Design and Implementation of a High-Efficiency 15-Level Asymmetrical Multilevel Inverter for Renewable Energy Applications

¹ Dept.of EEE, Aditya University, Surampalem
² Dept.of Mining Engineering, Aditya University, Surampalem
³ Dept.of EEE, Aditya University, Surampalem
⁴ Dept.of EEE, G Narayanamma Institute of Technology and Science
⁵ Dept.of Mathematics, Aditya University, Suramplam

^* Corresponding author: veeranjaneyulu.ikkurthi@adityauniversity.in

Abstract

The growing use of photovoltaic (PV) plants for energy generation highlights the need for efficient conversion of DC to AC for powering loads or grid integration. Multilevel inverters (MLIs) are an effective solution as their stepped output voltage approximates a sinusoidal waveform, reducing harmonic distortion. However, increasing the number of voltage levels typically requires additional switches and DC sources, complicating the design and control. This paper proposes a novel 15-level asymmetrical MLI that utilizes only 7 switches and 3 asymmetrical DC voltage sources, significantly simplifying the circuit while achieving high performance. The reduced switch count minimizes switching losses, resulting in improved efficiency. The inverter’s design employs a fundamental switching scheme that reduces complexity and enhances overall system efficiency. With a 97.4% switching efficiency, the proposed topology outperforms conventional multilevel inverters in terms of power conversion efficiency. The nearest level control modulation technique is used to generate pulses at the fundamental frequency, ensuring precise control and further reducing total harmonic distortion (THD). MATLAB/Simulink simulations are used to evaluate THD, with results showing a 25% reduction compared to conventional designs, ensuring compliance with IEEE 519 standards. The proposed inverter topology demonstrates significant advantages for renewable energy systems (RES) applications, offering a cost-effective, high-efficiency solution suitable for grid integration and PV applications. The findings suggest further potential for optimization through intelligent control algorithms and closed-loop systems.