Design of Quazi-Z-Source Inverter with SSE-RNN Controller for GridConnected Hybrid Renewable Energy Sources

  • Ratheesh. S


Due to the load variations and different impedance, the unbalanced voltages and harmonics usually
occur. In this paper, we proposed a quasi-Z-source inverter (qZSI) with SSE-RNN controller for grid
connected renewable energy sources (RES). The SSE-RNN is the combination of the salp swarm
algorithm (SSA) with Elman-recurrent neural network (E-RNN). Here, hybrid RES such as PV and wind
are used, and buck-boost converter is employed. To get exact power, SSE-RNN controller is used, which
is the combination of the salp swarm algorithm and Elman-recurrent neural network for tuning the gate
pulse of the inverter. E-RNN operation will be enhanced by SSA to make less harmonic controller
voltage. When compared with other metaheuristic algorithms, the SSA gives better results using
convergence rate and complexity issues. Thus we get the error-free output voltage without harmonic
distortions. The performance outcome is inverter voltage and current, grid current, DC link voltage,
grid voltage, and the analysis of THD, which is taken in the MATLAB/Simulink platform. The
performance of THD and DC link voltage is compared with other existing techniques.