Enhancing Controller Efficiency in Hybrid Power System Using Interval Type 3 Fuzzy Controller with Bacterial Foraging Optimization Algorithm

Abstract

Microgrids (MGs) are designed with the help of effective power extracted from renewable sources such as rooftop solar panels, photovoltaic cells, batteries, floating PV and solar PV with the grid. In a hybrid microgrid, Interlinking Converter (ILC) is a key component to connect the AC sub-grid and DC sub-grid. DC-DC converters are being used as power converters in between load and source to enforce and increase the PV depending on the voltage output signal. Accordingly, the work focused on a Multi-Input (MI) KY boost converter. This Proposed topology gathered maximum power using multi-input KY boost converters for hybrid energy. This hybrid topology operates mainly delivered power from renewable energy sources solar/wind to dc bus. In the absence of any one source, wind or solar supplies power to the dc bus. Without any renewable energy, sources battery deliver the power to the dc bus. The research proposed the interval type 3 fuzzy controller is used for controlling the load frequency of the multi-area system. Swarm-based hybrid metaheuristic optimizer of the Bacterial Foraging Optimization Algorithm (BFOA) is proposed for optimal tuning and controlling the PI controller parameters. Controlling the reactive power of the hybrid power system model with the aid of a Static Synchronous Compensator (STATCOM). A unique controller is deployed to regulate the AC and DC currents of the STATCOM using two PI controllers. In this paper effectiveness of the hybrid power system is simulated through MATLAB/SIMULINK. The battery current and voltage of this produce 2000 A and 205 V, grid voltage produced in this work is 1.9 × 104 V and the power of the work produce approximately 90 kW. The results show that the interlink converter improves the flexibility of the hybrid microgrid and, in addition, the power quality of the energy supplied in the utility grid is improved. In future, an intelligent control algorithm may be presented to improve the control strategies of the HMG, respectively.