Adaptive intelligent hydro turbine speed identification with water and random load disturbances

In this paper, the hydro power plant model (with penstock-wall elasticity and compressible water column effect) is simulated at random load disturbance variation with output as turbine speed for random gate position as input. The multilayer perceptron neural network (i.e. NNARX) and fused neural network and fuzzy inference system (i.e. ANFIS) for identification of turbine speed as output variable are reported. Emphasis is put on obtaining a generalized model, using (i) NNARX model and (ii) ANFIS model with membership functions defined by subtractive clustering for plant model representation under different values of water time constant. The comparative performance study between the two approaches is also addressed. In the end of the paper, an application of adaptive noise cancellation based on ANFIS model to identify the turbine speed dynamics is also discussed.