Identification of the optimal control characteristics of a small hydropower plant using artificial neural networks and the support vector machines method

ABSTRACT This study investigates a small hydropower plant at varying turbine speeds. The identification of the optimal turbine rotational speed as a function of a flow rate is usually based on the efficiency function of two variables. The existing procedures require a large number of operating points, which prolong the necessary field measurements. In this paper, a new identification procedure based either on the use of a traditional neural network (multi-layer perceptron, radial basis function), or on a support vector machines method using dedicated assessment factors, is proposed. The procedure was tested and verified in an experimental 150 kW small hydropower plant that contained two propeller turbines. The tests highlighted the advantages of the support vector machines network over traditional neural networks owing to the precise approximation under a limited amount of training data. It is shown that even 15 measurement points provide relatively accurate results.