Comparative Analysis of Particle Swarm Optimization and Salp Swarm Algorithm in improving the Voltage Profile of IEEE 9 Bus System

Voltage stability is a critical aspect of modern power system operation, directly influencing systemreliability, operational efficiency, and protection against grid collapse. Voltage instability oftenarises due to heavy reactive power demand, insufficient local reactive compensation, and weaktransmission infrastructure, especially under high loading or fault conditions. To address thesechallenges, advanced optimization techniques are often employed for reactive power planning andvoltage profile enhancement. Hence, a comparative analysis of two nature-inspired metaheuristicalgorithms—Particle Swarm Optimization (PSO) and Salp Swarm Algorithm (SSA)—in mitigatingvoltage instability of the IEEE 9-Bus test system is done. The aim is to compute the optimalplacement and sizing of reactive power compensation using shunt capacitors. The objective functionis formulated to minimize overall voltage deviation from the nominal value (1 p.u.), and ensurecomputational efficiency of the algorithms. Both algorithms were evaluated on three mainperformance metrics: voltage loss minimization, convergence characteristics, and recommendedcapacitor sizes (MVAr ratings). PSO algorithm showed faster convergence towards near-optimalsolutions, making it suitable for time-constrained applications. On the other hand, SSA showedsuperior performance in recommending more economical MVAr injection sizes. But SSA achievingcomparable voltage profile improvements, resulted with slightly higher iteration counts. Originalcontribution of this work lies in the direct comparison of PSO and SSA for increasing voltagestability under the same system conditions, offering insights into their relative strengths. Thesignificance of this study is its potential to guide power system engineers in selecting appropriateoptimization methods based on computational and economic trade-offs. Future research can extendthis approach to meshed and large-scale systems, integrating uncertainty in load demand andrenewable generation.