Estimation of Wind Turbine Generator Model Parameters using Artificial Intelligence Methods

Öz

Modelling (in a broad sense) is an essential tool for research in all areas and represents a scientifically based method for assessing the performance of systems and processes used for making engineering decisions. This applies in particular to the field of management systems, where the foundation is making decisions based on the information received.

The existing and newly designed systems effectively examined by using the mathematical models (analytical and spoofing) which allows identifying some constant parameters that are involved in the differential equations representing the dynamics of the system analyzed. Such systems may come from a broad scientific spectrum, for example from economics and biology from communication and weather forecasting.

The present paper investigates some Artificial Intelligence (AI) methods identifying the parameters of a dynamical system. Two types of methods are compared - 'evolution' and 'particle swarm' intelligence. First, for this purpose, a system simulation model generating data (for the two methods of identification in order to compare afterwards the results) is used. After that, Genetic (GA) and Particle Swarm Optimization (PSO) algorithms are applied to estimate the wind turbine generator model parameters. The results of both methods are compared in terms of their accuracy and performance. The software for the simulation and AI process has been developed using MATLAB™.

Anahtar Kelimeler

• Artificial intelligence,system parameter estimation,genetic algorithm,particle swarm optimization,wind turbine generator system model

Kaynakça

1. [1] Bedwani, W, A., Ismail, O. M. Genetic optimization of variable structure PID Control System, In: ACS/IEEE International Conference on Computer Systems and Applications, 2001, pp. 27–30. [2] Siegfried Heier, "Grid Integration of Wind Energy Conversion Systems," John Wiley&Sons Ltd, 1998, ISBN 0-471-97143-X [3] Kargupta, H., Smith, R. E., System identification with evolving polynomial networks, Proceeding of the 4th International Conference on Genetic Algorithm, University of California, San Diego, USA, 1991, pp. 370-376. [4] Kristinsson, K,, Dumont, G, System identification and control using Genetic Algorithms, Ieee Transactions on Systems, Man and Cybernetics, 1992 22 (5), pp, 1033–1046, [5] Holland J.H., Adaptation in natural and artificial system, Ann Arbor, The University of Michigan Press, 1975. [6] http://iridia.ulb.ac.be/~mdorigo/ACO/ACO.html [7] http://www.engr.iupui.edu/~shi/Coference/psopap4.html [8] http://www.engr.iupui.edu/~eberhart/web/PSObook.html