Estimation of Wind Turbine Generator Model Parameters using Artificial Intelligence Methods

Authors : Rositsa KAZAKOVA, Stefka NEDELCHEVA, Rumen POPOV

Pages : 51-57

View : 11 | Download : 3

Publication Date : 2016-09-30

Article Type : Research Paper

Abstract :Modelling insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(for the two methods of identification in order to compare afterwards the results); is used. After that, Genetic insert ignore into journalissuearticles values(GA); and Particle Swarm Optimization insert ignore into journalissuearticles values(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™.
Keywords : Artificial intelligence, system parameter estimation, genetic algorithm, particle swarm optimization, wind turbine generator system model