Control Strategy of OLTC using Quantum Binary Particle Swarm Optimization to Improve the Voltage Stability Index

Efficient voltage regulation in distribution and transmission systems heavily relies on transformers with On-Load Tap Changers (OLTC). This study introduces a novel optimization technique, called Quantum Binary Particle Swarm Optimization (QBPSO), to optimize transformer tap settings to improve voltage stability and reducing power losses. QBPSO combines the principles of quantum computing with binary particle swarm optimization, enhancing the algorithm's exploration and exploitation capabilities. Utilizing the Bus Injection to Branch Current-Branch Current to Bus Voltage (BIBC-BCBV) method for power flow analysis, this research evaluates the performance of the proposed method on the IEEE 34-bus 20 kV radial distribution system. The results indicate a significant reduction in the Voltage Stability Index (VSI) from 0.2257 to 0.2069, a decrease in power losses from 21.756 kW to 19.1573 kW, and an improvement in the average voltage from 19.0047 kV to 19.9453 kV. A comparative analysis with Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Quantum Differential Evolution (QDE) demonstrates that QBPSO achieves superior performance in computational efficiency and voltage stability enhancement. These results highlight the effectiveness of QBPSO as a powerful tool for optimizing OLTC settings, contributing to the reliability and efficiency of power distribution systems.