Abstract

Transmission power losses significantly affect the efficiency, reliability, and economic operation of modern electrical power systems. This study proposes a Hybrid Genetic Algorithm–Particle Swarm Optimization (HGAPSO) framework for transmission loss minimization in the IEEE 118-bus power system. The proposed approach combines the global exploration capability of Genetic Algorithms (GAs) with the rapid convergence characteristics of Particle Swarm Optimization (PSO) to optimize generator voltage settings, transformer tap positions, and reactive power compensation while satisfying all operational constraints. The HGAPSO framework was developed and implemented in MATLAB R2024a and evaluated using the IEEE 118-bus test system. The simulation results demonstrate that the proposed method reduced transmission losses from 132.8 MW under the base-case condition to 98.6 MW, representing a 25.75% reduction in total network losses. In addition, the optimized operating conditions improved the minimum bus voltage from 0.914 p.u. to 0.972 p.u., while the average voltage deviation decreased from 0.062 p.u. to 0.019 p.u. These voltage profile improvements were achieved as secondary benefits of the transmission loss minimization process and the enforcement of system operating constraints. Furthermore, the HGAPSO algorithm exhibited superior convergence performance, reaching the optimal solution within 82 iterations compared to 185 iterations for GA and 124 iterations for PSO. The results confirm that the proposed HGAPSO framework provides effective transmission loss reduction, faster convergence, and reliable network operation compared with standalone optimization techniques. The proposed methodology offers a robust and computationally efficient solution for large-scale power system optimization, optimal power flow studies, and smart grid applications.

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