Optimal Design of AI Controller for Fuel Cell and Renewable Energy Integrated Parallel Inverters Using Lyre Bird Algorithm

Power electronic devices are used to integrate clean energy systems (CES) like wind, solar, and other energy sources into the grid to meet the load demand. The parallel operated-inverters (POI) can improve the power capacity and reliability of power electronic systems. When employing the standard droop control approach during a load change with varying feeder impedances, the primary disadvantage is the uneven distribution of power among the inverters. Furthermore, circulating currents (CC) flow between these POI, resulting in distortions in the current waveform and common-mode voltage (CMV) and degrading the system performance. A novel approach is used in this work to address multiple objectives, such as (a) uniform power sharing between the two VSIs, (b) efficient CC and CMV minimization, (c) maintaining constant DC link voltage (CDCLV) under various solar power with constant temperature, and (d) reduction of THD. The improved droop control (IDC) approach is featured in this work along with an optimized artificial neural network controller (ANNC) with the lyrebird optimization algorithm (LOA). The performance of the suggested configuration is executed in MATLAB/Simulink under balanced and unbalanced load conditions. Additionally, a comparative analysis is conducted between controllers such as the proportional integral controller (PIC), fuzzy logic controller (FLC), and sliding mode controller (SMC), to demonstrate the effectiveness of the proposed controller.