systems (RES) has increased the complexity of the operation of the modern power infrastructure, making the detection of the faults reliably an inevitable condition of the stable functioning and safety. Traditional single-algorithm and rule-based monitoring systems are not sufficiently flexible or discriminatory to distinguish between the many varieties of faults that occur in photovoltaic (PV) arrays, wind turbines, battery management systems, and grid-tie inverters. The paper suggests a new stacking-ensemble hybrid machine learning model that will combine a one-dimensional convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) module, a Random Forest classifier, and an XGBoost gradient booster as base learners under the guidance of a logistic regression meta-learner. Publicly available SCADA and lab bench data were used to create a curated multi-source dataset of 8,400 labelled samples to represent five operational states. The proposed framework achieved an accuracy of 97.8, a macro-averaged F1-score of 97.1, and a Matthews Correlation Coefficient (MCC) of 0.972, outperforming all the compared baseline methods at least by 3.3 percentage points. The findings verify the effectiveness of the hybrid stacking paradigm in identifying faults in real-time and multiple classes in heterogeneous renewable energy settings.
A. Gopalakrushna· Materials Research Proceedin...· 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
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