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<p>This study proposes an enhanced particle swarm optimization algorithm designed to overcome the limitations of the traditional particle swarm optimization (PSO) in reactive power optimization, including premature convergence and insufficient search capability. The proposed enhancements enhance global exploration by integrating a competition mechanism with adaptive inertial weights, while optimizing particle distribution through hierarchical congestion and perturbation strategies to improve accuracy. A multi-objective optimization model is established to simultaneously minimize network losses and operational costs. Benchmark function verification, IEEE 14-node case studies, and IEEE 30-node and IEEE 33-node studies have demonstrated the superior performance of the hierarchical mechanism multi-objective particle swarm optimization (LMMOPSO) algorithm: it reduces power losses, accelerates convergence compared to standard PSO, and optimizes voltage distribution and power flow to maintain them within reasonable unit ranges. The algorithm’s robustness in reactive power optimal dispatch confirms its practical applicability to power system optimization.</p>

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Reactive power optimization of power systems based on an improved particle swarm algorithm

  • 期刊论文

  • 发表日期：

    2026年2月1日

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  • Fang Li,
  • Boqun Li,
  • Yanqi Zhao

摘要

This study proposes an enhanced particle swarm optimization algorithm designed to overcome the limitations of the traditional particle swarm optimization (PSO) in reactive power optimization, including premature convergence and insufficient search capability. The proposed enhancements enhance global exploration by integrating a competition mechanism with adaptive inertial weights, while optimizing particle distribution through hierarchical congestion and perturbation strategies to improve accuracy. A multi-objective optimization model is established to simultaneously minimize network losses and operational costs. Benchmark function verification, IEEE 14-node case studies, and IEEE 30-node and IEEE 33-node studies have demonstrated the superior performance of the hierarchical mechanism multi-objective particle swarm optimization (LMMOPSO) algorithm: it reduces power losses, accelerates convergence compared to standard PSO, and optimizes voltage distribution and power flow to maintain them within reasonable unit ranges. The algorithm’s robustness in reactive power optimal dispatch confirms its practical applicability to power system optimization.