Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters

Oluleke Babayomi, Zhenbin Zhang, Yu Li and Ralph Kennel
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Oluleke Babayomi: School of Electrical Engineering, Shandong University, Jinan 250061, China
Zhenbin Zhang: School of Electrical Engineering, Shandong University, Jinan 250061, China
Yu Li: School of Electrical Engineering, Shandong University, Jinan 250061, China
Ralph Kennel: Institute for Electrical Drive Systems and Power Electronics, Technische Universitaet Muenchen, 80333 Munich, Germany

Sustainability, 2021, vol. 13, issue 13, 1-17

Abstract: Model predictive control (MPC) is a flexible and multivariable control technique with better dynamic performance than linear control. However, MPC is sensitive to parametric mismatches that reduce its control capabilities. In this paper, we present a new method of improving the robustness of MPC to filter parameter variations/mismatches by easily implementable parameter estimation. Furthermore, we extend the proposed technique for wider operating conditions by novel neuro-fuzzy estimation. The results, which are demonstrated by both simulations and real-time hardware-in-the-loop tests, show a steady-state parameter estimation accuracy of 95%, and at least 20% improvement in total harmonic distortion (THD) than conventional non-adaptive MPC under parameter mismatches up to 50% of the nominal values.

Keywords: AC microgrid; model predictive control; LC-filter; grid-forming converter; parameter mismatch; neuro-fuzzy parameter estimation; distributed energy resources (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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