A comparative study of HNN and Hybrid HNN-PSO techniques in the optimization of distributed generation (DG) power systems

One of the major advances in recent years is the integration of multiple alternative energy sources, e.g., wind turbine generators, photovoltaic cell panels and fuel-fired generators, equipped with storage batteries to form a distributed generation (DG) power system. Nevertheless, cost effectiveness, reliability and pollutant emissions are still major issues with DG systems. The optimization goal was to minimize cost, maximize reliability and minimize emissions (multi-objective) subject to the constraints (power balance and design constraints). This paper discusses the optimization that was performed using Hopfield Neural Networks (HNN), and the Hybrid Hopfield Neural Network-PSO (HNN-PSO) algorithms.