Soft Computing Based Optimum Parameter Design of PID Controller in Rotor Speed Control of Wind Turbines
- 1.3k Downloads
Sensitivity and robustness is the primary issue while designing the controller for large non-linear systems such as offshore wind turbines. The main goal of this study is a novel soft computing based approach in controlling the rotor speed of wind turbine. The performance objectives for controller design is to keep the error between the controlled output (speed of rotor) and the target rotor speed, as small as possible. The wind turbine involves controlling both the aerodynamics and hydrodynamics response together, therefore in this paper an attempt is being made using soft computing approach. The commonly used proportional – integral – derivative controller (PID controller) for wind turbines employs Ziegler and Nichols (ZN) approach which leads to excessive amplitude in some situations. In this work, the parameters of PID controller are obtained using the conventional method that is ZN along with the artificial intelligence (AI) technique. Two types of AI (i) bacteria foraging optimization algorithm (BFOA) and (ii) particle swarm optimization (PSO) coupled with ZN controller are studied. The controller performance indices are taken as integral square error, steady state error, controller gain, maximum overshoot and settling time. In this work, the idea of model generation and optimization is explored for PID controller. The planned controller strategy would be able to carry out high quality performance which reveal that the proposed controller system can significantly reduce the errors and settling time.
KeywordsWind turbines optimization proportional–integral–derivative controller bacteria foraging optimization algorithm particle swarm optimization
Unable to display preview. Download preview PDF.
- 3.Kim, D.H., Hoon, J.C.: Biologically inspired intelligent PID controller tuning for AVR systems. Int. J. Control Automation Sys. 4(5), 624–636 (2006)Google Scholar
- 4.Kim, D.H., Abraham, A.: A hybrid genetic algorithm and bacterial foraging approach for global optimization and robust tuning of PID controller with disturbance rejection. Studies in Computational Intelligence 75, 171–199 (2007)Google Scholar
- 6.Ying, S., Zengqiang, C., Zhuzhi, Y.: Adaptive constrained predictive PID controller via PSO. In: Proceedings of the 26th Chinese Control Conference, Zhangjiajie, Hunan, China, pp. 729–733 (2007)Google Scholar
- 8.Aland, W.: Modern control design for flexible wind turbines.Technical Report NREL/TP-500-35816, NREL (2004)Google Scholar
- 9.Wright, A.D., Fingersh, L.J.: Advanced control design for wind Turbines: Control design, implementation and initial states. Technical report NREL/CP-500-36118, NREL (2008)Google Scholar
- 10.Hand, M.M.: Variable-speed wind turbine controller systematic design methodology: A comparison of nonlinear and linear model based design. Technical Report NREL/TP-500-25540, NREL (1999)Google Scholar
- 11.Leabi, S.K.: NN Self-Tuning pitch angle controller of wind power generation, M.S Thesis, University Of Technology,Baghdad, Iraq (2005)Google Scholar
- 12.Ogata, K.: Modern Control Systems Engineering. Prentice Hall, India (2010)Google Scholar