Modeling of GAO-ANFIS controller based hybrid solar photovoltaic and wind power system with seven-level converter

Abstract

In response to the growing demand for electricity and the depletion of fossil fuel resources, nations are transitioning towards renewable energy systems as viable alternatives for power generation. Wind and solar photovoltaic (SPV) energy systems have emerged as promising, sustainable options. However, conventional multilevel inverters fail to control both wind and SPV energy simultaneously. Therefore, in this study, a hybrid SPV wind power system with a level converter (HPWPS-SLC) was developed using a wind-based permanent magnet synchronous generator and SPV energy grid sources. The HPWPS-SLC leverages the benefits of the genetic algorithm-optimized adaptive neuro-fuzzy inference system controller for efficient energy generation and management. In addition, a pulse width modulation controller with a hybrid asymmetric switching scheme was implemented to reduce the total harmonic distortion (THD). This approach enables high switching frequency while minimizing the switch count, thereby reducing the losses and costs associated with conventional techniques. Simulation results show that the proposed HPWPS-SLC system achieves a power factor of 0.7 and a THD of 25.02 % for grid voltages under fault conditions. Despite the fault conditions, maintaining a THD value of 25.02 % ensures a better grid voltage waveform quality and minimizes distortions for stable operation. Utilizing a 42-cycle signal with a fast Fourier transform of 17 cycles enables finer resolution in harmonic analysis up to the 16th order, thereby enhancing the overall system performance.

Keywords

• Renewable energy system
• Multi-level inverter
• Permanent magnetic synchronous generator
• Genetic algorithm
• Adaptive neuro-fuzzy inference system
• Total harmonic distortion