The results indicate that phase-angle information provides supplementary and class dependent discriminative value, but does not consistently improve all fault classes, whereas conventional voltage and current measurements alone represent a simpler and more stable alternative, whereas phase-angle measurements may be incorporated when synchronized phasor information is already available.
Abstract
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.
The results confirm that the proposed framework provides a comprehensive and efficient solution for real-time fault analysis by combining classification, localization, temporal analysis, and stability-aware decision support within a single model.
Nazmun Nahar Karima, M. Hazari, Shameem Ahmad et al.· Energies· 0 citations
The proposed framework employs ANN as a nonlinear feature embedding and a Radial Basis Function SVM subsequently classifies using an Error-Correcting Output Codes (ECOC) strategy, validating the effectiveness of the proposed hybridisation strategy for intelligent transmission system protection.
Kudu Abubakar Mohammed, M. Balogun, Adesina M. Lambe et al.· Communication in Physical Sc...· 0 citations
With the large-scale integration of inverter-based resources (IBRs), the types and operating characteristics of power sources on both sides of transmission lines have changed significantly, rendering traditional fault-type selection methods inadequate. To address this, this paper proposes a neural network-based multi-parameter fault type identification strategy. The method constructs a 16-dimensional feature vector from local three-phase voltage/current magnitudes, phase angles, and zero-sequence components, and designs a lightweight fully-connected neural network with two hidden layers to learn the complex nonlinear mapping between these comprehensive inputs and fault types. Extensive training and testing data are generated using the PSCAD/EMTDC simulation platform, covering multiple scenarios including double-ended synchronous generator (SG), single-ended IBR, and double-ended IBR. The results show that the proposed strategy achieves identification accuracy exceeding 95% across all scenarios, significantly outperforming traditional current-based methods, especially in IBR-dominated cases. Moreover, the method exhibits strong robustness against variations in fault location, transition resistance, and source type, providing a reliable and adaptive protection solution for evolving hybrid power grids.
Luyun Zhang, Rui Xiong, Rui Hou et al.· International Conference on...· 0 citations
This paper presents a machine-learning based strategy for accurate identification of commutation failures instances. A commutation-failure identification (CFI) block has been proposed and implemented, which utilizes the DC current and the valve-side AC currents for accurate detection. The well-established CIGRE benchmark model, implemented in PSCAD/EMTDC, is employed as the test system. Extensive simulations are performed under diverse fault scenarios, and a comprehensive dataset is generated under both commutation failure and normal operating conditions. Six different machine-learning classifier models are trained using a sufficient portion of the generated dataset and are subsequently evaluated against the remaining test dataset. The performance of the classification is assessed though confusion matrices and several statistical metrics including Accuracy, Precision, Recall and F1-Score for the base case study. Several other studies including the reduced feature cases, noisy validation scenario, five-fold cross validation, and hyperparameter sensitivity analysis have been performed to validate the effectiveness and reliability of the proposed ML-assisted approach. The results demonstrate that the proposed framework can reliably distinguish commutation failure events from normal operating conditions with high accuracy. Among all these classifier models, the Random Forest classifier has been identified as the most suitable model to classify the commutation failure instances with an Accuracy of 99.53% and Recall score of 99.63% for the base case. This accurate classification of commutation failure will ensure enhanced reliability of an HVDC system.
Bilal Anwar, M. Saqib, Rizwan Khan 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
This paper presents a simulation-based analysis of fault detection in alternating current microgrids through data-driven methods. A detailed simulation-based microgrid model is created for conducting the fault analysis, including single phase ground fault, line fault, two phase fault and three phase fault situations. Simulations were carried out under different loading conditions and fault impedance values to obtain an extensive set of data containing voltage, current, and signal characteristics. This data was used to test the performance of various machine learning techniques for fault classification. The results show that the ensemble methods perform better than standalone models, with Random Forest (RF) demonstrating the highest classification performance. This findings of this research underscore the potential of data-driven techniques in improving the robustness and flexibility of AC protection systems in contemporary microgrids. Future work will focus on extending the analysis to more complex fault conditions and validating the proposed approach through real-time implementation.
Abdul Basit Taj, Sarosh Ali Shah Syed, Adnan Umar Khan et al.· International Journal of Ene...· 0 citations
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