Abstract
This paper reports the results of different fuzzy-based approaches applied to the fitting of a hydraulic turbine efficiency curve. This kind of curve is granted by the turbine manufacturer as a three-dimensional dataset that needs to be properly fitted in order to provide the turbine efficiency for any values of net head and water discharge in the relevant space and, therefore, guarantee an as realistic as possible representation of the hydroelectric power plant’s machines. The clustering algorithm Fuzzy C-Means and the ANFIS and Extreme Learning ANFIS architectures were widely tested and compared to the conventional polynomial adjustment. Since the studied curve is nonlinear, researches involving any kind of nonlinear curve fitting can benefit from this work’s information.
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