Affiliation:
1. School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
2. Key Laboratory of Photothermal and Wind Power Generation in Inner Mongolia, Baotou 014010, China
Abstract
This study addresses speed sensor aging and electrical parameter variations caused by prolonged operation and environmental factors in flywheel energy storage systems (FESSs). A model reference adaptive system (MRAS) flywheel speed observer with parameter identification capabilities is proposed to replace traditional speed sensors. The proposed method uses reference and adjustable models to identify the stator resistance and permanent magnet flux (PM Flux) to mitigate the adverse effects of electrical parameter changes on control performance. The Tent chaotic mapping-improved Sparrow Search Algorithm (SSA) optimizes the Proportional-Integral (PI) controller parameters for the dual closed-loop and MRAS speed adaptation laws of the flywheel motor. Moreover, a self-switching parameter identification (SSPI) scheme, which constructs a cost function based on the current, parameter identification, and speed errors, is proposed to prevent inaccuracies in parameter identification. The MRAS observer selects the appropriate PI adaptive mechanism based on the error values, thereby enhancing identification accuracy. Simulink simulations show significant improvements in the rapidity and accuracy of the Tent-SSA optimized MRAS flywheel speed observer, enhancing the stability and robustness of the flywheel rotor. Experimental validation on a constructed FESS platform confirms the feasibility of this method.
Funder
Major Science and Technology Project in the Inner Mongolia Autonomous Region
solar heat pump heating unit and its intelligent control, China
Research and Application of Key Technologies for Cooperative Optimal Operation of Distributed Energy Systems, China
Higher Education Scientific Research Project Inner Mongolia Autonomous Region, China
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