Utilizing Artificial Intelligence to Improve Solar Inverter Efficiency

In this research, an efficiency optimization analysis of a multilevel inverter system is conducted by comparing traditional PDCPWM and ANN control algorithms. The goal is to improve inverter performance by improving efficiency across various load and input voltages while minimizing Total Harmonic Distortion (THD). A multilevel inverter was designed and tested using PDCPWM control as the baseline technique, with ANN serving as an additional mechanism. Collected simulation data were used to train the ANN for dynamic adjustment of the reference signal used in the PDCPWM technique, enabling optimized switching for improved performance. The ANN-based control was able to demonstrate higher efficiency and THD reduction in varying load and input voltage conditions compared with traditional PDCPWM. The experimental results suggest that utilization of an AI driven controller is a feasible solution for the use cases considered during operation of inverters used in renewable energy systems. This study offers a platform for investigating AI techniques in the control of inverters; further research on state-of-the-art practical applications that demand higher performance (in terms of efficiency and harmonic distortion reduction) may refer to its conclusions.