Optimal Control of Renewable Energy Interfaces Using Embedded Ai-Driven Power Electronics

Abstract

ABSTRACT: Renewable energy sources such as solar PV and wind turbines introduce intermittency that challenges power system stability and power quality. This paper develops an AI-driven embedded control strategy integrated into power electronic interfaces, enabling dynamic regulation of active and reactive power output. The system uses predictive neural algorithms embedded within inverter or converter controllers to anticipate fluctuations based on irradiance, wind profiles, and load variations. Real-time control loops provide rapid compensation for voltage dips, harmonics, and unbalanced loads. Experimental results demonstrate that the proposed method enhances renewable hosting capability, ensures grid code compliance, and significantly reduces the response latency of power electronic devices.

KEYWORDS: Renewable integration, Embedded AI, Power electronics control, Predictive algorithms, Grid compliance.