Abstract

IJLTEMAS

International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS)

IJLTEMAS

2278-2540

IJLTEMAS

181

10.51583/IJLTEMAS.2026.150700180

Education

Comparative Operational Performance Assessment of 33 kV Distribution Feeders Using Load Profile Characteristics and Utilization Indices

O.O.

Adetunmbi

Department of Statistics, Federal Polytechnic, Ile-Oluji, Ondo State, Nigeria Nigeria A.O.

Adetunmbi

Department of Electrical/Electronic Engineering, Federal Polytechnic, Ile-Oluji, Ondo State, Nigeria Nigeria O.O.

Akinsooto

Department of Electrical/Electronic Engineering, Federal Polytechnic, Ile-Oluji, Ondo State, Nigeria Nigeria

15 7 2452 2462 01 08 2026 06 08 2026

27 08 2026

33 kV distribution feeder; load factor; peak-to-off-peak demand ratio; feeder utilization index; load profile; distribution network.

Abstract

The operational performance of electricity distribution feeders is influenced by variations in load demand and the extent to which available network capacity is utilized. This study comparatively assessed the Ondo, Okitipupa and Ile-Oluji 33 kV distribution feeders using monthly peak and off-peak load-demand data for 2023–2025. The analysis employed an estimated two-period load factor, peak-to-off-peak demand ratio (PDR), coefficient of variation (CV), feeder utilization index (FUI), descriptive statistics and Pearson correlation. Ondo recorded the highest three-year mean peak demand (10.90 MW), whereas Ile-Oluji had the highest overall estimated load factor (0.76). Ondo also had the highest average PDR (2.72) and the greatest three-year peak-demand variability (CV = 22.28%). Relative to the maximum observed peak during the study period, the three-year mean FUI values were 74.17% for Ondo, 75.33% for Okitipupa and 76.84% for Ile-Oluji. Peak demand declined markedly on the Ondo feeder from a 2023 mean of 12.87 MW to 7.81 MW in 2025. The results demonstrate that feeder performance depends not only on peak-demand magnitude but also on the temporal distribution and stability of demand. Regular load monitoring, demand-side management, load balancing and timely network reinforcement are recommended.

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