Stochastic modeling and performance monitoring of wind farm power production

TL;DR

This paper introduces a stochastic modeling approach for wind farm power output at 1Hz, capturing fluctuations and turbulence, and proposes a performance monitoring method based on drift coefficients for early anomaly detection.

Contribution

The paper develops a Langevin equation-based stochastic model for wind farm power output using 1Hz data, and introduces a drift coefficient for sensitive performance monitoring.

Findings

Stochastic model accurately reproduces gusty power fluctuations.

Drift coefficient detects turbine shutdowns within 4% of operation time.

Model provides a probabilistic description of wind farm conversion process.

Abstract

We present a new stochastic approach to describe and remodel the conversion process of a wind farm at a sampling frequency of 1Hz. When conditioning on various wind direction sectors, the dynamics of the conversion process appear as a fluctuating trajectory around an average IEC-like power curve, see section II. 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, see section III. 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 1Hz using only ten-minute 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 in section IV. 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 ten-minute averaging methods. Finally, a probabilistic description of the conversion process is proposed and modeled in appendix A, that can in turn be used to further improve the estimation of the stochastic coefficients.