•  
  •  
 

Corresponding Author

ayanlade.oladayo@oouagoiwoye.edu.ng (S.O. Ayanlade)

Subject Area

Electrical Engineering

Article Type

Original Study

Abstract

This paper presents the Enhanced Dingo Optimization Algorithm (EDOA) to reconfigure the radial distribution network with the long-standing problem of active power loss minimization while maintaining a stable and acceptable voltage profile. EDOA enhances the standard Dingo Optimization Algorithm (DOA) through the integration of adaptive search control, elite-guided local refinement, and crossover-based diversity preservation mechanisms tailored for distribution network reconfiguration problems. The proposed method has been tested on two test systems: the IEEE 33-bus standard as well as the real-world Ayepe 34-bus distribution feeder in Ibadan, Nigeria. In the IEEE 33-bus test system, EDOA reduces active power loss by 56.00%; it beats the DOA (36.76%) by 19.24 percentage points, as well as other methods such as Firefly Algorithm (32.57%), Whale Optimization (31.15%), and Grey Wolf Optimization (31.16%). EDOA is 40% faster than DOA, and it maintains all bus voltages above the safety limit of 0.95 p.u. for the IEEE 33-bus system. On the Ayepe 34-bus practical network—characterized by uneven load distribution and higher resistance-to-reactance ratios—EDOA reduces active power loss from 1003.67 kW to 804.51 kW (19.84% reduction), outperforming DOA (18.27% reduction) with improved voltage profiles at critical weak buses. The simulation results demonstrate that EDOA is effective under both benchmark and practical feeder conditions. Its fast convergence and adaptive search methods can be used as a benchmark for metaheuristic optimization on the grid.

Keywords

EDOA, active power loss reduction, voltage stability enhancement, metaheuristic optimization.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Download

Share

COinS