Wind ramp event prediction with parallelized Gradient Boosted Regression Trees
This work addresses the problem of ensuring reliability in power systems with high wind energy penetration, but it appears incremental as it applies an existing method to a specific domain.
The paper tackled wind ramp event prediction for power system stability by proposing a classification approach using parallelized Gradient Boosted Regression Trees, achieving superior classification accuracy compared to benchmark techniques.
Accurate prediction of wind ramp events is critical for ensuring the reliability and stability of the power systems with high penetration of wind energy. This paper proposes a classification based approach for estimating the future class of wind ramp event based on certain thresholds. A parallelized gradient boosted regression tree based technique has been proposed to accurately classify the normal as well as rare extreme wind power ramp events. The model has been validated using wind power data obtained from the National Renewable Energy Laboratory database. Performance comparison with several benchmark techniques indicates the superiority of the proposed technique in terms of superior classification accuracy.