Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Fuzzy Systems and Control (JFSC)
Arunava Chatterjee
Raghunathpur Government Polytechnic
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Hybrid Predictive Fuzzy-AI Energy Management with SOC-Constrained Optimization for Wind-Driven Microgrids Arunava Chatterjee
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.398

Abstract

Small-scale wind energy systems are increasingly being installed in distributed renewable systems and microgrids. These systems display significant power variability due to the uncertain nature of wind speed. Such fluctuations often lead to DC-link voltage deviations and irregular battery charging cycles. Thus, they can reduce overall system reliability and storage lifespan. This paper proposes a hybrid predictive Fuzzy-AI supervisory control strategy for energy management in a wind-battery microgrid. The supervisory layer integrates short-term wind power forecasting with fuzzy logic-based battery scheduling while enforcing state-of-charge (SOC) constraints. A multi-objective formulation is adopted to regulate DC-link voltage and simultaneously minimize battery current stress. The proposed controller generates adaptive battery current references through a rule-based inference mechanism with predictive information. Comparative results with conventional PI control and AI-only scheduling demonstrate improved voltage stability, reduced RMS battery current, and better SOC control. Experimental observations obtained from a small-scale wind generation setup further support the effectiveness of the proposed approach.
Co-Authors