PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 10, 2026IET Energy Systems Integration0 citationsOpen Access

An Intelligent Robust Control Framework for Stability Enhancement in Renewable Energy Powered Hybrid AC/DC Microgrids With Integrated Hydrogen Energy Systems

View Full Paper
MIMd Saiful IslamIBIsrat Jahan BushraTRTushar Kanti Roy

Key Points

  • This research aims to enhance the stability of hybrid AC/DC microgrids integrating renewable energy sources and hydrogen systems.
  • Developed a robust backstepping nonsingular fast terminal integral sliding mode controller.
  • Integrated a virtual capacitor and fractional-power reaching law to emulate synthetic inertia.
  • Utilized an adaptive neuro-fuzzy inference system for real-time gain tuning.
  • Created a MATLAB/Simulink model including photovoltaic, wind, battery, electrolyser, and fuel cell components.
  • Evaluated controller performance against two benchmark controllers in case studies.
  • Reduced voltage overshoot by 75%–100% under severe disturbances.
  • Decreased rise time by 58%–80%, ensuring faster response.
  • Achieved zero steady-state error and converter efficiency of 95.78%.
  • Eliminated mean absolute and mean squared errors, demonstrating strong performance.
  • Significantly improved DC-bus voltage regulation and microgrid reliability.

Abstract

ABSTRACT Hybrid AC/DC microgrids that integrate photovoltaic, wind, battery and hydrogen energy systems are prone to DC‐bus voltage fluctuations because of their low inertia and converter‐based operation. This paper proposes a robust backstepping nonsingular fast terminal integral sliding mode controller that incorporates a virtual capacitor and a fractional‐power reaching law to emulate synthetic inertia and improve transient damping. The controller coordinates energy exchange among distributed generation units and ensures precise DC‐bus voltage regulation while managing bidirectional power transfer between AC and DC subgrids. An adaptive neuro‐fuzzy inference system automatically tunes the controller gains in real time, and system stability is rigorously established through control Lyapunov functions. A detailed MATLAB/Simulink model, comprising PV, PMSG‐based wind turbine, battery storage, electrolyser and PEM fuel cell, implements ANN‐based MPPT to maximise renewable energy harvesting. The BNFTISMC is evaluated in three case studies against two benchmark controllers: the enhanced integral terminal SMC and the enhanced nonsingular terminal SMC. Under severe disturbances and varying load conditions, the proposed controller cuts overshoot by 75%–100%, reduces rise time by 58%–80% and completely eliminates mean absolute and mean squared errors. Processor‐in‐the‐loop testing confirms zero steady‐state error, whereas converter efficiency reaches 95.78% compared with 69.21% for the reference designs, demonstrating improved DC‐bus voltage regulation, enhanced microgrid reliability and efficient real‐time operation.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Islam et al. (2026) studied this question.

synapsesocial.com/papers/69af94fa70916d39fea4c09bhttps://doi.org/10.1049/esi2.70035
Ask AI
Helpful
Bookmark
Share
View Full Paper