Probabilistic computation of wind farm power generation based on wind turbine dynamic modeling

Herman Bayem (1) , Yannick Phulpin (2) , Philippe Dessante (2) , Julien Bect (2)

1 SUPELEC-Campus Gif
2 E3S - Supélec Sciences des Systèmes

This paper addresses the problem of predicting a wind farm's power generation when no or few statistical data is available. The study is based on a time-series wind speed model and on a simple dynamic model of a DFIG wind turbine including cut-off and cut-in behaviours. The wind turbine is modeled as a stochastic hybrid system with three operation modes. Numerical results, obtained using Monte-Carlo simulations, provide the annual distribution of a wind farm's active power generation. For different numbers of wind turbines, we compare the numerical results obtained using the dynamic model with those obtained considering the wind turbine's steady-state power curve. Simulations show that the wind turbine's dynamics do not need to be considered for analyzing the annual distribution of a wind farm generation.

Mots clés: cut-off behaviour; stochastic hybrid system; wind farm

Domaines: Energie électrique; Applications [stat.AP]

Herman Bayem, Yannick Phulpin, Philippe Dessante, Julien Bect. Probabilistic computation of wind farm power generation based on wind turbine dynamic modeling. 10th International Conference on Probablistic Methods Applied to Power Systems (PMAPS 2008), May 2008, Rincon, Puerto Rico. ⟨hal-00215958v3⟩