Adaptive Differential Evolution-Based Fault Detection and Location Model for the Ayepe 34-Bus Nigerian Distribution Network

Abstract

The increasing operational complexity and fault vulnerability of Nigeria’s electrical distribution networks demand intelligent systems capable of rapid fault detection, accurate localization, and efficient isolation. This study develops an intelligent fault detection and location framework for the Ayepe 34-bus Nigerian distribution network using the Adaptive Differential Evolution (ADE) algorithm. A mathematical model for fault location and distance estimation was formulated based on voltage and current measurements derived from the network’s impedance characteristics. The Forward and Backward Sweep (FBS) technique was employed to determine pre- and post-fault voltage and current profiles of the distribution buses under steady-state and faulted conditions. The ADE algorithm was implemented to minimize the fault location distance error and optimize fault clearing time, enabling improved coordination of network protection devices. Simulation was conducted in MATLAB R2023a, and the ADE performance was compared with that of Genetic Algorithm (GA) and Political Optimization (PO) approaches. Results show that ADE achieved faster convergence, lower fault location error, and shorter clearing times than GA and PO. Specifically, the ADE-based model accurately identified fault locations at buses 6, 15, 20, and 30, with an average fault clearing time of 80–92 ms and enhanced post-fault voltage recovery of approximately 0.77 p.u. The proposed ADE framework demonstrated superior precision, adaptability, and reliability, contributing to more efficient fault management and improved service continuity. This research establishes ADE as a powerful optimization-based tool for intelligent fault detection and location in Nigeria’s medium-voltage distribution networks, enhancing overall grid stability and operational efficiency.