Hybrid Predictive Fuzzy-AI Energy Management with SOC-Constrained Optimization for Wind-Driven Microgrids
DOI:
https://doi.org/10.59247/jfsc.v4i1.398Keywords:
Artificial Intelligence, Cloud Computing, Internet of Things, State of Charge optimization, Wind Energy ConversionAbstract
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.
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