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.
Abstract
Purpose: To ensure an accurate identification and discrimination between actual faults and power swing conditions, particularly those faults that are caused during the process of power oscillations.
Design / Methodology / Approach: The proposed approach combines advanced signal processing with intelligent machine learning. In this regard, an S-Transform is used to process the current signals to extract discriminative features that identify changes in the system caused by power swings and faults. Subsequently, these features are provided to an SVM classifier to ensure accurate identification and classification of faults caused during power swings. The proposed approach is validated on a three-machine, nine-bus test system using PSCAD software, under different symmetrical and unsymmetrical fault conditions and during power oscillations.
Research Limitation: The performance of the suggested method using real-time field data, diverse power system configurations, various levels of noise, and renewable power penetration is not considered.
Finding: The simulation results demonstrate that the suggested S-Transform-based SVM method can clearly distinguish between power swings and actual faults, including symmetrical three-phase faults that have characteristics similar to power swings.
Practical Implication: The suggested method can be successfully applied to modern numerical and intelligent-type power system protection relays to assist in decision-making during power swings.
Social Implication: By enhancing the reliability and selectivity of power system protection, the proposed method also helps improve the stability and sustainability of the power system. This reduces the chances of widespread power outages, which in turn contributes to economic development.
Originality / Value: This paper proposes a new hybrid approach to fault detection in power systems based on S-Transform feature extraction and SVM-based intelligent classification.
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.
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The detection of fault and its resolution are crucial in a power transmission line for ensuring unhampered and efficient power supply. These lines are often exposed to unpredictable environmental conditions and therefore encounter several challenges. Most failures in the power system are attributed to these, thereby necessitating the need for quick fault detection and resolution procedures. Research on hybridizing ANN and SVM is limited to fault detection and classification. Work on hybridizing ANN and SVM on IEE 39-bus for three simultaneous diagnostic tasks of fault type classification (LG, LL, LLG, LLL), faulted-line identification, and protection zoning defined as near-end versus far-end fault discrimination in a model is rare. This study bridges this gap by presenting a hybrid ANN-SVM model onfault type classification, fault line identification and protection zone identification in power transmission lines. 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. Evaluated on fault scenarios from the IEEE 39-bus New England test system simulated in MATLAB/Simulink R2025b, the hybrid model achieves fault type classification accuracy of 97.2%, protection zoning accuracy of 95.8%, and faulted-line identification accuracy of 97.7%, with ROC Area Under the Curve (AUC) values exceeding 0.90. These results consistently outperform standalone nd SVM baselines by 4% to 6% in fault type, 2.9% to 15.9% in protection zoning and 5% to 10% in fault line classification, validating the effectiveness of the proposed hybridisation strategy for intelligent transmission system protection.
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Power quality disturbances (PQDs) in microgrids pose significant challenges to stable and reliable operation, particularly in tourism-oriented island systems with highly variable and uncertain load patterns. This paper proposes an interpretable PQD identification framework based on time–frequency feature extraction. The method utilizes a short-time Fourier transform to capture the nonstationary characteristics of voltage signals and constructs a compact feature set integrating time-domain, frequency-domain, and time–frequency information for disturbance classification. A supervised learning model is employed to map the extracted features to disturbance categories, while interpretability is achieved through feature contribution analysis, enabling explicit linkage between model decisions and the physical characteristics of PQDs. The proposed approach is validated using a combination of synthetic datasets, simulation data derived from MATLAB/Simulink R2024a microgrid models, and experimentally measured signals from a hardware-based platform. Case study results demonstrate that the proposed framework achieves a high overall classification accuracy of 99.50% across multiple disturbance types, including voltage sag, voltage swell, harmonic distortion, voltage flicker, transient disturbances, and hybrid disturbances. The interpretability analysis further confirms that the identified features are physically consistent with the underlying disturbance mechanisms. Overall, the proposed framework provides an accurate, robust, and interpretable solution for PQD identification, offering practical value for real-time monitoring and intelligent operation of renewable-rich microgrids.
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