NDLI: PID neural network decoupling control for doubly fed hydro-generator system

Content Provider	IEEE Xplore Digital Library
Author	Aiwen Guo Jiandong Yang Haiyan Bao
Copyright Year	2008
Description	Author affiliation: State Key Lab. of Water Resources & Hydropower Eng. Sci., Wuhan Univ., Wuhan (Aiwen Guo; Jiandong Yang; Haiyan Bao)
Abstract	Proportional, integral and differential are defined as a neuron respectively, combined with neural network in this paper. PID neural network (PIDNN) is built and the structure of PIDNN is also simple. Using the PID neural network, the strong coupled time-varying system can be decoupled and controlled easily. The doubly fed hydro-generator system is a novel type of hydraulic generation system. Considering the performances of uncertain and nonlinear as well as parameters coupling and time-variation for three parts of water flux, hydro-turbine and generator, the PIDNN control strategy is introduced. By comparison with the conventional PID control, the results of simulation show that hydro-generator system is good robustness against system parameters uncertainly and load disturbance.
Starting Page	6149
Ending Page	6152
File Size	122761
Page Count	4
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4592788
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Artificial neural networks Control systems Generators Training Stability analysis Robustness simulation PID neural network doubly fed hydrogenerator system decoupling control
Content Type	Text
Resource Type	Article

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