Abstract
Abstract With the further increase of renewable energy such as wind power and photovoltaic in the power grid, the hydraulic turbine regulating system (HTRS) faces enormous challenges. As simulation modeling is the basis for studying the optimal control of HTRS, it is crucial to establish a high-precision model of HTRS. As the core component of the HTRS, the hydro-turbine contains extremely complex nonlinearity, which model accuracy directly affects the accuracy of the HTRS simulation platform. However, current modeling methods have certain disadvantages such as large modeling errors and making the structure of the HTRS simulation platform more complex. Therefore, this paper proposes a data modification strategy (IGSA-NN) based on the BP neural network (BPNN) and an improved gravity search algorithm (IGSA) and then achieves high-precision modeling of the hydro-turbine combining the BPNN and actual operation data. Finally, the accuracy of the hydro-turbine model is verified by the actual operation data, and the method to build the simulation platform of the HTRS which contains both real and virtual components is given. The research results have a certain guiding significance for hydro-turbine modeling.
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