Intelligent Embedded Control Framework for Modern Distribution Power Systems

Abstract

ABSTRACT: Modern distribution networks are rapidly evolving due to the integration of renewable energy, electric vehicles, and active consumer participation. These developments introduce increased volatility, uncertainty, and the need for faster decision-making at the grid edge. This paper proposes an intelligent embedded control framework capable of decentralized decision support for voltage regulation, load balancing, and disturbance mitigation. The architecture leverages ARM/DSP-based controllers, local AI inference modules, and multiprotocol communication layers to ensure that field devices operate autonomously even under weak connectivity conditions. Additionally, the system incorporates adaptive learning algorithms that fine-tune control actions based on historical patterns, weather-linked renewable fluctuations, and realtime measurement deviations. Simulation results show that the framework significantly improves grid stability, enhances dynamic response during contingencies, and optimizes reactive power flow, thereby reducing overall system losses and enhancing resiliency.

KEYWORDS: Embedded control, Distribution automation, AI-assisted decision-making, Decentralized architecture, Grid stability