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Mathematical Problems in Engineering
Volume 2015, Article ID 382954, 10 pages
Research Article

One-Step-Ahead Predictive Control for Hydroturbine Governor

1School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei 430072, China
2School of Water Resources and Architectural Engineering, Northwest A&F University, Xi’an, Shaanxi 712100, China
3Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada T2N 1N4

Received 24 December 2014; Accepted 23 January 2015

Academic Editor: Yun-Bo Zhao

Copyright © 2015 Zhihuai Xiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The hydroturbine generator regulating system can be considered as one system synthetically integrating water, machine, and electricity. It is a complex and nonlinear system, and its configuration and parameters are time-dependent. A one-step-ahead predictive control based on on-line trained neural networks (NNs) for hydroturbine governor with variation in gate position is described in this paper. The proposed control algorithm consists of a one-step-ahead neuropredictor that tracks the dynamic characteristics of the plant and predicts its output and a neurocontroller to generate the optimal control signal. The weights of two NNs, initially trained off-line, are updated on-line according to the scalar error. The proposed controller can thus track operating conditions in real-time and produce the optimal control signal over the wide operating range. Only the inputs and outputs of the generator are measured and there is no need to determine the other states of the generator. Simulations have been performed with varying operating conditions and different disturbances to compare the performance of the proposed controller with that of a conventional PID controller and validate the feasibility of the proposed approach.