Data‐Driven Harmonic Measurement, Reliability Enhancement, and Adaptive Power Flow Optimization for Renewable Energy Integrated Smart Microgrids

Abstract

ABSTRACT The increasing penetration of renewable energy sources in smart microgrids introduces significant challenges related to power quality degradation, harmonic distortion, operational uncertainty, and system reliability. Conventional centralized control approaches often suffer from communication bottlenecks, limited scalability, and reduced adaptability under dynamic operating conditions. This study presents a data‐driven intelligent framework for harmonic measurement, reliability enhancement, and adaptive power flow optimization in renewable energy‐integrated smart microgrids. The proposed framework combines decentralized multi‐agent control, reinforcement learning‐based energy management, Long Short‐Term Memory (LSTM) networks for harmonic prediction, and adaptive filtering for real‐time harmonic mitigation. Graph‐based interaction modeling is further incorporated to improve coordination among distributed energy resources, storage systems, and loads. The framework enables continuous monitoring of power quality indicators while supporting reliable and resilient microgrid operation under varying renewable generation and load conditions. Performance evaluation is conducted using renewable energy operational datasets and MATLAB/Simulink‐based validation. Results demonstrate significant improvements in renewable energy utilization, reduction of harmonic distortion, enhanced energy balancing, faster dynamic response, and improved system reliability compared with conventional control methods. The proposed approach provides an effective reliability‐oriented solution for intelligent monitoring, adaptive control, and sustainable operation of next‐generation renewable energy microgrids.