Advanced Synchronverter Control Algorithms for Microgrid Stability: A Systematic Literature Review

Abstract

This study aims to comprehensively analyze the development, classification, and performance of advanced control algorithms for synchronverters in microgrid applications. The approach used is a systematic literature review (SLR) with the PRISMA method to ensure a systematic and transparent literature selection process. A total of 83 initial articles were obtained from the Scopus database, which then underwent a screening process to yield 25 articles that were analyzed in depth. The results show that synchronverter control algorithms have evolved from conventional methods toward more adaptive, optimal, and intelligence-based approaches. The classification of control methods includes conventional control, PLL-less control, adaptive control, optimal control, and model-based analysis control, with adaptive and model-based approaches being dominant. Furthermore, it was found that each method has different advantages and limitations in improving frequency stability, voltage stability, and system dynamic response. Specifically, fuzzy logic-based adaptive control reduces frequency deviation by up to 40–60% compared to conventional droop control, while LQR-based optimal control improves settling time by approximately 25–35% under nominal conditions but degrades under nonlinearities. PLL-less methods enhance synchronization speed by 30–50% but show limited transient performance during mode transitions. Model-based methods offer high analytical accuracy but lack direct real-time control applicability. This study also identifies research gaps, particularly in the integration of multi-function control, the limited application of predictive methods, and the lack of experimental validation. In conclusion, this research provides a systematic mapping of synchronverter control algorithms and highlights the need for developing more adaptive, robust, and integrated control methods to support the stability of renewable energy-based microgrids.