| Abstract: |
The accelerating global transition toward renewable energy sources has brought smart grid-connected inverters to the forefront of power electronics research. This paper presents a comprehensive review and meta-analysis of the design strategies, control architectures, and operational challenges associated with grid-connected smart inverters deployed in photovoltaic (PV), wind energy, and hybrid renewable energy systems. The review systematically examines literature published between 2010 and 2024, synthesizing findings from over 120 peer-reviewed studies to identify dominant design paradigms, prevailing control methodologies, and emerging technologies. Key aspects investigated include pulse width modulation (PWM) techniques, model predictive control (MPC), droop control, virtual synchronous generator (VSG) concepts, and artificial intelligence-based adaptive control schemes. The meta-analysis reveals a progressive shift from conventional PI-based control toward advanced predictive and learning-based algorithms, driven by the need for improved dynamic response, harmonic distortion reduction, and enhanced grid stability. Power quality compliance with IEEE 1547 and IEC 61727 standards emerges as a persistent design constraint across all reviewed works. The paper further identifies critical research gaps in fault ride-through capability, multi-objective optimization, and real-time digital twin integration for inverter management systems. Findings indicate that hybrid control frameworks combining MPC with machine learning exhibit the highest potential for future smart inverter deployment in high-penetration renewable energy grids. This review provides a structured foundation for researchers, engineers, and policymakers engaged in the design, standardization, and deployment of next-generation inverter technologies. |
