OPTIMIZING THE PERFORMANCE OF STATE ESTIMATION IN POWER SYSTEMS USING A NOVEL SWARM-INTELLIGENT APPROACH

For power systems to operate reliably and effectively, state estimate is essential. For monitoring and control applications, it offers real-time estimates of the system's state indicators, including voltage levels and orientations. In this paper, a novel catagonfly optimization (CFO) technique that aims to improve state estimation effectiveness in electrical systems is presented. The suggested strategy combines optimization techniques from cat swarm and dragonfly. The suggested strategy is evaluated using the IEEE-118 test environment under various simulated scenarios, and the findings are contrasted with those of other methods already in use. The optimization results and statistical error assessment demonstrate the suggested CFO technique's efficiency to the alternatives. The results of this study also have the potential to improve the incorporation of sustainable energy sources, microgrids, and additional emerging innovations into existing power grids.