Particle Swarm Optimization Combined with Continuation Power Flow Method

In this paper, we present an implementation of Particle Swarm Optimization combined with Continuation Power Flow method for optimizing transformer tap settings. The algorithm utilizes PSO's population-based search mechanism to handle multiple optimization variables simultaneously, while the Continuation Power Flow method ensures stable tracking of power system operating points during parameter adjustments. This multi-variable optimization approach significantly enhances system stability by coordinating various control parameters. Additionally, we can integrate alternative optimization algorithms to further improve system performance. For instance, genetic algorithms with chromosome encoding for tap configurations or simulated annealing with temperature-controlled probability acceptance functions can be implemented to discover superior transformer tap arrangements. These optimization techniques contribute to increased system efficiency and reduced energy losses. Furthermore, intelligent control technologies can be incorporated into the system architecture using fuzzy logic controllers or neural network-based adaptive systems to further optimize operational performance. In summary, the combined Particle Swarm Optimization and Continuation Power Flow method, when complemented with other optimization strategies, provides a robust framework for enhancing system stability while improving overall performance and efficiency metrics.