2026-09-19T09:09:55Zhttps://digital.lib.washington.edu/server/oai/request

oai:digital.lib.washington.edu:1773/511572026-02-16T02:20:19Zcom_1773_4888col_1773_49166

Logenthiran, Thillainathan Sheng, Jie Pasumarthi, Mohana Venkata Shanmukha Srikar 2024-02-12T23:40:29Z 2024-02-12T23:40:29Z 2024-02-12 2023 PASUMARTHI_washington_0250O_26455.pdf http://hdl.handle.net/1773/51157 Thesis (Master's)--University of Washington, 2023 The research work in this thesis focuses on the application of Model Predictive Control (MPC) to Dual Battery Energy Storage System (dual BESS) so that the reference power from the actual wind farm power can be tracked with satisfactory performance. The control strategy considers certain practical constraints including the power delivered/extracted from each battery, as well as the state of charge on each battery. The operation of the two batteries is categorized into two modes. In Mode 1, the first battery charges and the second battery discharges; and Mode 2 deals with the opposite, i.e., the second battery charges and the first battery discharges. The power from the battery is treated as the control signal which is an optimized computation results given real-time by MPC.The significance of this work contains mainly two aspects. First, the two-mode operation of batteries removes the problem of overloading battery. Second, the actual wind power data is used to verify the dual battery integrated wind farm performance using MPC, thereby tracking the reference power by reducing the battery switching times and extending the life of the battery. application/pdf en_US none Engineering Electrical engineering Electrical and computer engineering Application of model predictive control on wind farm incorporated with dual battery energy storage system Thesis Open Access open.access