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.
G. Ajenikoko· INTERNATIONAL JOURNAL OF APP...· 0 citations
The reliability of radial distribution systems is critically affected by the frequency and severity of
electrical faults, which often result in voltage instability, supply interruptions, and equipment
degradation. Effective fault diagnosis and protection coordination therefore remain essential
components of modern distribution network operation. This study presents an enhanced Adaptive
Differential Evolution (ADE)-based framework for fault diagnosis and optimal protection
coordination using the IEEE 33-bus radial distribution system as a case study. A mathematical
model that integrates post-disturbance voltage and current signatures with network impedance
characteristics was formulated to estimate fault distance and classify fault severity. The Forward
and Backward Sweep (FBS) method was applied to compute pre- and post-fault system states
under multiple symmetrical and asymmetrical fault scenarios. The ADE algorithm was then
employed to optimize protection indices, minimizing both fault location error and cumulative
clearing time while ensuring selective relay coordination. Simulation results demonstrate that
ADE significantly improves fault diagnosis accuracy compared to Genetic Algorithm (GA) and
Political Optimization (PO). For the IEEE 33-bus system, ADE accurately identified critical
faulted buses (10, 27, 26, 28, and 6) while achieving the lowest location error and the shortest
fault clearing interval of 85–92 ms. Post-fault stability analysis further revealed that ADE
maintained higher voltage recovery (0.8071 p.u) and lower short-circuit current magnitudes
(14.1651 p.u) relative to GA and PO. These improvements directly enhance relay selectivity,
reduce miscoordination risk, and minimize stress on feeder equipment. The findings confirm that
the ADE-based approach offers a robust, fast, and intelligent protection strategy for radial
distribution networks, and establishes its potential for integration into future smart-grid
automation and self-healing protection schemes.
G. Ajenikoko· INTERNATIONAL JOURNAL OF APP...· 0 citations
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