Quarterly Journal of Information and Communication Technology ​

Artificial Intelligence Approaches for Modeling and Analysis Fault in Power ‎Distribution Networks

Document Type : Original Research Article

Author

KAR Higher Educational Institute

10.22034/apj.2026.2072799.1057
Abstract
Background and Objectives: Fault of power distribution networks is based on the ‎uncontorable factors. Rapid approache to identidtification and localization of power ‎network fault is ctritical to maintain system riable. While traditional approaches use ‎measurements from current and voltage transformers, this study proposes an ‎artificial intelligence-driven approach for enhanced fault detection and analysis in ‎power distribution networks. A custom-designed sensing prototype captured voltage ‎and current data under simulated fault conditions, including short-circuit and open-‎circuit faults. The presented approach includes fundamental variables, such as fault ‎type, sensor placement topology, and line distance, were rigorously controlled during ‎data acquisition.‎

Methods: This paper presentes two algorithms: an Artificial Neural Network (ANN) ‎and an Adaptive Neuro-Fuzzy Inference System (ANFIS). Both used algorithms have ‎many parmeters that rquire to be tune While the values of these parameters effect ‎on the performance, but the major target of this study is using of these algorithms. ‎Steps of algorithms are described in the paper. The redults of algorithms are showed ‎with the used data. Performance was validated under variable load conditions across ‎line distances of 200–800 meters. ‎

Finding: Simulation results demonstrate that the ANFIS classifier achieved superior ‎accuracy in fault classification (99.7%) and minimal distance estimation error (0.5%). ‎Both ANN and ANFIS delivered high precision in fault detection, localisation, and ‎classification, with ANFIS exhibiting significantly faster training convergence (1 ms). ‎Indeed ANFIS has more consistency. ‎

Cnclusion: The framework presents a robust, computationally efficient solution for ‎real-time fault management (modeling), recommending (analysis) the integration of ‎dedicated sensors by power distribution network utilities to enable targeted grid ‎interventions.‎

Keywords


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