Enhancement of particle swarm optimization algorithm based modelling of lossless power transformer for EV charging station

Abstract

Contactless power transmission is recently applied in various real-time applications such as electric vehicle charging, space travel and other distribution systems. The transformer winding has magnetic coupling with each phase; It analyzes the primary and secondary winding for the core structure of the transformer. The overall electrical vitality utilization is growing rapidly to meet demand; It is important to start more power plants. However, petroleum uses are limited and it is only a matter of time before they run out, unnecessary to specify their commitment in an unnatural climate change, many countries are currently looking at their public vitality approach. Looking forward to figuring out other options. Sustainable energy sources are among the vitality sources that provide another approach to dodge this fossil vitality cutoff time. It is also expected to assume an important function as a clean energy source later on vitality requests. Among this various sustainable power sources, PV power grid framework is the most commonly used. One of the most important applications is the grid connected PV infrastructure on electric vehicle applications such as motor driving systems, charging stations. Here a contactless power transformation approach for wireless transformation in the application of electrical charging stations to electric vehicles was demonstrated. Conventional loosely coupled transformers with mixed winding and electromagnetic shielding for a contactless power transmission method used various conventional genetic algorithms and particle swarm optimization algorithms to minimize power losses. This prolongs transmission time and iv reduces efficiency. In the proposed methodology, the optimal model of the contactless power transformer approach uses an improved particle swarm optimization algorithm to reduce power losses in grid-connected PV modules. The maximum power of the solar panel is tracked using the MPPT algorithm and fed with a switching controller to reduce overlap. Here in IPSO, the frequency d