Power quality control of an autonomous wind-diesel power system based on hybrid intelligent controller

Wind power generation is gaining popularity as the power industry in the world is moving toward more liberalized trade of energy along with public concerns of more environmentally friendly mode of electricity generation. The weakness of wind power generation is its dependence on nature-the power output varies in quite a wide range due to the change of wind speed, which is difficult to model and predict. The excess fluctuation of power output and voltages can influence negatively the quality of electricity in the distribution system connected to the wind power generation plant. In this paper, the authors propose an intelligent adaptive system to control the output of a wind power generation plant to maintain the quality of electricity in the distribution system. The target wind generator is a cost-effective induction generator, while the plant is equipped with a small capacity energy storage based on conventional batteries, heater load for co-generation and braking, and a voltage smoothing device such as a static Var compensator (SVC). Fuzzy logic controller provides a flexible controller covering a wide range of energy/voltage compensation. A neural network inverse model is designed to provide compensating control amount for a system. The system can be optimized to cope with the fluctuating market-based electricity price conditions to lower the cost of electricity consumption or to maximize the power sales opportunities from the wind generation plant.

[1]  R. Chedid,et al.  Probabilistic production costing of diesel-wind energy conversion systems , 2000 .

[2]  G. W. Ng application of Neural Networks to Adaptive Control of Nonlinear Systems , 1997 .

[3]  Ray Hunter,et al.  Wind-Diesel Systems , 1994 .

[4]  Un-Chul Moon,et al.  A self-organizing fuzzy logic controller for dynamic systems using a fuzzy auto-regressive moving average (FARMA) model , 1995, IEEE Trans. Fuzzy Syst..

[5]  M. Kawato,et al.  A hierarchical neural-network model for control and learning of voluntary movement , 2004, Biological Cybernetics.

[6]  Nicholas Jenkins,et al.  Distributed load control of autonomous renewable energy systems , 2001 .

[7]  Brian T. Gold,et al.  Real-time adaptive control using neural generalized predictive control , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[8]  R. Chedid,et al.  Adaptive fuzzy control for wind-diesel weak power systems , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[9]  S. C. Tripathy,et al.  Dynamics and stability of wind and diesel turbine generators with superconducting magnetic energy storage unit on an isolated power system , 1991 .

[10]  Per Printz Madsen Neural network for optimization of existing control systems , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[11]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[12]  R. Chedid,et al.  Adaptive fuzzy control for wind-diesel weak power systems , 2000 .

[13]  C.-L.M. Harnold,et al.  Free-model based adaptive inverse neurocontroller for dynamic systems , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[14]  Kwang Y. Lee,et al.  An optimal tracking neuro-controller for nonlinear dynamic systems , 1996, IEEE Trans. Neural Networks.

[15]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[16]  Un-Chul Moon,et al.  A self-organizing power system stabilizer using fuzzy auto-regressive moving average (FARMA) model , 1996 .

[17]  Jun Nakanishi,et al.  Feedback error learning and nonlinear adaptive control , 2004, Neural Networks.

[18]  J. Yen,et al.  Fuzzy Logic: Intelligence, Control, and Information , 1998 .