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Stochastic modeling and performance monitoring of wind farm power production
We present a new stochastic approach to describe and remodel the conversion process of a wind farm at a sampling frequency of 1 Hz. The method is trained on data measured on one onshore wind farm for an equivalent time period of 55 days. Three global variables are defined for the wind farm: the 1-Hz wind speed u(t) and 10-min average direction both averaged over all wind turbines, as well as the cumulative 1-Hz power output P(t). When conditioning on various wind direction sectors, the dynamics of the conversion process u(t) → P(t) appear as a fluctuating trajectory around an average IEC-like power curve. Our approach is to consider the wind farm as a dynamical system that can be described as a stochastic drift/diffusion model, where a drift coefficient describes the attraction towards the power curve and a diffusion coefficient quantifies additional turbulent fluctuations. These stochastic coefficients are inserted into a Langevin equation that, once properly adapted to our particular system, models a synthetic signal of power output for any given wind speed/direction signals. When combined with a pre-model for turbulent wind fluctuations, the stochastic approach models the power output of the wind farm at a sampling frequency of 1 Hz using only 10-min average values of wind speed and directions. The stochastic signals generated are compared to the measured signal, and show a good statistical agreement, including a proper reproduction of the intermittent, gusty features measured. In parallel, a second application for performance monitoring is introduced. The drift coefficient can be used as a sensitive measure of the global wind farm performance. When monitoring the wind farm as a whole, the drift coefficient registers some significant deviation from normal operation if one of twelve wind turbines is shut down during less than 4% of the time. Also, intermittent anomalies can be detected more rapidly than when using 10-min averaging methods. Finally, a probabilistic description of the conversion process is proposed and modeled, which can in turn be used to further improve the estimation of the stochastic coefficients.
Stochastic modeling and performance monitoring of wind farm power production
We present a new stochastic approach to describe and remodel the conversion process of a wind farm at a sampling frequency of 1 Hz. The method is trained on data measured on one onshore wind farm for an equivalent time period of 55 days. Three global variables are defined for the wind farm: the 1-Hz wind speed u(t) and 10-min average direction both averaged over all wind turbines, as well as the cumulative 1-Hz power output P(t). When conditioning on various wind direction sectors, the dynamics of the conversion process u(t) → P(t) appear as a fluctuating trajectory around an average IEC-like power curve. Our approach is to consider the wind farm as a dynamical system that can be described as a stochastic drift/diffusion model, where a drift coefficient describes the attraction towards the power curve and a diffusion coefficient quantifies additional turbulent fluctuations. These stochastic coefficients are inserted into a Langevin equation that, once properly adapted to our particular system, models a synthetic signal of power output for any given wind speed/direction signals. When combined with a pre-model for turbulent wind fluctuations, the stochastic approach models the power output of the wind farm at a sampling frequency of 1 Hz using only 10-min average values of wind speed and directions. The stochastic signals generated are compared to the measured signal, and show a good statistical agreement, including a proper reproduction of the intermittent, gusty features measured. In parallel, a second application for performance monitoring is introduced. The drift coefficient can be used as a sensitive measure of the global wind farm performance. When monitoring the wind farm as a whole, the drift coefficient registers some significant deviation from normal operation if one of twelve wind turbines is shut down during less than 4% of the time. Also, intermittent anomalies can be detected more rapidly than when using 10-min averaging methods. Finally, a probabilistic description of the conversion process is proposed and modeled, which can in turn be used to further improve the estimation of the stochastic coefficients.
Stochastic modeling and performance monitoring of wind farm power production
Milan, Patrick (author) / Wächter, Matthias (author) / Peinke, Joachim (author)
2014-05-01
29 pages
Article (Journal)
Electronic Resource
English
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