Research on Energy - Saving Operation Strategy of Substations Based on Bayesian Networks and Particle Swarm Optimization

With the continuous expansion of power systems, the energy consumption issue of substations has gradually drawn extensive attention. To enhance energy utilization efficiency and reduce operational costs, this paper innovatively proposes an energy-saving operation strategy for substations by integrating Bayesian networks (BNs) and particle swarm optimization (PSO) algorithms. Specifically, BNs are employed to accurately model and predict substation energy consumption, enabling an in-depth analysis of multiple factors influencing energy consumption. Subsequently, the PSO algorithm is utilized to optimize substation operation strategies, thereby achieving effective reduction of energy consumption. The validation results from practical cases strongly demonstrate that the proposed strategy has achieved remarkable effects in reducing substation energy consumption, with outstanding energy-saving performance. Experimental data indicate that compared with traditional methods, this strategy outperforms in both energy-saving performance and operational stability, opening up a new pathway for the intelligent energy-saving operation of substations.