arXiv subjects

Fathima Razeeya Mohamed Razick

Publications and source records attributed to Fathima Razeeya Mohamed Razick.

2 recordsLinked to original sources

arXiv2026

DER-Fault: A Simulation-Based Fault Dataset for DER-Integrated Distribution Systems Under Diverse Operating Conditions

The growth of distributed energy resource (DER) integration introduces greater variability in distribution system operating conditions, creating a need for comprehensive datasets for developing and evaluating data-driven fault diagnostic methods. This paper presents \textbf{DER-Fault}, a simulation-based dataset generated in OpenDSS using the Iowa 240-bus distribution system with photovoltaic (PV) generation, electric vehicle (EV) charging, and vehicle-to-grid (V2G) operation. To capture annual load variability while maintaining a manageable number of simulation cases, 60 representative operating conditions are selected from 8,760 hourly load profiles, covering peak, off-peak, transition, typical weekday, and typical weekend conditions. Six DER scenarios with varying PV and EV penetration levels and V2G participation are considered. The dataset includes normal operation and ten short-circuit fault classes, with varying fault resistance. Pre- and post-fault voltage and line-current phasor measurements are recorded at selected measurement locations, resulting in 19,800 simulation cases. Technical validation demonstrates broad coverage of annual loading and DER operating conditions, distinct phase-specific responses corresponding to the simulated fault types, and a consistent reduction in voltage-phasor disturbance with increasing fault resistance. The resulting dataset provides a structured resource for developing and evaluating machine-learning and other data-driven methods for fault detection, classification, and localization in DER-integrated distribution systems.

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arXiv2026

GCN-Based Model for Fault Classification in Distribution Networks under Harmonic Distortion

The increasing integration of distributed energy resources introduces new challenges for accurate fault detection and classification in distribution networks. This paper presents a topology-aware graph convolutional network-based model for fault classification under inverter-induced harmonic distortion. The model uses pre-fault and post-fault voltage and current phasors, including harmonic components, mapped to the IEEE 34-bus system topology. The simulation data generated in OpenDSS encompass varying levels of penetration of solar photovoltaics (30%, 50%, 70%) and current total harmonic distortion (0%, 1%, 3%, 5%) across multiple fault types: single line-to-ground, line-to-line, double line-to-ground, and three-phase-to-ground. A differential evolution algorithm is employed to optimize the hyperparameters of the network, resulting in a three-layer model that achieves an F1-score of 0.98. Stress testing across scenarios confirms high robustness and minimal class confusion. The results demonstrate that a graph convolutional network-based model can effectively classify distribution faults under varying photovoltaic penetration levels and harmonically distorted conditions. Comparative analyses with convolutional neural network, long short-term memory, and multi-layer perceptron architectures demonstrate that the proposed model delivers superior classification performance, particularly under the most stressed condition with 70% photovoltaic penetration and 5% harmonic distortion.

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