Prediction of Power Output of Wind Turbines Using System Identification Techniques
In this paper, a generalized State Space Model for prediction of wind turbine output power has been developed via System Identification techniques. The techniques involved in this paper are the subspace methods, mainly, Numerical algorithms for Subspace State Space System Identification based on advanced linear algebra, and Prediction Error Method that based on iterative optimization. Predicting the power output of wind turbines in general is very useful in many applications, such as online monitoring of wind turbines, increasing the share of wind power from the wind farms to grids, operator training simulations, control system upgrades and so on. In order to attain sufficient accuracy for the model, many influencing inputs are taken into account, which are pitch angle, rotor speed with several other meteorological inputs as humidity, temperature, air pressure and wind speed have been considered in the system identification prediction; the result has shown that each input is sufficient and affects the model accuracy. Furthermore, the effect of model order on simulation accuracy has been investigated. In addition, a comparative study has been conducted between Prediction Error Method and Numerical algorithms for Subspace State Space System Identification methods in terms of complexity, accuracy, and speed of simulation. Simulations have demonstrated each methods’ ability to predict wind power with some slight differences in accuracy and computational requirements.
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